IRJET- Brain Tumor Detection using Digital Image Processing

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7222
Brain Tumor Detection Using Digital Image Processing
Amit D. Mudukshiwale1, Prof. Y M. Patil2
1Student, Dept. of Electronics Engineering, KIT’s college of Engineering Kolhapur, Maharashtra, India
2Professor, Dept. of Electronics Engineering, KIT’s college of Engineering Kolhapur, Maharashtra, India
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Abstract - According to International Agency for Research
on Cancer (IARC), the rate of diagnosis of brain tumor is
estimated to be comparatively greater than the mortality
rate. Brain tumor is one of the major causes for the increase
in Mortality among people. A tumor is an abnormal growth
caused by cells reproducing themselves in an uncontrolled
manner. Brain tumors are the tenth most common cause of
cancer death. Generally, CT scan or MRI that is directed into
intracranial cavity produces a complete image of brain. This
image is visually examined by the physician for detection
and diagnosis of brain tumor. However this method of
detection resists the accurate determination of stage and
size of tumor. Most Medical Imaging Studies and detection
conducted using MRI, Positive Emission Tomography (PET)
and Computed tomography (CT) Scan.
Brain tumor diagnosis is done by doctors. For detecting
brain tumor grading always gives different conclusion
between one doctors to another. For helping doctors
diagnose brain tumor grading, we have taken this project.
This project made a software program in MATLAB which
detects the brain tumor. The algorithm incorporate steps for
preprocessing, image segmentation, feature extraction and
image classification using MRI image.
Key Words: Median filter, K-means segmentation,
Watershed segmentation, Fuzzy c means segmentation,
Morphological operators
1. INTRODUCTION
The nervous system consists of the brain, spinal cord, and
a complex network of neurons. This system is responsible
for sending, receiving, and interpreting information from
all parts of the body. The nervous system monitors and
coordinates internal organ function and responds to
changes in the external environment. The central nervous
system (CNS) is a part of nervous system. It is the
processing center for the nervous system. It receives
information from and sends information to the peripheral
nervous system. The two main organs of the CNS are the
brain and spinal Cord.
The brain processes and interprets sensory information
sent from the spinal cord. The brain is the anterior most
part of the central nervous system. Nowadays, detection of
anatomical brain structures with their exact location and
orientation has become an extremely important task in the
diagnosis of brain tumor. Detection of anatomical brain
structures plays an important role in the planning and
analysis of various treatments including radiation therapy
and surgery. Because of this, development of efficient and
accurate MRI segmentation technique has become one of
the most important areas of research today.
These days, in most of the hospitals, radiologists performs
the diagnosis of brain tumor manually on MR images, this
process is error prone, in particular because of large
number of image slices of single patient and due to the
large variation in the intensity of various images
representing different brain structures. Due to the
involvement of various kinds of abnormalities, pathology,
radiologist’s perception and image analysis. At diagnosis
stage, manual segmentation of brain tumor from MR
2. METHODOLOGY
Here fig.2.1 shows the block diagram of our brain tumor
detection system. it contains the steps such as
preprocessing , segmentation and feature extraction and
classification. For processing this different brain images
should be preprocessed to remove the film artifacts, labels
for that tracking algorithm is used. Median filter is used to
remove high frequency component also morphological
techniques are used for to remove skull portion. The
position of tumor objects is separated from other part of a
brain image by using different segmentation algorithms.
K-means clustering is a type of clustering algorithms.
This technique belongs to the category of unsupervised
learning. Region growing termed as a pixel-based image
segmentation technique which incorporates the selection
of initial seed points from the image. Fuzzy C-means is a
clustering technique which allows partial belongingness of
pixels into different clusters. The SOM is a neural network
models. It belongs to the category of competitive learning
networks based on unsupervised learning. Water-shed is
highly sensitive to local minima and a watershed is
created, if we have an image with noise, this will influence
the segmentation.

Fig 2.1: Block Diagram
2.1 IMAGE AQUISISION
Preprocessing and enhancement techniques are used to
improve the detection of the suspicious region from
Magnetic Resonance Images (MRI). From the fig 2.2 the
preprocessing and enhancement method consists of two
steps; first the removal of film artifacts such as labels and
X-ray marks are removed from the MRI using tracking
algorithm. Second the removal of high frequency
components using median filtering technique. Film
artifacts that are removed using tracking algorithm which
removes the high intensity value of film artifacts are
removed from MRI brain image. Median Filter can remove
the noise, high frequency components from MRI without
disturbing the edges.
Fig 2.2: Block Diagram of stages of preprocessing
Images of a patient obtained by MRI scan is displayed as
an array of pixels and stored in Matlab 18.Here, grayscale
or intensity images are displayed of default size 256*256
by giving a large matrix whose entries are numbers
between 0 and 255, with 0 corresponding to black, and
255 to white. The brain MR images are stored in the
database in JPEG format.
2.2 Use of median filter for de-noising
Median Filter can remove the noise, high frequency
components from MRI with-out disturbing the edges and it
is used to reduce salt and pepper noise. This technique
calculates the median of the surrounding pixels to
determine the new (de-noised) value of the pixel. A
median is calculated by sorting all pixel values by their
size, then selecting the median value as the new value for
the pixel. For each pixel, an 3*3, 5*5, 7*7, 9*9, 11*11
window of neighborhood pixels are extracted and the
median value is calculated for that window. The intensity
value of the center pixel is replaced with the median value.
This procedure is done for all the pixels in the image to
smoothen the edges of MRI.
2.3 Use of Segmentation Methods
K means segmentation is type of clustering methods use
greedy interactions with existing clusters to come up with
a good overall representation. For example, in
agglomerative clustering we repeatedly make the best
available merge. However, the methods are not explicit
about the objective function that the methods are
attempting to optimize. An alternative approach is to write
down an objective function that expresses how good a
representation is, and then build an algorithm for
obtaining the best representation. A natural objective
function can be obtained by assuming that we know there
are k clusters, where k is known. Each cluster is assumed
to have a center; we write the center of the ith cluster as ci.
The jth element to be clustered is described by a feature
vector xj. For example, if we were segmenting scattered
points, then x would be the coordinates of the points; if we
were segmenting an intensity image, x might be the
intensity at a pixel. Notice that if the allocation of points to
clusters is known, it is easy to compute the best center for
each cluster. However, there are far too many possible
allocations of points to clusters to search this space for a
minimum. Instead, we define an algorithm which iterates
through two activities: first is Assume the cluster centers
are known, and allocate each point to the closest cluster
center and second one is to assume the allocation is
known, and choose a new set of cluster centers. Each
center is the mean of the points allocated to that cluster.
We then choose a start point by randomly choosing cluster
centers, and then iterate these stages alternately. This
process will eventually converge to a local minimum of the
objective function. It is not guaranteed to converge to the
global minimum of the objective function, however. It is
also not guaranteed to produce k clusters, unless we
modify the allocation phase to ensure that each cluster has
some non-zero number of points. This algorithm is usually
referred to as k-means. It is possible to search for an
appropriate number of clusters by applying k-means for
different values of k, and comparing the results.

2.4 Fuzzy C means segmentation
FCM algorithm based on the concept of fuzzy C partition,
which was introduced by various researcher in this field
Ruspini , developed by Dunn and generalized by Bezdek.
The aim of FCM is to find cluster centers (centroids) that
minimize dissimilarity functions. In order to accommodate
the fuzzy partitioning technique, the membership matrix
(U) is randomly initialized. In the first step, the algorithm
selects the initial cluster centers from SOM clustering
algorithm. Then, in later steps after several iterations of
the algorithm, the final result converges to actual cluster
center. Therefore a good set of initial cluster is achieved
and it is very important for an FCM algorithm. If a good set
of initial cluster centers is chosen, the algorithm make less
iterations to find the actual cluster centers. The winning
neural units and their corresponding weight vectors from
each layer result in a hierarchical structure termed as an
abstraction tree. Each node in the abstraction tree
represents the region of the image at a specified level of
abstraction. A segmented image is generated on demand
by traversing the abstraction tree in the breadth first
manner starting from the root node until some criterion is
met. The size of the abstraction tree (weight vector) is
expanded if the sum of the variances of weight vector
divided by size of the weight vector is less than element of
weight vector. Otherwise the node is labeled as a closed
node and none of its descendants are visited. Regions
corresponding to the closed nodes constitute a segmented
image and the resulting segmented image usually contains
the regions from different abstraction levels.
The Fuzzy C-Means algorithm is an iterative algorithm that
finds clusters in data and which uses the concept of fuzzy
membership. Instead of assigning a pixel to a single
cluster, each pixel will have different membership values
on each cluster.
2.5 Watershed segmentation method
In this technique the MRI image is extracted using
watershed segmentation method and the exact tumor part
is classified using morphological operators like erosion
and dilation. Grey-level image may be seen as a
topographic relief, where the grey level of a pixel is
interpreted as its altitude in the relief. A drop of water
falling on a topographic relief flows along a path to finally
reach a local minimum. Intuitively, the watershed of a
relief corresponds to the limits of the adjacent catchment
basins of the drops of water. In image processing, different
watershed lines may be computed. In graphs, some may be
defined on the nodes, on the edges, or hybrid lines on both
nodes and edges. Watersheds may also be defined in the
continuous domain.
There are also many different algorithms to compute
watersheds. It is more preva-lent in the fields like
biomedical and medical image processing, and computer
vision. In topography, watershed means the edge that
partitions area drained by diverse river system. If image is
viewed as geological landscape, the watershed lines find
out boundaries which separate image regions. The
watershed transform figures catchment basins and
ridgelines (otherwise called watershed lines), where
catchment basins relating to image regions and ridgelines
identifying with region boundaries. Segmentation by
watershed embodies many of the concepts of the three
techniques such as threshold based, edge based and region
based segmentation. It gives very good segmentation
results, and meets the criteria of less computational
complexity.
2.6 Threshold Segmentation
Threshold Segmentation: Thresholding method is
frequently used for image segmentation. This is easy and
effective segmentation methodology for pictures with
completely different intensities. The technique essentially
tries for locating a threshold price that permits the
classification of pixels into completely different classes. A
major weakness of this segmentation mode is that: it
generates solely 2 categories. Therefore, this methodology
fails to touch upon multichannel pictures. Besides, it also
ignores the spatial characteristics due to which an image
becomes noise sensitive and undergoes intensity in-
homogeneity problem, which are expected to be found in
MRI. Both these features create the possibility for
corrupting the histogram of the image. For overcoming
these issues numerous versions of thresholding technique
are introduced that segments medical pictures by
exploitation the data supported native intensities and
property. Though this is a simple technique, still there are
some factors that can complicate the thresholding
operation, for example, non stationary and correlated
noise, ambient illumination, busyness of gray levels at
intervals the item and its background, inadequate
contrast, and object size not commensurate with the
scene.

3. RESULT
Table 3.1:Comparison of results
4. CONCLUSION
In this system brain tumors have been segmented with the help of 4 methods. The execution time for watershed and k
means method is less compared to the other segmentation methods. Regarding the number of tumor pixels, thresholding
and Fuzzy C-means clustering give a better result than the other methods.
REFERENCES
[1] Nilakshi Devi and Kaustubh Bhattacharyya "Automatic Brain Tumor Detection and Classification of Grades of
Astrocytoma"
[2] Shrutika Santosh Akshata Raut Swati Kulkarni et al "Implementation of Image Processing for Detection of Brain
Tumors"(Proceedings of the IEEE 2017 International Conference on Computing Methodologies and Communication
(ICCMC))
[3] S. G, Ranjith Balakrishnan “Brain Tumor Diagnosis from MRI feature Analysis A Comparative Study” (IEEE-2nd
International Conference on Innovation in Information Embedded and Communication Systems ICIIECS'15, 2015.)
[4] D. M. Joshi, Dr.N. K. Rana, &, V.M. MisraMisra,_Classi_cation of “Brain Cancer Using Artificial Neural Network”(IEEE-
2nd International Conference on Electronic Computer Technology (ICECT), 2010.)
[5] Asra Aslam, Ekram Khan, M.M. Sufyan Beg, “Improved Edge Detection Algorithm for Brain Tumor Segmentation”,
(Elsevier, Science Direct, Procedia Computer science , pp. 430-437., 2015.)
[6] Shobana G, "Brain Tumor Diagnosis From MRI Feature Analysis - A Comparative Study” (IEEE Sponsored 2nd
International Conference on Innovations in Information Embedded and Communication Systems ICIIECS'15)
[7] P. Sangeetha, "Brain Tumor Classification Using PNN and Clustering"(International Journal of Innovative Research in
Science, Engineering and Technology, 2014. Pp. 796-803.)
[8] R. Shelkar, M. N. Thakare, "Brain Tumor Detection and Segmentation by using Thresholding and Watershed
Algorithm",(IJACT, Volume 1, Issue 3, July 2014)
[9] C. Arizmendi, Alfredo Vellido, Enrique Romero, "Binary Classification of Brain Tumors Using a Discrete Wavelet
Transform and Energy Criteria", (IEEE 2011, 2011, pp. 1-4.)
method Input
image
Output
image
Area MSE PSN
R
K-Mean 0.279 3882 2.27
db
Fuzzy C
Mean
0.094
0
3072 3.28
db
Waters
hed
segmen
tation
0.060
99
6234 -89.
81
db
Theshol
ding
0.191 8871 8.65
db

[10] applied science and technology (ajast) (open access quarterly international journal) volume 2, issue 2, pages 274-276,
april-june 2018.
[11] Anujaraj V1, C B Bromy2, Johns Vinod3, Nithin Prakash4, Binsa Mathew5 “An OBD Based Heads up Display for
Automobiles” proceeding of 2017International Journal of Advanced Research in Electrical, Electronics and
Instrumentation Engineering
.

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