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Basics of Image Analysis
Chapter · January 2015
DOI: 10.1007/978-1-4939-2836-1_2
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Chapter 2
Basics of Image Analysis
Fernando Mendoza and Renfu Lu
2.1 Introduction
Image analysis is used as a fundamental tool for recognizing, differentiating, and
quantifying diverse types of images, including grayscale and color images, multi-
spectral images for a few discrete spectral channels or wavebands (normally less
than 10), and hyperspectral images with a sequence of contiguous wavebands
covering a specific spectral region (e.g., visible and near-infrared). Earlier works
on image analysis were primarily confined to the computer science community, and
they mainly dealt with simple images for such applications as defect detection,
segmentation and classification. Nowadays, image analysis is becoming increas-
ingly important and widespread because it can be done more conveniently, rapidly
and cost effectively (Prats-Montalbán et al. 2011). Image analysis relies heavily on
machine vision technology (Aguilera and Stanley 1999). The explosive growth in
both hardware platforms and software frameworks has led to significant advances
in the analysis of digital images.
Image analysis has been applied to many different fields of science and
technology. For example, it has been used to assess or quantify the external
characteristics (i.e., color, size, shape and surface texture) and internal structures
(architecture and/or connectivity of the material constituents) of food products.
F. Mendoza
U.S. Department of Agriculture, Agricultural Research Service, Sugarbeet and Bean
Research Unit, 524 S. Shaw Lane, Room 105A, East Lansing, MI 48824, USA
Department of Biosystems and Agricultural Engineering, Michigan State University,
East Lansing, MI, USA
e-mail: fmendoza@msu.edu
R. Lu (*)
U.S. Department of Agriculture, Agricultural Research Service, East Lansing, MI, USA
e-mail: renfu.lu@ars.usda.gov
© Springer Science+Business Media New York 2015
B. Park, R. Lu (eds.), Hyperspectral Imaging Technology in Food and Agriculture,
Food Engineering Series, DOI 10.1007/978-1-4939-2836-1_2
9

Commercial machine vision units are readily available to meet the requirements
of automatic inspection for the food processing and packaging industries.
As consumers are demanding better quality and safer food products, there is an
increasing need for rapid and non-destructive quality evaluation of foods. In recent
years, new imaging-based inspection techniques, such as multispectral and
hyperspectral imaging, have been developed for quality assessment of a variety
of foods, which have overcome some of the drawbacks of traditional human and
instrumental inspection techniques (Du and Sun 2007). These methods, which are
based on the automatic detection of various image features, correlate well with
quality attributes of foods that are related to the sensorial, chemical, and physical
properties (Valous et al. 2009a).
It is important to note that image analysis is part of a wider field known as image
processing, where the main underlying idea is to improve the visual quality of an
image and/or to extract useful information or features. The analysis is based on
different image properties such as color, gloss, morphology of the objects,
and texture. Image processing actions can be grouped into three sub-areas
(Prats-Montalbán et al. 2011):
(a) Image compression, which reduces the memory requirements by removing the
redundancy present in the image, that is, the image information which is not
perceptible to the human eye.
(b) Image preprocessing, which consists of improving the visual quality of the
image by reducing noise, pixel calibration and standardization, enhancing the
edge detection, and making the image analysis step more reliable based on
objective and well established criteria. The term image preprocessing, in
general, is referred to all manipulations on an image, each of which produces
a new image.
(c) Image analysis, which usually returns numeric values and/or graphical infor-
mation about the image characteristics that are suited for classification, defect
detection, or prediction of some of the quality properties of the imaged object.
The term image analysis is used when the output is a number or decision, not
an image.
These processing actions are related and may have a different effect or output for
each application. The following sections describe basic concepts and characteristics
of a digital image and how they are processed and transformed (improved), give an
overview of typical image analysis methods and techniques, and show some
application examples in food quality assessment and control.
2.2 The Digital Image
A digital image is a numerical 2D or 3D representation of a real physical object or
scene, from which we can obtain an accurate spatial (geometric) and/or spectral (for
the case of a hyperspectral image) representation with sufficient detail (resolution)
for processing, compression, storage, printing and display. A digital image may be
10 F. Mendoza and R. Lu

of vector or raster type, depending on whether or not the image resolution is fixed
(Wikipedia 2012). Raster images are electronic files that consist of discrete picture
elements, called pixels (short for picture elements). Associated with each pixel is a
number that is the average radiance (or brightness) of a relatively small area within
a scene, representing the color or gray-level at a single point in the image.
Vector graphics formats are complementary to raster graphics, which are the
representation of images based on mathematical expressions, as are typically used
in computer graphics for images made up of vectors (arrows of direction, points,
and lines) that define shapes, as compared to the individual pixels used to represent
a raster image. A vector image is resolution independent, which means the image
can be enlarged or shrunken without affecting the output quality. The term digital
images usually refers to raster images, which are also called bitmap images.
Moreover, it is also applied to data associated to points scattered over a three-
dimensional region, such as produced by tomographic equipment. In that case, each
datum is called a voxel.
The most commonly used method for the creation of raster images (i.e., digital
imaging or image acquisition) is digital photography with a CCD camera in a process
called digitization. The digitization process requires the mapping of the image on a
grid and a quantization of the intensity level. Digitization is the process of converting
an analog signal into a digital signal, known as an A/D (analog to digital) conversion.
For raster images, an analog voltage signal (from any of several types of imaging
sensors), proportional to the amount of light reflected or transmitted by an item being
digitized, is divided into discrete numeric values (Castleman 1979). In other words,
the process converts an image into a series of small picture square elements or pixels
that are either black or white (binary), a specific shade of gray (grayscale) or color.
Each pixel is represented by a single or series of binary digits, either 1 s or 0 s.
By measuring the color (or gray-level for black and white photos) of an image at
a large number of points, we can create a digital approximation of the image from
which a copy of the original can be reconstructed.
Figure 2.1 depicts an example of the process for creation of a digital image.
This figure shows the energy from an illumination source being reflected from a
Voltage
Signal
Image Buffer
(an array of numbers)
Computer
Either
Camera
Lens
Physical
World
Camera
Electronics
# # # # # # # # # #
# # # # # # # # # #
# # # # # # # # # #
# # # # # # # # # #
# # # # # # # # # #
# # # # # # # # # #
# # # # # # # # # #
# # # # # # # # # #
# # # # # # # # # #
# # # # # # # # # #
Digital
Data
Computer
Interface
From physical object to grayscale image
Fig. 2.1 An example of the process for creation of a digital image
2 Basics of Image Analysis 11

scene element (apple). The imaging system collects the incoming energy and
focuses it onto an image plane. The front end of the imaging system is an optical
lens that projects the viewed scene onto the lens focal plane. The camera sensor is
an array of photo cells which measure light at discrete points. The sensor does not
directly recognize the color of the incoming light; instead, in a color camera (with
3 CCD sensors) a prism separates the light into three components or color channels
i.e., R (for red), G (for green) and B (for blue). The response of each sensor, which is
coincident with the focal plane, is proportional to the integral of the light energy
projected onto the surface of the sensor. Once a digital image has been created and
stored in any media, there is a corresponding digital to analog conversion that
allows the computer to present the image in a human readable form on either a
display or printer. Displaying an image on a computer monitor or printing the image
on a printer are both examples of an analog representation of a digital image.
In order to process and analyze images on a computer we ﬁrst have to digitize them.
Figure 2.2 emphasizes the characteristics of a digital color image. The displayed
color image is a two-dimensional matrix of thousands or millions of pixels, each of
Fig. 2.2 Characteristics of a digital color image: (a) original image (340 256 pixels),
(b) magniﬁed color image (1,600) to visualize the pixels, (c) the numerical R, G, and B color
intensity values for a selected region

which has its own address, size, and grayscale or color channel representation.
By zooming in on this digital image, it is noticed that the image is composed of a
series of rows and columns of square pixels. Each pixel represents the brightness or
intensity value of a given color channel (or gray-level) at any single specific point in
the image. Typically, the pixel information is stored in computer memory as a raster
image or raster map, a 2D array of integers. These values are often transmitted or
stored in a compressed form.
Constraints of the digitized representation are that it contains much less
information of the original scene since a 3D scene is reduced to a 2D representation.
The sensors in any acquisition device (still or video cameras, scanners among other
sensors) in general are not capable to capture and reproduce exactly, although not
sensitive to the human vision, all color information from the real scene. Moreover,
the size and location of the objects on the image are now estimates, whose precision
and accuracy are dependent on the sampling resolution. The advantages of digital
images are that they can be processed and analyzed, in many ways, by computers.
2.2.1 Basic Image Measurements
There are three basic measures of every static digital image: spatial resolution,
pixel bit depth, and color (Puglia 2000). The specifications selected for each
measure determine the amount of electronic information captured to represent the
original photograph or scene. Generally, the higher the values are within these
measures, the more data will be captured representing a greater amount of photo-
graphic detail from the original.
2.2.1.1 Resolution
Spatial resolution is defined as the rate, or number of times, at which an image is
sampled during the acquisition or imaging process. More specifically, it is the
frequency of pixels used to capture sample shades in the space of the object being
digitized. Spatial frequency is synonymous for spatial resolution. Generally, more
pixels per unit dimension means a higher resolution, but the overall image quality
cannot be determined by spatial resolution alone. Typical array sizes in pixels
(or pixel resolution) in many imaging sensors vary from 640 480 to
2,048 1,536 pixels. For reference human vision is 100 million pixels.
Quantitatively, spatial resolution (i.e., the number of pixels in an image) can
be described in a number of ways, being the most common measures: dpi (dots
per inch), ppi (pixels per inch), and lpi (line pairs per inch) (Gonzales and
Woods 2008). The resolution of a digital image file is most appropriately referred
to as pixels per inch or ppi. The dpi and lpi measures are considered printing terms
and are most appropriate when referring to the resolution at which a computer

printer produces a print. However, dpi is a more generic term and is more
commonly used than ppi in image reproduction and photography.
In science and engineering research, however, the dpi term is not frequently
reported when referring to the resolution of images. Generally, they are expressed
by a spatial or unit distance per pixel, i.e., mm/pixel, μm/pixel, etc., and sometimes
represented by a horizontal bar on the same image indicating the actual spatial
length of that bar (as shown in Fig. 2.3b). Figure 2.3 presents two examples of
images at different dpi levels while keeping their size the same. Reducing the
resolution of the original image generally leads to larger pixels and less detail in the
image. Images saved with the lowest dpi levels appear blurred and have a reduced
contrast. By decreasing the image resolution of the apple tissue (Fig. 2.3b) the finest
details of the tissue microstructure are lost, making it more difficult for the
visualization and analytical determination of the smallest structures.
2.2.1.2 Pixel Bit Depth
This measure is sometimes called pixel depth or color depth. This defines the
number of shades that can actually be represented by the amount of information
saved for each pixel. Computers work on a binary system; each bit of data is either
1 or 0. Each pixel in a raster image is represented by a string of binary digits and the
number of digits is known as the bit depth. Hence, a one-bit image can assign only
one of two values to a single pixel: 0 or 1 (black or white). An 8-bit (28
) grayscale
image can assign one of 256 colors to a single pixel. A 24-bit (2(38)
) RGB image
(8-bits each for red, green and blue color channels) can assign one of 16.8 million
colors to a single pixel. The bit depth determines the number of possible
Fig. 2.3 Images saved at different dpi levels while keeping the same size. (a) typical effects on a
color image; (b) X-ray microtomography image of ‘Jonagold’ apple captured with a spatial
resolution of 8.5 μm/pixel, where black areas represent pores and gray areas the cellular material

combinations of 1 s and 0 s for that number of binary digits and therefore the
number of gray shades or color shades that can be represented by each pixel. This is
calculated by the following formula (Puglia 2000):
Number of Shades ¼ 2x
, where x ¼ the bit depth: ð2:1Þ
The bit depth influences the representation of images. The greater the bit depth,
the finer the levels of change can be recorded. Consequently, more space is needed
in the computer system to handle and store the image. Although grayscale images
with bit depths of 2, 4, 6, 12, 16 and 32 exist, 8 bpp (i.e., byte-per-pixel) grayscale
images are the most common. This is for two reasons; first, the 8 bpp size makes it
easier to manipulate with a computer, and second, since the human eye can
distinguish less than 200 shades, it can faithfully represent any grayscale image
because it provides 256 distinct levels of gray. Thus, the use of 8-bit grayscale
image files and corresponding 24-bit RGB color image files, in many cases,
represent a reasonable compromise for image processing and analysis purposes.
Some scientific applications requiring wider or higher dynamic ranges, as in multi-
and hyper-spectral imaging, frequently use camera sensors with 14-bit (16,384
shades) or 16-bit (65,536 shades) in order to reduce the noise level for a higher
signal-noise-ratio.
To achieve a desired bit depth without any data loss, it is necessary to digitize a
photograph at a higher bit depth and then scale down to the desired bit depth after
any image processing has occurred. In addition to the loss of data from small
fluctuations in the acquisition system, raw digital images often require minimal
processing (e.g., sharpening or minimal tonal corrections). Any processing of a
digital image results in some data loss. Acquiring and processing an image at a
higher bit depth and then reducing to the desired bit depth will minimize the impact
of the data loss and provide a file with the desired quality.
2.2.1.3 Color Representation
Several different systems are used to represent color images. The most common are
RGB (additive color system), CMYK (subtractive color system), HSV and the
CIELAB color space. A color space is a specific implementation of a color model.
There are many RGB and CMYK color spaces defined for specific purposes and
applications (such as sRGB and CMY). The terms color space and color profile can
often be used interchangeably, since a profile is a description or numerical model of
a specific color space.
Operating systems and programs need to have access to an embedded profile that
describes the meaning of the color values in order to interpret the color correctly.
There are two types of profiles: matrix-based and table-based. Matrix-based
profiles use mathematical formulas to describe the 3D color spaces. They can be
relatively small and most appropriate as working spaces and as embedded profiles.
Table-based profiles, as the name implies, use a large table of sample points called a

Look-Up Table or LUT to deﬁne the 3D color space. These proﬁles are more
customizable, and are therefore more useful when translating color information
from one space to another, or in describing the color characteristics of a particular
device. Because they rely on many data points, they are much larger.
RGB is a color model that uses the three primary (red, green, blue) additive
colors, which can be mixed to make all other colors. It builds its model by adding
different colors of light together, where the mixture of all three colors produces
white light. Grayscale values follow the line from black (the origin of the coordi-
nate system) to white, as shown in Fig. 2.4a. Digital cameras produce RGB images,
and monitors display RGB images. Mathematical conversions between different
color spaces for analysis and special visualizations are also possible.
CMYK (CMY) is a color model based on subtracting light. The cyan (C), magenta
(M), and yellow (Y) are the basic colors for a subtractive model, and represent the
complements of the three primary colors (see Fig. 2.4a). R, B, G, and black (K) inks
are used in most commercial color printing (books, magazines, etc.). Inks absorb
colored light, which is why the model is called a subtractive one. CMYK is
commonly referred to as process color, and there are many individual color spaces
that use the CMYK color model. Conversions from RGB to CMY are done by using
the following simple equation (Gonzales and Woods 2008):
Intensity
Saturation
Hue
G (0, 255, 0)
R (255, 0, 0)
B (0, 0, 255)
+a (+120)
-a (-120)
+b (+120)
-b (-120)
L (0)
L (100)
Magenta
Cyan
Yellow
Grayscale
Line
a
b c
Fig. 2.4 Commonly used color spaces: (a) RGB color cube; (b) HSV color cone; and (c) CIELAB
or L*
a*
b*
color system

C
M
Y
2
4
3
5 ¼
1:0
1:0
1:0
2
4
3
5
R
G
B
2
4
3
5 ð2:2Þ
If a color image has to be printed, black (K) as a fourth color is added to the model
to achieve a purer black than the simple combination of the other three colors,
resulting in the CMYK model. Transformation from CMY to CMYK is done by:
K ¼ min CCMY; MCMY; YCMY
ð Þ ð2:3Þ
CCMYK ¼ CCMY K ð2:4Þ
MCMYK ¼ MCMY K ð2:5Þ
YCMYK ¼ YCMY K ð2:6Þ
HSV is a user-oriented color model based on the artist’s idea of tint, shade and tone.
HSV expresses color into three components that vary from 0 to 1; H (hue) distin-
guishes among the perceived colors, such as red, yellow, green and blue,
S (saturation) refers to how much of the light is concentrated at each speciﬁc
wavelength of the hue; and V (value) represents the total brightness (see Fig. 2.4b).
The computation of H, S, and V values from the RGB color space is made according
to the following expressions (Du and Sun 2005):
V ¼ max nR, nG, nB
ð Þ ð2:7Þ
S ¼
V min nR; nG; nB
ð Þ
V
ð2:8Þ
Let
tR ¼
V nR
V min nR; nG; nB
ð Þ
; ð2:9Þ
tG ¼
V nG
V min nR; nG; nB
ð Þ
; ð2:10Þ
tB ¼
V nB
V min nR; nG; nB
ð Þ
; ð2:11Þ

Then
6H ¼
5 þ tB if nR ¼ max nR; nG; nB
ð Þ and nG ¼ min nR; nG; nB
ð Þ
1 tG if nR ¼ max nR; nG; nB
ð Þ and nG 6¼ min nR; nG; nB
ð Þ
1 þ tR if nG ¼ max nR; nG; nB
ð Þ and nB ¼ min nR; nG; nB
ð Þ
3 tB if nG ¼ max nR; nG; nB
ð Þ and nB 6¼ min nR; nG; nB
ð Þ
3 þ tG if nB ¼ max nR; nG; nB
ð Þ and nR ¼ min nR; nG; nB
ð Þ
5 tR otherwise
8

:
ð2:12Þ
where H, S, V 2 0, . . . , 1
½ .
In food research, color is frequently represented using the CIELAB or L*
a*
b*
color space since results closely match those of the human perception. L*
is the
luminance or lightness component that goes from 0 (black) to 100 (white), and
parameters a*
(from green to red) and b*
(from blue to yellow) are the two
chromatic components, varying from 120 to +120 (see Fig. 2.4c). The definition
of L*
a*
b*
is based on the intermediate system CIE XYZ which is derived from RGB
(Rec. ITU-R BT.709-5, 2002). Thus, L*
, a*
, and b*
are defined as:
L*
¼ 116
Y
Yn
1=3
16 ð2:13Þ
a*
¼ 500
X
Xn
1=3

Y
Yn
1=3
#
ð2:14Þ
b*
¼ 200
Y
Yn
1=3

Z
Zn
1=3
#
ð2:15Þ
where Xn, Yn, Zn correspond to the XYZ values of a reference white chart.
2.2.2 Types of Files
File types are used to encode digital images, allowing for compression and storage.
Image files can be of different sizes and formats, and larger file types mean more
disk usage and slower download. Compression is a term used to describe ways of
reducing the size of the file. Compression schemes can by lossy or lossless (Shapiro
and Stockman 2001). A lossless compression algorithm does not discard informa-
tion. It looks for more efficient ways to represent an image, while making no
compromises in accuracy. A lossless algorithm might, for example, look for a
recurring pattern in the file, and replace each occurrence with a short abbreviation,
thereby cutting the file size.

Contrarily, a lossy algorithm might accept some degradation in the image in
order to achieve smaller file size. For example, JPEG or JPG file format works by
analyzing images and discarding information that the eye is least likely to notice.
Changes in luminance are more significant by the human observer than change in
hue. Thus, in JPEG compression, factors of more than 20 are often quite acceptable.
Better graphics programs, such as Paint Shop Pro and Photoshop, allow viewing
the image quality and file size as a function of compression level, so that it is
possible to conveniently choose the balance between qualities and file size. Higher
JPEG compression equals lower image quality since the color information in
individual pixels is compressed into blocks of pixels using mathematical algorithms
that methodically blend all the pixel colors in each block. Increasing the compres-
sion produces smaller computer file sizes, whereas lower compression produces
better quality but larger computer file sizes.
The most common digital image file types and their main characteristics are
summarized in Table 2.1. Currently, GIF and JPEG are the formats used for nearly
all web images. PNG is supported by most of the latest generation browsers. TIFF is
not widely supported by web browsers, and should be avoided for web use. PNG
does everything GIF does, and better; so it is expected to replace GIF in the future.
PNG will not replace JPEG, since JPEG is capable of much greater compression of
photographic images, even when set for quite minimal loss of quality.
Table 2.1 Common digital file types
File type Description
TIFF Tagged Image File
Format
• Lossless uncompressed file format with 24 or 48 bit color
support. File sizes are quite big
• Supports embedded information like EXIFa
, calibrated color
space and output profile information
• There is a lossless compression for TIFF called LZW. LZW
works like zipping the image file because there is no quality
loss. An LZW TIFF decompresses with all of the original
pixel information unaltered
PNG Portable Network
Graphics
• Lossless compression format with up to 16-bit depth for
grayscale values and up to 48-bits for color values
• In contrast with common TIFF files, it looks for patterns in
the image that it can use to compress file size
• The compression is exactly reversible, so the image is
recovered exactly
JPEG Joint Photographic
Experts Group
• Lossy compression format optimized for photographs that
contain many colors which store information as 24-bit color
and the degree of compression is adjustable
• JPEG compression ratio and resolution are not relational. It
is possible to get a high resolution JPEG image with either
low or high compression
• Supports embedded information like EXIF, calibrated color
space and output profile information
• This is the best format for photographs to be shown on the
web or as email attachments
(continued)

2.2.3 Types of Digital Images
As described above, each pixel of a raster image is typically associated to a specific
position in the 2D region, and has a value consisting of one or more quantities
(samples) related to that position. Digital images can be classified according to the
number and nature of those samples in: binary, grayscale, color, false-color, multi-
spectral, thematic, and picture function (Shapiro and Stockman 2001).
For image processing and analysis, the input image is supposed to be grayscale
or RGB. However, there are four basic types of digital images frequently used as
intermediate steps in the processing of an image, which allow to identify, enhance,
quantify and also represent specific characteristics or regions of interest on an
image. They are: indexed color images, intensity images, binary, and labeled
images.
Table 2.1 (continued)
File type Description
GIF Graphics Interchange
Format
• Lossless uncompressed 8-bit file format that supports only
256 distinct colors. GIF creates a table of up to 256 colors
from a pool of 16 million
• Not suitable for photographs because of its limited color
support. Best used with web clip art and logo type images
BMP Bitmap Format • Lossless uncompressed file format that supports 24-bit color
invented by Microsoft
• Does not support embedded information like EXIF, cali-
brated color space and output profiles
• BMP produces approximately the same file sizes as TIFF
without any of the advantages of TIFF
RAW • Lossless compressed file format that is proprietary for each
digital camera manufacturer and model
• Though lossless, it is a factor of three or four smaller than
TIFF files of the same image
• It contains the full range of color information from the
sensor. RAW files converted to 16-bit TIFF produce the
absolute best quality image available from any digital
camera
• Camera RAW supports imbedded EXIF data
PSD,
PSP
Photoshop file, Pain
Shop Pro
• Proprietary formats used by graphic programs
• Preferred working formats as you edit images in the soft-
ware, because only the proprietary formats retain all the
editing power of the programs
• These packages use layers building complex images, and
layer information may be lost in the nonproprietary formats
such as TIFF and JPG
a
EXIF stands for Exchangeable Image File Format. This data includes technical information about
each photograph including calibrated color space, output profile device information shutter speed
and aperture used, whether or not flash was used, and the date the photo was taken

2.2.3.1 Indexed Color Images
A digital color image is an image that includes the R, G, and B color channel
information or other color representation for each pixel. An indexed color image
consists of two arrays: an image matrix and colormap. The colormap (also called a
palette) is an ordered set of values that represent the colors in the image. For each
image pixel, the image matrix contains a value that is an index into the colormap.
In computing, the encoding of the color image data in this way allows for saving
computer memory and file storage, while speeding up refreshing and file transfers.
It is a form of vector quantization compression.
2.2.3.2 Intensity Images
An intensity image or digital grayscale image is an image in which the value of
each pixel is a single sample, that is, it carries only intensity information. These
images are composed exclusively of various shades of gray, varying within a given
range from 0 (for black) to 1 (or 255 for white), with any fractional values in
between. However, it must be noted that this does not define what black or white is
in terms of colorimetry. Another convention is to employ percentages, so the scale
is then from 0 to 100 %. This is used for a more intuitive approach, but if only
integer values are used, the range encompasses a total of only 101 intensities, which
are insufficient to represent a broad gradient of grays.
Grayscale images are often the result of measuring the intensity of light at each
pixel in a single band of the electromagnetic spectrum (e.g., infrared, visible light,
ultraviolet, etc.), and in such cases they are monochromatic when only a given
frequency is captured. Multispectral or hyperspectral images have numerous bands
or a finer spectral resolution, and they are examples of this type of intensity images.
But they also can be synthesized from a full color image by converting to grayscale.
Grayscale images are also called monochromatic because of the absence of any
chromatic variation (i.e., one color).
If a color image has to be converted into an intensity or grayscale image, the
following equations can be used. One alternative is the simple average of the R, G,
B color channels:
I ¼ 0:333 R þ 0:333 G þ 0:333 B ð2:16Þ
Another equation, which takes into account the luminance perception of the
human eye, is
Y ¼ 0:2162 R þ 0:7152 G þ 0:0722 B ð2:17Þ
The weights used to compute luminance are related to the monitor’s phosphors.
The explanation of these weights is due to that for equal amounts of color, the eye is
more sensitive to green, then red, and then blue. This means that for equal amounts

of green and blue light, the green will, nevertheless, be much brighter. Thus the
image obtained by the normal averaging of an image’s three color channels
produces a grayscale brightness that is not perceptually equivalent to the brightness
of the original color image. The weighted sum that defines Y (Eq. 2.17),
however, does.
2.2.3.3 Binary
A binary image has only two possible values (0 and 1) for each pixel. Typically the
two colors used for a binary image are black and white, though any two colors can
be used. The color used for the object(s) in the image is the foreground color while
the rest of the image is the background color. Thus, the set of all white pixels in a
binary image is a complete morphological description of the image (Gonzales and
Woods 2008). Binary images are also called one-bit, bi-tonal, bi-level or two-level.
This means that each pixel is stored as a single bit (0 or 1, see Sect. 2.2.1.2). In digital
image processing, binary images often arise as masks or result from certain opera-
tions such as segmentation, thresholding, and dithering. A binary image is usually
stored in memory as a bitmap, a packed array of bits (Wikipedia 2012).
2.2.3.4 Labeled Images
A labeled image is a digital image whose pixel values are symbols from a finite
alphabet. The symbol value of a pixel denotes the outcome of some decision made
for that pixel. An example is the labeling of objects in a binary image, which means
that these objects are classified and numbered. Related concepts are thematic
image, false-color image and pseudo-colored image. In a false-color image this
close correspondence between subject color and image color is altered. The term
false-color is typically used to describe images whose colors represent measured
intensities outside the visible portion of the electromagnetic spectrum. Pseudo-
coloring can make some details more visible, by increasing the distance in color
space between successive gray levels. Pseudo-color images differ from false-color
images in that they are made from only one original grayscale image, rather than
two or three.
2.3 Steps for Image Processing and Analysis
Image processing involves a series of image operations to enhance the quality of a
digital image so as to remove defects such as geometric distortion, improper focus,
repetitive noise, non-uniform lighting and camera motion. Image analysis is the
process of distinguishing the objects (regions of interest or ROIs) from the back-
ground and producing quantitative information, which is subsequently used for

decision making. Processing and analysis can be performed on many different types
of image data. These include, in an increasing order of complexity: binary images,
grayscale, color, polarized-light, multi-spectral and hyper-spectral, 3D images,
multi-sensor and multimedia systems, and image sequences and video.
Gunasekaran and Ding (1994) defined three levels of image processing, named,
low level processing which includes image acquisition and pre-processing of
images, intermediate level processing which involves image segmentation, image
representation and description, and high level processing which involves recogni-
tion of ROIs and interpretation for quality sorting and grading. The terms machine
vision or computer vision is often used for the entire subject, including image
processing and analysis and pattern recognition techniques. Hence, the process
of making a decision involves a number of steps in sequential order. Not all
situations require all of these steps or operations, but all are potentially available
to deal with particular problems.
Machine vision generally consists of the following five steps or operations
(Fig. 2.5): (1) image acquisition operations to convert images into digital form, as
explained in Sect. 2.2; (2) pre-processing operations to obtain an improved image
with the same dimensions as the original image; (3) image segmentation operations
to partition a digital image into disjoint and non-overlapping regions; (4) object
measurement operations to measure the characteristics of objects, such as size,
shape, color and texture; and (5) classification or sorting operations to identify
objects by classifying them into different groups.
(3) Segmentation
of ROIs (4) Features
Extraction (5) Classification
Classes or patterns
Object of
interest
Matrix of
features
X1 ……
X2 ……
X3 ……
. ……
. ……
XN ……
...........
.
...........
...... (CCD, scanners,
MNR, X-ray, etc.)
(to remove noise
and enhance image
characteristics)
(to identify ROI’s from
image backgound)
(2) Pre-processing
(color intensities,
textural features,
and geometrical
properties)
(discriminant analysis, neural
networks, and fuzzy logic, etc.)
Improved
image
Raw
image
(1) Image
Acquisition
Z1
Z2
Z3
.
.
ZN
...........
.
...........
......
...........
.....
Fig. 2.5 An overview of the operational steps for a machine vision system

2.4 Image Processing
Image processing or pre-processing encompasses a broad range of operations,
which may be treated as an end in themselves, or are intended to simplify or
enhance subsequent analysis. Pre-processing improves the image data by removing
unintended distortions or enhancing some image features that are important for
further processing and creating a more suitable image than the original for a specific
application. The operations that can be performed on digital images include point,
local or neighborhood, and global operations.
Point operations transform pixels without regard to neighboring pixels. The gray
value of the output image at a particular pixel depends only on the gray value of the
same pixel in the input image. They map the pixels in one image to form another using
a single mapping function. Point operations do not consider the spatial organization
of the image, which forms the fundamental character of images as opposed to other
types of data. Examples of these operations include contrast stretching, segmentation
based on gray value, and histogram equalization (Marchant 2006). The term contrast
refers to the amplitude of gray level variations within an image.
A local or neighborhood operation or mask operation generates an output pixel
whose value depends on the pixel values in a neighborhood of the corresponding
input point. Examples include convolution (as for image smoothing or sharpening)
and spatial features detection (e.g., line, edge, and corner detection). A large and
powerful class of non-linear neighborhood operations is morphological methods;
they extend naturally to gray-level (and multiband) images (Soille 1999).
Finally, an operation is a global operation if the output value at specific
coordinate is dependent on all the values in the input images. Spatial domain
processing methods include all three types, but frequency domain operations, by
nature of the frequency (and sequence) transforms, are global operations. Of course,
frequency domain operations can become mask operations, based only on a local
neighborhood, by performing the transform on small image blocks instead of the
entire image. This section presents a brief description of the types of algorithms
commonly utilized for digital image processing. We intentionally limit the discus-
sion to the types of image processing algorithms that are widely used in applications
for foods.
2.4.1 Grayscale Operations for Image Enhancement
Once the grayscale or color image is obtained, one of several different techniques
may be used to improve the quality of the image. Image enhancement techniques
are used to emphasize and sharpen image features for further analysis in order
to facilitate the development of a solution to a specific application problem.
Consequently, the enhancement methods are application specific and are often
developed empirically.

2.4.1.1 Arithmetic Operations
All arithmetic operations performed on matrices may be performed on images.
Arithmetic operations between images are array operations carried out between
corresponding pixel pairs. Hence, the images normally have to be of the same size.
These operators are frequently used for reducing noise and image enhancement.
The four arithmetic operations are as follows:
s x; y
ð Þ ¼ f x; y
ð Þ þ g x; y
ð Þ ð2:18Þ
d x; y
ð Þ ¼ f x; y
ð Þ g x; y
ð Þ ð2:19Þ
p x; y
ð Þ ¼ f x; y
ð Þ g x; y
ð Þ ð2:20Þ
v x; y
ð Þ ¼ f x; y
ð Þ g x; y
ð Þ ð2:21Þ
where x ¼ 0, 1, 2, . . ., M1 and y ¼ 0, 1, 2, . . ., N1 for images with the size of
M rows and N columns.
Addition is a discrete version of continuous integration, and it is an operation
frequently used to create double-exposures or composites. A common variant of
this operator simply allows a specified constant to be added to every pixel in an
image, so as to brighten that image. Image subtraction is frequently used to enhance
differences between images; such as finding changes between two images.
The multiplication or division of two images is performed in two main forms.
The first form takes two input images and produces an output image in which the
pixel values are just those of the first image, multiplied (or divided) by the values of
the corresponding values in the second image. The second form takes a single input
image and produces output in which each pixel value is multiplied (or divided) by a
specified constant. This latter form is probably the more widely used and is
generally called scaling.
Image averaging is an application for correcting noisy images. Averaging of
multiple pictures of the same scene helps to reduce this noise. In practice, however,
the images must be previously registered (aligned) in order to avoid the introduc-
tion of blurring and other artifacts in the output image.
Other related operators are the logical operators such as And, Or, Not, If. . .Then,
If and only If, among others which are often used to combine (mostly binary) two
images. An example is when a mask is used for selecting a region of interest from
an image.
2.4.1.2 Histogram Equalization
The histogram of a digital image gives the numeric (quantitative) information about
the distribution of the number of pixels per gray-level value. Histograms are the

basis for numerous spatial domain processing techniques, and their manipulation
can be used for image enhancement. In addition to providing useful image statistics,
the information inherent in histograms also is quite useful in other image processing
applications, such as image compression and segmentation. Mathematically, the
histogram of a digital image is a discrete function h(k) ¼ nk/n, where k ¼ 0, 1, . . .,
L1 is the kth gray-level, nk is the number of pixels in the image having gray-level
k, and n is the total number of pixels in the image.
Frequently, an image is acquired in such a way that the resulting brightness
values do not make full use of the available dynamic range. Histogram equalization
is a common point operation method for spreading the histogram of pixel levels
more evenly. The gray levels are transformed with a function such that all
gray values are equally represented in the histogram. In the method each original
gray-level k is mapped into new gray-level i by:
i ¼
Xk
i¼0
h j
ð Þ ¼
Xk
j¼0
nj
n
ð2:22Þ
where the sum counts the number of pixels in the image with gray-level equal to or
less than k. Thus, the new gray-level is the cumulative distribution function of the
original gray-levels, which is always monotonically increasing. The resulting
image will have a histogram that is flat in a local sense, since the operation of
histogram equalization spreads out the peaks of the histogram while compressing
other parts of the histogram. In more complicated cases, the global histogram may
not be a good representation for local statistics in two parts of the image. In such
cases it is best to use an adaptive histogram equalization where you can divide the
image into several rectangular domains, compute an equalizing histogram and
modify levels so that they match across boundaries (Macaire and Postaire 1989).
Figure 2.6 illustrates the image enhancement of a loaf of bread using global
histogram equalization. In general, the function is non-linear. It is important to
realize that whatever point operation is used, the separation into ROIs is still not
possible. Thus, although the image in Fig. 2.6c appears easier to separate into
components, a machine vision system will still not be able to do it on the basis of
point operations only.
2.4.2 Spatial Image Filtering
With spatial image filtering technique, a window of finite size and shape is
scanned across the entire image, transforming the local intensities in that image.
The window with its weights is called the convolution kernel or filter mask. Thus,
filtering creates a new pixel with coordinates equal to the coordinates of the center
of the neighborhood, and whose value is the result of the filtering operation.
Two main linear spatial filtering methods are correlation and convolution.
Correlation is the process of moving a filter mask over the image and computing

the sum of products at each location. The mechanisms of convolution are the same,
except that the filter is first rotated by 180
(Gonzales and Woods 2008). Correla-
tion is often used to measure the similarity between images or parts of images
(e.g., pattern matching). Correlation and convolution yield the same result when the
filter mask is symmetric. Nonetheless, basic image processing techniques are
mainly based on convolution.
The convolution is performed by sliding the kernel over the image, generally
starting at the top left corner and moving the kernel through all the positions within
the boundaries of the image. Thus, in a convolution, a convolution kernel is applied
to every pixel to create a filtered image, Iout(x, y):
Iout x; y
ð Þ ¼ Iin x; y
ð Þ*W x; y
ð Þ ¼
X1
u¼1
X1
u¼1
Iin u; v
ð ÞW u x, v y
ð Þ
ð2:23Þ
where the minus signs on the right flip W (i.e., rotate it by 180
). Flipping and
shifting W instead of the input image, Iin(u,v), is done for notational simplicity and
also to follow convention, which is the same as flipping and shifting the input
0
0.5
1
1.5
2
x 104
0 50 100 150 200 250
0
0.5
1
1.5
2
x 104
0 50 100 150 200 250
a b
c d
Fig. 2.6 Image quality enhancement using histogram equalization: (a) grayscale image of a loaf
bread; (b) histogram of the image in (a); (c) resulting image obtained from image (a) by histogram
equalization; (d) histogram of the image in (c)

image. It should be noticed that a special treatment is needed for calculating the
border of the new image during spatial filtering. Klinger (2003) suggests four
possibilities for handling the border of an image. First, if the convolution filter
has a size of 3 3, get an image smaller by the border of one pixel. Second, keep
the same gray-level values of the original image for the new border pixels.
Third, use special values for the new border pixels; for example, 0, 127, or 255.
Finally, use pixels from the opposite border for the calculation of the new values.
2.4.2.1 Image Smoothing and Blurring
All smoothing filters build a weighted average of the surrounding pixels, and some
of them also use the center pixel itself. Averaging and Gaussian filters are linear
filters often used for noise reduction with their operation causing a smoothing in the
image but having the effect of blurring edges.
The average or low-pass filter is a linear filter and one of the simplest types of
neighborhood operation. In the average filtering the new value is calculated as the
average of all nine pixels (for a [3 3] kernel) using the same weight. The elements
of the mask must be positive and the coefficients for the center pixel are either 0 or
1. With the averaging filter, the mean value of the pixels in the neighborhood is used
to form a pixel in a new image at the same location as the center pixel in the original
image. Like all averaging operations, this can be used to reduce some types of noise
at the expense of a loss of sharpness as it is shown in Fig. 2.7. The averaging
operation is represented by,
Iout x; y
ð Þ ¼
1
9
Xxþ1
u¼x1
Xyþ1
v¼y1
Iin u; v
ð Þ ð2:24Þ
Rather than weight all input pixels equally, it is better to reduce the weight of the
input pixels with increasing distance from the center pixel. The Gaussian filter does
this and is perhaps the most commonly used of all filters (Shapiro and
Stockman 2001). The center pixel coefficient of a Gaussian kernel is always greater
than 1, and thus greater than the other coefficients because it simulates the shape of
a Gaussian curve according to,
Fig. 2.7 Examples of averaging filter using masks [3 3] and [9 9]

Gout x; y
ð Þ ¼
1
ffiffiffiffiffi
2π
p
σ
exp
d2
2σ2

ð2:25Þ
where d ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
x xc
ð Þ2
þ y yc
ð Þ2
q
is the distance of the neighborhood pixel [x,y]
from the center pixel [xc,yc] of the output image to which the filter is being applied.
Convolution with this kernel forms a weighted average which stresses the point at
the center of the convolution window, and incorporates little contribution from
those at the boundary. As σ increases, more samples must be obtained to represent
the Gaussian function accurately. Therefore, σ controls the amount of smoothing.
A second derivative filter based on the Laplacian of the Gaussian is called a LOG
filter. Examples of Gaussian filter using masks [3 3] and [9 9] with σ ¼ 3 are
given in Fig. 2.8.
Sometimes, non-linear operations on neighborhoods yield better results. An
example is the use of a median filter to remove noise. Median filtering replaces
each pixel by the median in a neighborhood around the pixel. Consider an area of a
scene of reasonably uniform gray-level; the resulting image may have a single pixel
with a very different gray-level due to some random noise. The output of an
averaging filter would contain a proportion of this outlier pixel value. However,
the median filter would set the output pixel to the median value (the 5th largest
gray-level in a 3 3 neighborhood) and so it would be unaffected by the outlier.
The method is very effective for removing salt and pepper noise (i.e., random
occurrences or impulses of black and white pixels) (Gonzales and Woods 2008), as
shown in Fig. 2.9. A disadvantage is that the median filter may change the contours
of objects in the image.
Fig. 2.8 Examples of Gaussian filter using masks [3 3] and [9 9]
Fig. 2.9 Example of median filter using a kernel [3 3]: the input image (left) contains Gaussian
noise, and the noise is removed in the resultant image (right) after 3 3 median filtering

Computing the median requires more computation time than computing a
neighborhood average, since the neighborhood values must be partially sorted.
Moreover, median filtering is not so easily implemented in special hardware that
might be necessary for real-time processing. However, in many image analysis
tasks, its value in image enhancement is worth the time spent.
2.4.2.2 Edge Detection and Enhancement
The detection of edges in an image is an important area of study. An edge is an area
of an image characterized by sharp changes in gray-level or brightness. The process
of edge detection attenuates high fluctuations in color, i.e., dramatic change in
intensity. In the frequency domain, this process refers to the attenuation of high
frequencies. Among the families of edge detection filters are: gradient filters,
Laplacian, and wavelet transform (Klinger 2003). Both gradient and Laplacian
kernels are of the high-pass filter, which operates by differencing adjacent pixels,
because the sharp edges can be described by high frequencies. However, as in other
areas of signal processing, high-pass filtering amplifies noise, if an appropriate
attempt is made to find object boundaries in the image with a simple edge detector
(Marchant 2006).
A gradient filter extracts a significant brightness change in a specific direction
and is thus able to extract edges perpendicular to this direction. These filters are
known as Prewitt filter masks. The Prewitt operator is based on convolving
the image with a small, separable, and integer valued filter in horizontal and
vertical direction and is therefore relatively inexpensive in terms of computations.
Because the components of the gradient vector are derivatives, they are linear
operators.
Another group of gradient masks are the Sobel filters or Sobel kernels. A Sobel
operator gives the specified filter direction a stronger weight. In general, the
gradient specifies the amount of change of a value in a certain direction. First
derivatives in image processing are implemented using the magnitude of the
gradient. The simplest filter kernels, used in two orthogonal directions, are:
Gx ¼
0 0 0
0 1 0
0 1 0
2
4
3
5 ð2:26Þ
and
Gy ¼
0 0 0
0 1 1
0 0 0
2
4
3
5 ð2:27Þ

resulting in two images, Ix ¼ Sx x; y
ð Þ
ð Þ and Iy ¼ Sy x; y
ð Þ

. The mask coefficients
sum to zero, as expected of a derivative operator. The value and direction of the
gradient are therefore calculated as follows:
I ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Sx x; y
ð Þ2
þ Sy x; y
ð Þ2
q
ð2:28Þ
and
θ ¼ arctan
Sx x; y
ð Þ
Sy x; y
ð Þ

ð2:29Þ
The Laplacian operator is an example of a second order or second derivative
method of enhancement. It is particularly good at finding the fine detail in an image.
All masks of the Laplacian filter group are omni-directional, meaning that they
provide edge information in each direction. The Laplacian operators show two
interesting effects. First, if the sum of all coefficients is equal to 0, the filter kernel
shows all image areas with a significant brightness change; that means it works as
an isotropic or omni-directional edge detector. In other words, isotropic filters are
rotation invariant (Gonzales and Woods 2008), in the sense that rotating the
image and then applying the filter gives the same result as applying the filter to
the image first and then rotating the result. Second, if the center coefficient is
greater than the sum of the absolute values of all other coefficients, the original
image is superimposed over the edge information (Klinger 2003).
The simplest isotropic derivative operator is the Laplacian, which for a image
f(x,y) of two variables, is defined as
∇2
f ¼
∂
2
f
∂x2
þ
∂
2
f
∂y2
ð2:30Þ
Laplacian as derivative operator highlights intensity discontinuities in an image
and deemphasizes regions with slowly varying intensity levels. This will tend to
produce images that have grayish edge lines and other discontinuities, all
superimposed on a dark, featureless background. Hence, it is important to define
the type of Laplacian operator used. If the filter mask has a negative center
coefficient, then it subtracts, rather than adds, the Laplacian image to obtain a
sharpened result. Typical Laplacian masks are:
Lsubtract ¼
0 1 0
1 4 1
0 1 0
2
4
3
5 ð2:31Þ
and
Ladd ¼
1 1 1
1 8 1
1 1 1
2
4
3
5 ð2:32Þ

The basic representation using these Laplacian masks for image sharpening is,
g x; y
ð Þ ¼ f x; y
ð Þ þ c ∇2
f x; y
ð Þ

ð2:33Þ
where f(x,y) and g(x,y) are the input and sharpened images, respectively. The
constant c ¼ 1 if the Laplacian filter Lsubtract is used and c ¼ 1 if Ladd is used.
Since derivative filters are very sensitive to noise, it is common to smooth the image
(e.g., using a Gaussian filter) before applying the Laplacian. This two-step process
is call the Laplacian of Gaussian (LoG) operation.
2.4.3 Frequency Filtering
Filtering in the frequency domain consists of modifying the Fourier transform of an
image and then computing the inverse transform to obtain the processed result. Thus,
the Fourier basis can be used to remove high frequency noise from the image of signal,
to extract texture features that can be used to classify the type of objects in an image
region, and also for image compression. Formally, the Fourier transform is defined by
F u; v
ð Þ ¼
ð
1
1
ð
1
1
f x; y
ð Þei2π xuþyv
ð Þ
dx:dy ð2:34Þ
with F(u,v) as the frequencies and f(x,y) as the pixel intensities. The letter i ¼
ffiffiffiffiffiffiffi
1
p
denotes the imaginary unit of complex numbers. The exponential function of
Eq. 2.34 satisfies the Eulerian formula
ei2πα
¼ cos 2πα i sin 2πα ð2:35Þ
for any real α. Thus, the entire set of pixel intensities can be described by a sum of
sine and cosine functions but the result is complex.
The Fast Fourier Transform (FFT) is more efficient than Fourier transform since
it saves computational time by sharing common operations for different pairs of u,
v and is usually used on square images of size 2m
2m
. It can be described by
F u; v
ð Þ ¼
1
m2
Xm1
x¼0
Xm1
y¼0
f x; y
ð Þei2π xuþyv
m
ð Þ ð2:36Þ
where m are the numbers of pixels in x and y directions assuming square images.
Despites its common use in image processing, the Fourier transform can cause
unwanted degradation in local features of images because it is a global transform
that uses all image pixels in every computation of F(u,v) (Shapiro and Stockman
2001). An example of FFT spectrum of a potato chip image and frequency filtering
is shown in Fig. 2.10.

Fig. 2.10 Example of fast Fourier transform (FFT) and frequency filtering: (a) grayscale image of
a potato chip; (b) FFT spectrum of (a) where low frequency components are grouped in the centre
(brighter pixels) and high frequencies near to the corners (darker pixels); (c) low-pass filtered
transform defined by a radius ¼ 100 pixels around the central point and zero-out every point in the
Fourier image that is beyond that radius; (d) low-pass filtered image after applying an inverse
Fourier transform to smooth regions of dark and bright, but lose the sharp contours and crisp edges;
(e) high-pass filtered transform using the same spatial frequency threshold of (c), where only the
higher spatial frequency components are preserved; (f) high-pass filtered image after applying an
inverse Fourier transform, which preserves all of the sharp crisp edges from the original, but loses
the larger regions of dark and bright

2.4.4 Wavelet Transform
The wavelet transform is used primarily for smoothing, noise reduction and lossy
compression. In all cases the procedure is to first perform a forward wavelet
transform, and then perform some operations on the transformed image followed
by an inverse wavelet transform. The reconstructed image exhibits a number of
compression-related artifacts, but it is worth noting that unlike an FFT based
low-pass filter, the advantage of the wavelet transform is that the image contains
a fair amount of high-frequency content.
The wavelet transform is a convolution product of a signal with a scaled and
translated kernel (usually a n-th derivative of a smoothing kernel in order to
precisely detect singularities)
W f u; s
ð Þ ¼
1
s
ð
1
1
f x
ð Þψ
x u
s

dx ð2:37Þ
where s and u are real numbers (s and u 0) which are discretized for computa-
tional purposes. The wavelet transform performs a transformation of a function f(x)
into a function defined over the scale-space plane (pair of values u and s). For
implementation, typical wavelet bases are: Daubechies of order 4, Symlet of order
2 and 4, first derivative of a Gaussian and Mexican Hat wavelets.
Two simple multi-scale wavelet transforms can be used for image processing:
the discrete wavelet transform (DWT) and the continuous wavelet transform
(CWT). For DWT computations, a number of discrete levels can be computed
from the images, and four sets of coefficients are extracted for each level: horizon-
tal, vertical, diagonal and approximation coefficients (Mallat 1989). For CWT
computations, the extension of the previous concept from 1D (Eq. 2.25) to 2D is
given by Pi~
nuela et al. (2007):
M f u; v; s
ð Þ ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
W1
f u; v; s
ð Þ

2
þ W2
f u; v; s
ð Þ

2
q
ð2:38Þ
with u and v denoting the 2D coordinates and the scale parameter being usually used
as s ¼ 2j. Now, two separate wavelet transforms are computed: W1
refers to the
wavelet transform performed along the horizontal dimension and W2
refers to the
vertical one.
2.4.5 Binary Morphological Operations
Morphological operations are methods for processing binary images based on
shapes. Basically, morphological operations change the structure of the elements
or particles in an image. In a binary image, the elements or particles are defined as

the segmented regions (or ROIs) in which the pixel value is 1. The rest of the image
(pixel value 0) is called background. In these operations, the value of each pixel in
the output image is based on the corresponding input pixel and its neighbors.
Morphological operations can be used to construct filters similar to the spatial
filters discussed above. The basic operations of binary morphology are dilation,
erosion, closing, and opening.
Morphological operations use a structuring element to calculate new pixels,
which plays the role of a neighborhood or convolution kernel in other image
processing operations (as shown in filter mask operations). Figure 2.11 shows two
typical examples of structuring elements. The shape of the structuring element
could be rectangular, square, diamond, octagon, disk, etc. The connectivity defines
whether four or all eight surrounding pixels are used to calculate the new center
pixel value (in the case of a [33] structuring element) (Klinger 2003).
2.4.5.1 Erosion and Dilation
These two operators are fundamental for almost all morphological operations
(Gonzales and Woods 2008). Opening and closing are also duals of each other
with respect to set complementation and reflection. Thus, Erosion is an operator
that basically removes objects smaller than the structuring element and removes
perimeter pixels from the border of larger image objects (sets the pixel value to 0).
If I is an image and M is the structuring element (mask), the erosion (operator ) is
defined as:
erosion I
ð Þ ¼ I M ¼ a2MIa ð2:39Þ
where Ia indicates a basic shift operation in the direction of element a of M and Ia
would indicate the reverse shift operation.
Contrarily, a dilation operation enlarges a region. A dilation adds pixels to the
perimeter of each image object (sets their values to 1), filling in holes and broken
0 1 0
1 1
0 1 0
1 1 1
1 1
1 1 1
a b
Origin of the [3×3]
structuring element
Fig. 2.11 Examples of
square structuring elements
(a) connectivity 4;
(b) connectivity 8

areas, and connecting areas that are separated by spaces smaller than the size of the
structuring element. The dilation (operator ) is defined as:
dilation I
ð Þ ¼ I M ¼ [a2MIa ð2:40Þ
2.4.5.2 Opening and Closing
Both operations generate a certain amount of smoothing on an object’s contour.
An opening operation (erosion then dilation) can separate objects that are connected
in a binary image. Opening generally smoothes the contour of an object, breaks
narrow isthmuses, and eliminates thin protrusions. Mathematically, the opening
function can be described by
opening I
ð Þ ¼ dilation erosion I
ð Þ
ð Þ ð2:41Þ
or, using the operator ∘,
I∘M ¼ I M
ð Þ M ð2:42Þ
The closing operation is defined as dilation followed by an erosion using the
same structuring element. A closing operation can close up internal holes and gaps
in a region and eliminate bays along the boundary.
closing I
ð Þ ¼ erosion dilation I
ð Þ
ð Þ ð2:43Þ
or, using the operator •,
I
M ¼ I M
ð Þ M ð2:44Þ
It should be noted that multiple openings or closings have no effect after the
operator has been applied once.
2.5 Image Segmentation
Image segmentation is one of the most important steps in the entire image
processing technique, as subsequent extracted data are highly dependent on the
accuracy of this operation. Its main aim is to divide an image into regions that have
a strong correlation with objects or areas of interest (i.e., ROIs). Segmentation can
be achieved by three different techniques: thresholding, edge-based segmentation,
and region-based segmentation (Sonka et al. 1999; Sun 2000). These algorithms,
their modifications, and combinations with other approaches are frequently used in
food quality applications.

Thresholding or global thresholding is a simple and fast technique for
characterizing image regions based on constant reflectivity or light absorption of
their surfaces. Edge-based segmentation relies on edge detection by edge operators.
Edge operators detect the sharp discontinuities in gray level, color, texture, etc., in
the image. Region-based segmentation involves the grouping together of similar
pixels to form regions representing single objects within the image (such as in the
seeded region growing method). The segmented image may then be represented as
a boundary or a region. Boundary representation is suitable for analysis of size and
shape features, while region representation is used in the detection and evaluation
of texture, defects or simply ROIs (Brosnan and Sun 2004).
Two examples are presented to illustrate the segmentation process. Figure 2.12
shows the image segmentation process for green and yellow bananas using a simple
global thresholding. In general, successful results are found for the identification of
the true area of both bananas since the intensity distribution of the banana peel and
background pixels are sufficient distinct. The histogram is bimodal with the peaks
for background being tall and narrow, and separated from the banana peaks by deep
valleys (Fig. 2.12b).
Ideally a segmentation process should be fully automatic so that it can provide
fully objective and consistent data. Several methods have been proposed for
automatic threshold determination. An interesting alternative for that is the Otsu
method, which selects the threshold based on the minimization of the within-group
variance of the two groups of pixels separated by the thresholding operator. So, if
the histogram is bimodal, the thresholding problem is to determine the best thresh-
old t separating the two modes of the histogram from each other (as shown in
Fig. 2.12). Each threshold t determines a variance for the group of values that are
less than or equal to t (t) and a variance for the group of values greater than t (t).
The definition for the best threshold suggested by Otsu is that threshold for which
the weighted sum of within-group variances is minimized. The weights are the
probabilities of the respective groups (Shapiro and Stockman 2001). The applica-
tion of the Otsu method for segmenting the same images of bananas in ripening
stages 1 and 5 (shown in Fig. 2.12) gave threshold values of 56 and 63, respectively.
Removal of the background should be fairly straight-forward but removing
non-useful sub-regions of an object can be far more difficult. Difficulty in segmen-
tation can vary with each task. Figure 2.13 compares the results of segmenting
pores and cellular material of apple tissue from X-ray computed tomography
(CT) images using a global thresholding and a combination with the kriging
based segmentation method (Oh and Lindquist 1999). For automatic segmentation
of apple pores from a stack of CT images, a common practice in using a simple
global threshold is to choice a threshold value that would match a predetermined
bulk measurement of porosity (e.g., around 28 % for ‘Jonagold’ apples). However,
this procedure is very subjective and may lead to biases when one is trying to
segment and extract reproducible quantitative information from multiple images
(e.g., for binary 3D reconstructions from stacks of CT images). The distinction
between the void and solid phases in radiographic images is frequently not sharp
(i.e., do not show a bimodal distribution) due to the amount of peak overlapping in

the attenuation coefficient histogram and the nature of X-ray tomography (where
processing and analysis are based on voxels instead of pixels). Moreover, the
resulting binary image of apple tissue using global thresholds of 60 is noisy
(Fig. 2.13b), and the average porosity is highly dependent on the selected thres-
hold value. Figure 2.13e plots the distribution of the transmitted X-ray intensities
(solid line, right axis) measured for the reconstructed tomography images of apple
tissue, and it also shows the typical dependence of the porosity (open circles,
left axis) of the resulting segmented image when a simple global threshold
is chosen.
Grayscale intensities
0 25 50 75 100 125 150 175 200 225 250
0
2000
4000
6000
8000
10000
12000
14000
16000
Number
of
pixels
Stage 1
Stage 5
banana
background
60
Segmented RGB image
Binary image
Threshold = 60
Ripening stage 5
Gray scale image
Ripening stage 1
a
b
Fig. 2.12 Image segmentation process for bananas using a simple global thresholding:
(a) pre-processing of a color image of bananas in ripening stages 1 and 5, including previous
grayscale transformation and image smoothing using a Laplacian-of-Gaussian filter (LOG filter)
[3 3] for easing the edge detection, binarization, and segmentation; (b) histogram of the
grayscale images for both bananas showing the chosen threshold value of 60

Alternatively, the thresholding algorithm developed by Oh and Lindquist (1999)
is a non-parametric formulation able to analyze regions of uncertainty based on the
estimation of the spatial covariance of the image in conjunction with indicator
kriging to determine object edges (Mardia and Hainsworth 1988). Use of indicator
kriging makes the thresholding local based on two threshold values, T0 and T1, and
guarantees smoothness in the threshold surface. Implementation of the method
requires a-priori population identification of some percentage of the image. Thus,
for the thresholding step of apple tissue images, the gray threshold values were set
at 40 and 70. According to this thresholding window, pores were identified as
those voxels with gray values less than 40, and voxels of gray value greater than
70 were classified as cellular material (in general, non-edge). Finally, the remaining
voxels of the population were identified by indicator kriging (Fig. 2.13c).
0
10
20
30
40
50
60
70
80
90
100
X-ray intensities
Porosity
(%)
0 50 100 150 200 250
10e-6
10e-5
10e-4
0.001
0.01
0.1
Probability
70
40
Porosity
Histogram
a b c d
e
Fig. 2.13 Image segmentation process for apple tissue images using a simple global thresholding
and krigking based segmentation methods: (a) original grayscale image; (b) segmented image
using a simple global threshold of 60; (c) segmented image after using a thresholding window T0
(¼40) and T1 (¼70) and indicator krigking for edge detection; (d) segmented image after cleaning
(c) by erosion and dilation operations; and (e) the distribution of the transmitted X-ray intensities
(solid line, right axis) measured for the tomography images of apple tissue, and the typical
dependence of the porosity (open circles, left axis) of the resulting segmented image when a
simple global threshold is chosen (Reproduced from Mendoza et al. 2007b with permission from
Springer-Verlag)

Small disconnected void or solid phase components which may be either physical
or arise from segmentation error, can be cleaned up from each image using a
morphological procedure called opening which involves a simple step of erosion
followed by dilation (Fig. 2.13d).
2.6 Quantitative Analysis
A large part of computer vision is concerned with extracting features from images.
Techniques by which numerical measurements are extracted from images vary
considerably in technological complexity. The process of image measurement
involves an enormous reduction in the amount of data, by selecting from the
original image those objects and parameters that are important for further charac-
terization and sorting of samples. This selection and reduction is at the heart of
image analysis and measurement, and it is achieved by ignoring irrelevant
information.
In food images, the most frequently occurring features are color, texture and
morphology. These are the features that reveal information on palatability, quality
and defects. They are easy to measure but can be difficult to characterize succinctly.
After processing of the image and with the region of interest identified, the region
must be described with measurable features based on standard chemical and
physical measurements (e.g., color parameters by colorimeter or spectrophotome-
ter, firmness by penetrometer, soluble solids content by refractometer, etc.), or
empirical standards developed by experts (standard charts for color, gloss and
texture of food surfaces, and sensorial evaluations). Many image processing tech-
niques developed for 2D images can also be extended to analyzing multi-
dimensional images, such as multispectral or hyperspectral images and
tomographic images. In this section, we discuss in some detail the three main
approaches and methods used in image analysis: color analysis, image texture
analysis, and geometrical or morphological analysis, accompanied with specific
application examples in food quality inspection.
2.6.1 Color Analysis
Color is specified by the geometry and spectral distributions of three elements: the
light source, the reflectivity of the sample and the visual sensitivity of observer.
Each of these was defined by the Commission Internationale de I’Eclairage (CIE)
in 1931. CIE also defined, for the purpose of color measurement, the cone of
spectral sensitivities of an average observer and introduced several methods to
describe color objectively. The definition was aimed at stimulating the human color
perception based on a 2
field of view, a set of primaries (red, green, blue), and
color-matching functions (CIE 1986). Several standard illuminants which are

specified by their color temperatures were also defined by CIE. The most common
one is standard illuminant D65, corresponding to the radiation of a black body at
6,500
K, which is intended to represent average daylight (Hunt 1991).
2.6.1.1 Color Evaluation
To accurately specify object colors and color differences, CIE recommended two
color spaces that are less illumination-dependent, namely, CIELAB or L*a*b* and
CIELUV L*u*v* (Robertson 1976). The CIELAB space has a function of
correcting for chromatic adaptation to the white point, and is intended for object
color displays. The CIELUV space is defined in a similar manner, and the coordi-
nate (L*, u*, v*) is calculated from the Y and (u0
, v0
) of the given light stimulus and
the white point.
The color difference in the CIELAB space is calculated as the Euclidean
distance between the points in this three-dimensional space, and is given by,
ΔE*
ab ¼ ΔL*
2
þ Δa*
2
þ Δb*
2
h i1=2
ð2:45Þ
Equation 2.45 is called the CIE 1976 (L*
, a*
, b*
) color difference formula. Chroma
C*
ab and hue angle h*
ab are also calculated from (L*
, a*
, b*
) by
C*
ab ¼ a*2
þ b*2

1=2
ð2:46Þ
h*
ab ¼ tan1
b*
=a*

ð2:47Þ
Procedures for calibrated color measurements and color reproduction using
machine vision systems with color digital cameras were proposed and discussed
in Mendoza et al. (2006) for the analysis of fruits and vegetables and in
Valous et al. (2009a) for the analysis of pre-sliced hams.
2.6.1.2 Color Measurement on Curved Surfaces
Computer vision systems based on color digital cameras can quantify the color of
any food easily and quickly (Segnini et al. 1999; Papadakis et al. 2000). Different
color channels give different color information about the pixel intensity distribution
of an image, and therefore, evaluations using different color components represent
an advantage in further quality characterization and classification tasks. However,
average color results from curved and/or uneven surfaces, as they are typical in
many fresh foods, should be interpreted with caution. A sensitivity analysis of color
measurements by a common machine vision system for samples with curved
surfaces (bananas and red pepper) revealed that L*a*b* is more appropriate for

color representation of surfaces or materials illuminated by a light source. These
color profiles are less affected by the degree of curvature, shadows and glossiness of
the surfaces than the RGB and HSV color systems, and, therefore, more appropriate
for color measurement of food surfaces (Mendoza et al. 2006). An example of the
effect of the curvature on a yellow banana using the L*a*b* and HSV color scales is
illustrated in Fig. 2.14. L* and V color scales were highly sensitive to the curvature
of the banana surface, while the S scale was also surface-curvature sensitive but to
a lesser extent. For the a*, b* and H scales no or minimum color variations
were observed.
2.6.2 Texture Analysis
The meaning of the term texture in image processing is completely different from
the usual meaning of texture in foods. Image texture can be defined as the spatial
organization of intensity variations in an image at various wavelengths, such as the
visible and infrared portions of the electromagnetic spectrum (Haralick et al. 1973).
Image texture is an important aspect of images and textural features play a major
role in image analysis. In some images, it can be the defining characteristic of
regions and critical in obtaining a correct analysis (Shapiro and Stockman 2001).
These features provide summary information defined from the intensity maps of the
Fig. 2.14 Color profiles expressed in L*a*b* and HSV color scales for a yellow banana. The
profiles were constructed using the average values of the pixels in the longitudinal direction of the
segmented image and its angular positions varying from 90
to 90
(Modified from Mendoza
et al. 2006)

scene, which may be related to visual characteristics (coarseness of the texture,
regularity, presence of a privileged direction, etc.), and also to characteristics that
cannot be visually differentiated.
Texture is a property of areas, so there is no texture for a point. Texture involves
the spatial distribution of gray levels and there is a need for a signiﬁcant number of
intensity units (i.e., pixels) in a region to detect texture features. This is linked to the
fact that texture involves gray levels in a spatial neighborhood. There are many
texture analysis techniques that can be applied to images. Among the most popular
methods used for the characterization and evaluations of food surfaces and biolog-
ical structures are: First-order Statistics, Gray Level Co-occurrence and Run
Length Matrices, and Fractals.
2.6.2.1 First-Order Statistics (FOS)
Image histogram gives primarily the global description of the image. The histogram
of a gray-level image represents the relative frequency of occurrence of each gray-
level in the image. The features of FOS are commonly derived from the normalized
gray-level histogram of the digital image, which is built by counting the number of
pixels (N) with the gray value of i (I) and can be written as:
H i
ð Þ ¼ N x; y
ð ÞjI x; y
ð Þ ¼ i
h i ð2:48Þ
The histogram, H(i), is normalized using the function given below:
H
0
i
ð Þ ¼
H i
ð Þ
X
i
H i
ð Þ
ð2:49Þ
The extracted statistical features for further partial least squares analysis
included: mean of the pixel histogram (MV), variance (VA), entropy (ET), and
energy (EN), which are deﬁned as follows (Cernadas et al. 2005):
MV ¼
X
i
iH
0
i
ð Þ ð2:50Þ
VA ¼
X
i
i MV
ð Þ2
H
0
i
ð Þ ð2:51Þ
ET ¼
X
i
H
0
i
ð Þlog H
0
i
ð Þ

ð2:52Þ
EN ¼
X
i
i2
H
0
i
ð Þ ð2:53Þ

These FOS features, however, do not provide any insight about the possible textural
differences in an image, because they do not extract any information about the
relative position of pixels, and the correlation of their intensities.
2.6.2.2 Gray Level Co-occurrence Matrix (GLCM)
When a non-random spatial distribution (or more than one texture in an image) has
to be characterized, second-order statistics are more suitable to describe these types
of relationships. A GLCM is a matrix with a number of rows and a number of
columns equal to the number of gray level intensities of the image, which contains
textural features (spatial relationships) from an image obtained using second order
statistics. Each element of the gray level co-occurrence matrix P(i,j|Δx, Δy) is the
relative frequency or probability of occurrence linked to the combination of inten-
sities I(i,j) in all pixel pairs of the image located at a distance (Δx, Δy). This relative
position is deﬁned by a distance (d) and an angle (θ ¼ 0
, 45
, 90
, 135
). For a
given directional orientation and distance of the patterns, 14 textural features can
be extracted from a grayscale image using this matrix as proposed by
Haralick et al. (1973), the most common being energy, entropy, contrast, correla-
tion, local homogeneity and variance. They are computed by:
Energy ¼
X
i
X
j
Pdθ i; j
ð Þ2
ð2:54Þ
Entropy ¼
X
i
X
j
Pdθ i; j
ð Þ log Pdθ i; j
ð Þ
ð Þ ð2:55Þ
Contrast ¼
X
i
X
j
i j
ð Þ2
Pdθ i; j
ð Þ ð2:56Þ
Correlation ¼
X
i
X
j
i j
ð Þp i; j
ð Þ μi μ j
σi σ j
ð2:57Þ
Homogeneity ¼
X
i
X
j
1
1 þ i j
ð Þ2
Pdθ i; j
ð Þ ð2:58Þ
Variance ¼
X
i
X
j
i ui j
2
Pdθ i; j
ð Þ ð2:59Þ
where μi and μj are the means, and σi and σj are the standard deviations. Energy
measures the textural uniformity of the image, i.e., the repetition of pixel pairs.

Entropy measures the disorder or randomness of the image and it is an indication of
complexity within the image, i.e., more complex images have higher entropy
values. Contrast is a measure of the local variations present in the image; so higher
contrast values indicate larger local variations. Homogeneity (also called an inverse
difference moment) is inversely proportional to contrast at constant energy. Simi-
larly at constant contrast, homogeneity is inversely proportional to energy. Finally,
correlation is a measure of image linearity among pixels (Mendoza et al. 2007a).
An application of GLCM features for the characterization of the surface appearance
of a banana (Musa cavendish) during ripening is presented in Fig. 2.15.
2.6.2.3 Run Length Matrix (RLM)
First introduced by Galloway (1975), the gray-level RLM approach characterizes
texture by the gray-level run, which is a set of consecutive pixels with the same
gray-level. Run length is the number of pixels in a run. Therefore, the run length of
coarse textures will be longer than that of ﬁne textures. In RLM method, a matrix
containing the information about the run length of images is constructed in terms of
the brightness value and length of the runs (Fardet et al. 1998).
The run-length matrix P(i, j) is deﬁned by specifying direction (i.e., 0
, 45
, 90
,
135
) and then counting the occurrence of runs for each gray levels and length in
this direction. i-dimension corresponds to the gray level (bin values) and has a
0 1 2 3 7 8 9
4 5 6
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 1 2 3 4 5 6 7 8 9
Textural
image
features
Days of storage
Correlation
Contrast
(x10³)
Entropy (x10)
Homogeneity
Fig. 2.15 Image texture analysis using the gray level co-occurrence matrix (GLCM) method for
quantifying the ripening process of a banana (Musa cavendish)

length equal to the maximum gray-level (bin values), j-dimension corresponds to
the run length and has a length equal to the maximum run length (bin values). Five
features were proposed by Galloway (1975), namely, short run emphasis (SRE)
which measures the distribution of short runs, long run emphasis (LRE) which
measures the distribution of long runs, gray-level non-uniformity (GLNU) which
measures the similarity of gray-level values throughout the image, run length non-
uniformity (RLNU) which measures the similarity of the length of runs throughout
the image, and run length percentage (RLP) which measures the homogeneity and
the distribution of runs of an image in a given direction. These RLM features are
calculated as follows:
SRE ¼
1
nr
X
M
i¼1
X
N
j¼1
P i; j
ð Þ
j2
ð2:60Þ
LRE ¼
1
nr
X
M
i¼1
X
N
j¼1
P i; j
ð Þ j2
ð2:61Þ
GLNU ¼
1
nr
X
N
j¼1
X
M
i¼1
P i; j
ð Þ
!
2
ð2:62Þ
RLNU ¼
1
nr
X
M
i¼1
X
N
j¼1
P i; j
ð Þ
!
2
ð2:63Þ
RLP ¼
nr
np
ð2:64Þ
in which nr is the total number of runs and np is the number of pixels in the image,
M is the number of gray levels (bins) and N is the number of run length (bins).
2.6.2.4 Fractal Methods
A fractal describes a rough or fragmented geometric shape that can be subdivided
into parts, each of which is, at least approximately, a reduced-size copy of the
whole. This means that they are generally self-similar and independent of scale
(Mandelbrot 1983). Contrary to the classical Euclidean geometry, fractals are not
regular and may have an integer or non-integer dimension. Thus, fractal dimensions
offer a systematic approach in quantifying irregular patterns that contain an internal
structure repeated over a range of scales. Self-similarity is not visually obvious,
but there may be numerical or statistical measures that are preserved across scales.

Due to their statistical scaling invariance, natural objects may exhibit statistical
fractality (Klonowski 2000).
The methods to calculate fractal dimensions can be divided into two types:
spatial and spectral. The ﬁrst type operates in the spatial domain, while the second
type operates in the frequency domain, using the Fourier power spectrum. The two
types are uniﬁed by the principle of fractional Brownian motion (Dougherty and
Henebry 2001).
Box-Counting Method
Estimations of fractal dimension are all based on the box-counting method. The
technique allows to obtain the scaling properties of two dimensional fractal objects
(such as from binary images) by covering a measure with a range of boxes of size ε
and counting the number of boxes containing at least one pixel representing the
object under study. This means that the technique does not consider the amount of
mass (density of pixels) inside each box. Hence, in a homogeneous system, the
number N of features of a certain size ε varies as (Evertsz and Mandelbrot 1992):
N ε
ð Þ / εD0
ð2:65Þ
where the fractal dimension D0,
D0 ¼ lim
ε!0
logN ε
ð Þ
log1
ε
ð2:66Þ
can be measured by counting number N of boxes needed to cover the object under
investigation for increasing box sizes ε and estimating the slope of a log-log plot.
Figure 2.16 illustrates the scaling properties of a binary image for apple tissue.
Variogram Method
The directional fractal dimension (DFD) of grayscale images using the variogram
model is computed as follows (Kube and Pentland 1988; Mandelbrot 1983):
v d
ð Þ ¼ c da
ð2:67Þ
v d
ð Þ ¼
1
Nd
X
N d
ð Þ
y s þ d
ð Þ y s
ð Þ
½ 2
ð2:68Þ
DFD ¼ 3 0:5^
a ð2:69Þ

where v(d) is the variogram of the image, a is termed the fractal index, c is a
constant, d is the separation distance in pixels, y(s) denotes the gray-level of the
pixel at location s, and N(d) denotes the cardinality of the set of pairs of observa-
tions. The image variogram v(d) represents the variance or dispersion of the
difference between two random variables. Thus, the relationship between a and v
(d) can also be represented using a linear regression model by applying log function
to both sides of this equation to get an estimate of fractal index ^
a. Then, the
directional fractal dimension (DFD) of the image can be directly computed.
The variogram of the image and hence the fractal dimension are estimated at a
fixed image resolution level without specifying any spatial direction along which
the set of pairs of observations is constructed. This means that the image is assumed
to be isotropic. Since the fractal texture parameters of many images of foods and
biological materials do not have isotropic patterns, four variograms should be
computed along the directions d ¼ 0
, 45
, 90
, and 135
(namely horizontal, first
diagonal, vertical and second diagonal) respectively. These variograms should be
analyzed and averaged for a given pixel location s of an image, and then be used in
further analysis and image characterization.
This approach has been recently applied to the appearance characterization
and classification of commercial pork, turkey and chicken ham slices (Mendoza
et al. 2009). DFD features were extracted from the digitalized intensity images in
grayscale, and R, G, B, L*, a*, b*, H, S, and V color components were calculated for
three image resolution levels (100, 50, and 25 %). Simulation results showed that in
1024x2-1 4 4
= Lx2-k
N(e)=2k Actual N(e)
Binary X-ray apple image
(1024x1024 pixels)
1024x2-2 16 16
1024x2-3 64 64
1024x2-4 256 250
1024x2-5 1024 995
1024x2-6 4096 3084
1024x2-7 16384 7236
1024x2-8 65536 18282
1024x2-9 262144 53050
D = -1.74
0.0 0.5 1.0 1.5 2.0 2.5 3.0
0
1
2
3
4
5
log
N(ε)
log(ε)
logN(ε) = -1.7 log(ε)+ 5 .4
R2
=0.992
e
Fig. 2.16 Illustration of fractal box-counting theory applied to a binary image of fresh apple tissue
(pores are represented by white pixels)

spite of the complexity and high variability in color and texture appearance, the
modeling of ham slice images with DFD allowed the capture of differentiating
textural features between the four commercial ham types. Independent DFD
features entailed better discrimination than that using the average of four directions.
However, the DFD features revealed a high sensitivity to color channel, orientation
and image resolution for the fractal analysis. The classification accuracy using six
DFD features was 93.9 % for the training data and 82.2 % for the testing data.
Fourier Fractal Texture (FFT)
In the calculation of FFT, the 2D Fourier transform of the grayscale image is first
calculated and the 2D power spectrum is then derived. The 2D power spectrum is
reduced to a 1D radial power spectrum (direction-independent mean spectrum, i.e., the
average of all possible directional power spectra) by averaging values over increas-
ingly larger annuli for each of the radial increments. The power spectrum, P( f ),
varying with frequency f, is calculated as follows (Dougherty and Henebry 2001):
P f
ð Þ ¼ k f 12H
ð Þ
ð2:70Þ
where k is a constant and H is the Hausdorff-Besicovitch dimension. When the log
[P( f )] is plotted against log[f], a straight line can be fitted. According to the Fourier
slice theorem, the 1D Fourier transform of a parallel projection of an image along a
line with the direction h is identical to the value along the same line in the 2D
Fourier transform of the image. This means that the line through the spectrum gives
the spectral information obtained from a projection with the same orientation in the
spatial domain. FFT dimensions, Df, are calculated as a function of orientation
based on this theorem, with 24 being the frequently number of directions that the
frequency space is uniformly divided. The data of magnitude vs. frequency are
plotted in log-log scale and its slope is determined using linear least-squares
regression. Thus, Hausdorff-Besicovitch dimension H is computed from the slope
c of the straight line, c ¼ (12H). The Df dimension of the grayscale image is
related to the slope c of the log–log plot by the equation below, with H ¼ Df3,
2 Df 3 and 3 c 1 (Geraets and Van der Stelt 2000):
D f ¼
7
2
þ
c
2
ð2:71Þ
For analysis the slope and intercept for all directions can be computed from each
image and used for further image characterization and classification. This algorithm
was proposed by Russ (2005) and was modified by Valous et al. (2009b) for hams
processing.

Fractal Lacunarity (FL)
Various studies have shown that the fractal dimension alone is not a sufficient
metric for the characterization of most textures, because fractal dimension only
measures how much space is filled. A second-order fractal metric such as
Lacunarity complements fractal dimension by measuring how the data fill the
space, enabling the parsimonious analyses of textures (Tolle et al. 2008). In this
method, a square structuring element or moving window of side length b is placed
in the upper left-hand corner of the ham slice image of side length T (pixels), such
that b T. The algorithm records the number or mass m of pixels that are associated
with the image underneath the moving window. The window is then translated by
one pixel to the right and the underlying mass is again recorded. When the moving
window reaches the right-hand side of the image, it is moved back to its starting
point at the left-hand side of the image and is translated by one pixel downward.
The computation proceeds until the moving window reaches the lower right-hand
edge of the image, at which point it has explored every one of its (T b + 1)2
possible positions. Allain and Cloitre (1991) defined lacunarity Λ measured with a
square moving window of side length b on an image of side length T (pixels), such
that b T, as:
Λ ¼
σ2
μ2
þ 1 ð2:72Þ
where the ratio of σ (standard deviation) to μ (mean) changes with window size
which signifies that lacunarity depends on the scale (moving window size relative to
image size). Lacunarity may assume any value between 1 and 1. A lacunarity
value of 1 indicates that the textural content is uniformly dispersed at a given scale,
while values 6¼1 indicate non-uniformity.
The technique can be performed on binary and grayscale images. However,
computing the lacunarity for characterizing binary images would result in the
counting of every gliding box, thus the boxes would all be full and the output
would be rather uninteresting. In contrast, when the algorithm measures the average
intensity of pixels per gliding box in most cases the results of this textural analysis
is more than adequate (Krasowska et al. 2004).
Valous et al. (2009b) applied the gliding box algorithm to characterize the
texture appearance of ham slices based on segmented images of pores/defects and
fat-connective tissue. Later, Valous et al. (2010) used the same approach to
characterize ham slices based on grayscale information. Lacunarity was computed
for each value of b between bmin ¼ 2 and bmax ¼ 256 with a step size of 1 for
the 256 256 pixel intensity images. Once the computation was complete, the
lacunarity as a function of moving window b was presented as a spatial response
function with a 2D scatter plot, which illustrated the scale dependency of spatial
non-stationary in the intensity images. As shown in Fig. 2.17, lacunarity plots
revealed important textural content information that corresponds to degree of

spatial heterogeneity of intensities and level of self-similar behavior. The results of
intensity lacunarity suggested that window sizes up to 10 pixels may be adequate to
cover textural features and produce meaningful results.
2.6.3 Morphology Analysis
Once a set of regions-of-interest has been identified by image segmentation, the
individual regions (objects) or silhouettes can be described by their geometric and
shape properties. Most image analysis systems offer a number of measures related
to the size and shape and produce a numeric output suitable for further image
characterization and classification. Size and shape are common object measure-
ments for food quality evaluation; and compared with other features such as color
and image texture, they are easier to measure using image processing techniques.
Among these morphological properties are the counting of objects (such as parti-
cles, holes, granules, droplets, bubbles, among others) and defining their position,
surface roughness and orientation in the image.
Fig. 2.17 Visualization of surface of three wet-cured cooked pork ham qualities (high yield or
low quality, medium yield or intermediate quality, and low yield or premium quality ham) using
different muscle sections, percentages of brine solutions and processing regimes. (a) intensity
images of B color channel and their three-dimensional mesh plots. (b) two-dimensional log–log
scatter plot of averaged values of intensity lacunarity as a function of moving window b.
(Reproduced from Valous et al. 2010)

2.6.3.1 Particle Size
Many image analysis applications need to characterize or describe the distribution
of particle sizes on binary images. The usual way of summarizing data for a
particular system is to draw a frequency histogram of the number of particles in a
certain size class. Furthermore, since many natural samples are broadly shaped like
a normal or Gaussian distribution, this allows to apply statistical methods to
evaluate confidence parameters and make recommendations on the minimum
number of particles to analyze in order to achieve a reasonable level of statistical
significance. Another widely used method of depicting data is to calculate a
cumulative distribution, which shows what percentage of material lies above or
below a particular size (Aguilera and Stanley 1999).
In food quality evaluations, there are three commonly used features for size
measurement of an object: area, perimeter, and length and width. The most basic
measurement for size is the area, which is represented by the number of pixels
within the area and is straightforwardly determined by counting. The perimeter of
an object is the length of its boundary, and it is particularly useful for discriminating
between objects with simple and complex shapes. Area and perimeter are easily
computed from a segmented image, however the quality of the measurements is
highly dependent of the complexity of the measured objects and the efficiency of
the segmentation method. The length and width of an object can also be used to
measure the size of an object. It is necessary to locate the major axis of the object
and measure its relative length and width (Du and Sun 2004).
Although relative measurements of the percentage of pixels of selected areas on
an image could be done for various particle size evaluations, in some applications
we need to know the x and y dimensions of an image in its real dimensional units.
Hence, before making any measurements, the relationships between the size of a
pixel to a size of an object of known length (in mm for example) that is visible in the
image must be first specified. A scale calibration factor ( f ) for the x and y directions
can be determined by
f ¼
real distance mm
ð Þ
image distance pixels
ð Þ
ð2:73Þ
2.6.3.2 Shape Descriptors
Measuring size alone is sometimes insufficient to detect important but subtle
differences between samples. This is because particles or grains with similar
composition and/or structure could measure the same area or perimeter, but have
different shapes.
Frequently, the objects of one class can be distinguished from the others by their
shapes, which are physical dimensional measurements that characterize the appear-
ance of an object. Shape features can be measured independently and by combining

size measurements. Table 2.2 summarizes some of the most widely used shape
features with combinations of size measurements for food products.
Of particular interest in particle analysis is circularity, which is a good measure
of deviation from a perfect circle. However it is important to remember that it is
unlikely that one single shape descriptor will perfectly discriminate and character-
ize all applications and different combinations of shapes. Circularity has values in
the range of 0 1. A perfect circle has a circularity of 1, while a very spiky or
irregular object has a circularity value closer to 0. Circularity is sensitive to both
overall form and surface roughness.
2.7 Concluding Remarks
The field of image processing and analysis has experienced dramatic growth and its
application has become increasingly widespread in recent years. The development
of new and more effective algorithms for image processing and analysis, along with
advances in image acquisition, computer, data storage, and the Internet, has made it
possible to handle an increasingly large volume of image data. Digital image
processing has become economical in many fields of research and in industrial
applications. While each application is different or unique from the others, they are
all concerned about speed, affordability and performance or accuracy. More and
more research activities and applications in machine vision are being focused on
real-time and interactive operations, where image acquisition, processing, and
analysis and decision-making are almost carried out simultaneously or in parallel.
Table 2.2 Common shape
descriptors
Statistical parameter Calculation
Area ratio
¼
Area
Max Diameter Min Diameter
Aspect ratio
¼
Max Diameter
Min Diameter
Compactness
¼
Perimeter2
Area
Circularity
¼
4π Area
Perimeter2
Diameter range ¼ Max Diameter Min Diameter
Eccentricity
¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1
Semi Minor2
Semi Major2
s
Elongation ¼ 1 Aspect Ratio
Roundness
¼
4π Area
π Max Diameter2
Shape factor1
¼
4π Area
Perimeter2
Shape factor2
¼
Max Diameter
Area

For the past decade, we have also witnessed an exponential increase in research and
development activities in multispectral and hyperspectral imaging technology for
food and agriculture applications. While many of the basic image processing and
analysis methods described in this chapter are still useful for processing and
analyzing two or three-dimensional multispectral and hyperspectral images, it
also entails new challenges in handling these types of images. Methods and
techniques specific to processing and analyzing multispectral and hyperspectral
images are presented in Chaps. 6 and 7.
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