IRJET- Review of Detection of Brain Tumor Segmentation using MATLAB

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 11 | Nov 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1069
Review of Detection of Brain Tumor Segmentation using MATLAB
Miss Roshani S. Thombare1, Mr. Girish D. Bonde2
12nd Year M.Tech Student 2Assistant Professor, Department of Electronics and Telecommunication Engineering,
J.T.M. College of Engineering, Faizpur, India
---------------------------------------------------------------------***----------------------------------------------------------------------
Abstract - The aim of this survey is to provide an outlinefor
those who are new to the field of image processing, andalsoto
provide a reference for those searching for literature in this
application. Tumor is because of an abnormal development of
cells (tissues) inside the brain. Magnetic Resonance Imaging
(MRI), Computer Tomography (CT) imaging techniques are
used for early detection of abnormal changes in tumor tissues
or cells. Its correct detection and identification at an early
stage is the only way to get cure. Brain tumor tissues may
become malignant (cancerous) if not diagnosed at right time.
Different methodologiesareproposedby differentresearchers.
The MRI scan image considers as a high quality input for
experiments as compared to other scans. In the future, we will
develop a deep learning based automated brain tumor
detection system and will compare with the existing state of
the art techniques for better and more accurate results.
Key Words: Brain Tumor, MRI, Machine Learning
1. INTRODUCTION
Brain is the central main part of the human body that
controls theirnervoussystem.Braincontrolsmanyfunctions
like heart, breathing, talking, walking, thinking ability,
consciousness and unconsciousness balance, etc. Therefore,
it plays vital and central role of the nervous system of
humans. Brain tumor is the irregular growth of cells in
human brain. A brain tumor has two types, benign which is
noncancerous and malignant which is cancerous. Malignant
brain tumor has two categories such as primary tumor and
secondary tumor. Primary brain tumor arises in the brain
and secondary brain tumor arises in the other parts of body
and spreads to brain and affect them.
The automatic brain tumorsystemmayconsistofstepstages
as shown in Fig. 1. The image acquisition step consists of
capturing scanned images (MRI, CT, PET etc.) and some free
datasets are BraTS, IBSR or BrainWeb. The pre-processing
step composes of different techniques of digitization of
images, noise removal, image enhancement and sharpening.
Likewise, different techniques use for isolation and
separation of the region of interest in segmentationstepand
some free tools for segmentation are available at. Statistics,
structured or global features are extracted in the step of
features extraction. Finally, different kinds of machine
learning models use for classification or clustering for
grouping the affected and non-affectedpartsofthebrain and
output image display tothephysicianorexpertindiagnosing
and making final medical decision.[20]
1.1 Workflow chart
Fig-1: Flow of Classification Brain Tumor System
This review paper is providing sufficientand quick reviewof
the current state of the art techniques for brain tumor
segmentation and detection. We investigate different
techniques for segmentation of the tumor and will develop
automatic detection of brain tumor. We investigatedifferent
techniques for segmentation of the tumor and will develop
automatic detection of brain tumor [20].
The motivations of the work area:
▪ High speed to diagnose bugs
▪ High accurate results
▪ Take small period to diagnose
▪ helping medical specialist to identify and cure disease in
early stages
▪ Time and life savage
1.2 RELATED WORK
Kharrat et al.[1] have developed a methodology, where the
brain tumor has been detected from the cerebral MRI
images. The methodology includes three stages:
Enhancement, segmentation and classification. An
enhancement process has been performed to enhance the
quality of images as well as to reduce the risk of distinct
regions fusion in the segmentation stage. Also, a
mathematical morphology has been used to increase the
contrast in MRI images. Then, the MRI images have been

decomposed by applying a wavelet transform in the
segmentation process. Finally, the suspicious regions or
tumors have been extracted by using a k-means algorithm.
The feasibility and the performance of the proposed
technique have been revealed from their experimental
results on brain images.
Mishra [2] has developed an efficient system, where the
brain tumor has been diagnosed with higher accuracy using
artificial NN. After the extraction of features from MRI data
by means of the wavelet packets, an artificial NN has been
employed to find out the normal and abnormal spectra.
Normally, the benefit of wavelet packets is that it gives
richest analysiswhencompared withthewavelettransforms
and thus adding more advantages to the performance of
their proposed system. Moreover, two cancer detection
approaches have been discussed. The NN system has been
trained using the Error Back Propagation Training Learning
rule.
P. S. Mukambika et al. [3] have advocated an image
processing scheme that essentially consists of i) Pre-
processing, ii) Segmentation, iii) Feature extraction and iv)
Classification phases. In pre-processing stage, the
morphology scheme is adopted where double thresholding
approach is implemented to detach the skull image from the
MRI brain images. This current work has put forth a
comparative study between two techniques that have been
devised for tumor detection purpose.Boththeschemeshave
been briefly described here: first is based on the Level set
approach that exploit advantages of the non-parametric
distorted models having active contours that are used to
segment brain tumors from the MRI brain scans; the second
approach uses the K-means segmentation algorithm. Once
the segmentation stage terminates, decision making is
adopted in two stages: i) Feature extraction adopting DWT
(Discrete Wavelet Transform)andGrayLevel Co-occurrence
Matrix, and ii) Classification using the Support Vector
Machine (SVM). Here the dataset consisted of MRI brain
tumor cans that included T2 weighted 17 benign and 24
malignant tumor images of multiple patients. The achieved
results were as follows: SVM having Level Set approach and
K-Means segmentation scheme had classified brain scans
into normal, benign or malignant tumor categories with
94.12% and 82.35% accuracy respectively. Obviously as
evident, the Level Set methodology gave better results as
compared to its k-means segmentation counterpart.
Ketan Machhale et al. [4] have adopted an intellectual
classification systemtocategorize normal andabnormal MRI
brain scans where the scan undergoes three phases namely;
i) image pre-processing, ii) feature extraction and
subsequent iii) classification. During the pre-processing
stage, first the RGB components of the brain scans are
transformed into grey scale format.Next,theMedianFilteris
applied to de-noise the MRI scans. Finally Skull Masking
approach is used to separate non-brain tissues from MRT
brain images. Dilation and Erosion are two fundamental
morphological operations that are used for implementing
the skull masking technique. In the second stage of feature
extraction the texture features of the scan like symmetrical,
gray scale components are extracted. Finally in the
classification phase, varied machinelearningtechniques like
Support Vector Machine (SVM), K- Nearest Neighbor (KNN)
and Hybrid Classifier (SVM-KNN) have been adopted and a
comparative study among them is facilitated. The dataset
comprised 50 images and it was concluded that the Hybrid
classifier SVM-KNN scheme offeredthehighestaccuracyrate
of 98% as compared to its counterparts.
Sumitra et al. [5] in this work have presented a neural
network technique for the classificationofMRIbrainimages.
The proposed scheme encompasses 3 stages namely: i)
feature extraction, ii) dimensionality reduction and iii)
classification. The feature extractionwasimplementedusing
PCA from MRI scans and essential traits such as mean,
median, variance, correlation values of maximum and
minimum intensity were extracted. The classifier in the
classification stage was based on back propagation and
neural networks have been developed. This classifier
classified the scan as either normal, benign and malignant.
The result reveals that the BPN classifier gave fast and
accurate classification as compared to any other neural
network counterparts. The classification accuracywas73%.
Its future work might improve the performance of devised
scheme by expanding the data set.
Researchers Nandagopal et al. [6], in their work have
presented a combined approach of wavelet statistical
features (WST) and wavelet co-occurrence texture feature
(WCT) that has been attained from two level discrete
wavelet transform which is eventuallyusedforclassification
of abnormal brain scans into benign and malignant
categories. The designed scheme encompasses four phases:
i) segmentation of region under investigation, ii) discrete
wavelet decomposition, iii) feature extraction and feature
selection and iv) classification and evaluation. The support
vector machine (SVM) approach is exploited for conducting
brain tumor segmentation. For feature extraction of tumor
zone, a merged approach of WST and WCT is employed that
has been primarily extracted fromtwolevel discretewavelet
transform. Genetic algorithm was utilized to select set of
optimal texture features from among extracted feature set.
The probabilistic neural network (PNN) was adopted for
classifying aberrant brain tissues into benign and malignant
variants and finally the performance evaluation was
facilitated by conducting a comparative study between the
PNN with its other variants. The attained accuracy was
97.5%.However one fundamental limitation of this scheme
lies with the requirement of new training for Gaussian SVM
classifier whenever a change is encountered in image data
set and this method can only be applied to CT images.Future
work scope of this devised methodology might be extended
to other types of imaging such as liver CT imaging, MRI
imaging, ultrasound imaging etc.

Trung Le et al. (2010) [7] proposed the new help vector
machine method for the two-class medical picture
classification. The principle thought of the technique is to
build an ideal hypersphere with the end goal that both the
inside edge between the surface of this circle, the typical
information, and the outside edge between this surface and
the anomalous information are as substantial as could be
expected under the circumstances. The proposed strategyis
executed effortlessly and can diminishboththefalsepositive
and furthermore false negative mistake rates to acquire
great order comes about.TheSupportVectorMachine(SVM)
classifier is a decent classifier that functions admirably on
the extensive variety of order issues, even issues in the high
measurements and the cases that are not straightly distinct.
Maybe the most concerning issue with the support vector
approach is in decision of the piece.
2.PREPROCESSINGANDSEGMENTATIONMETHODS
Preprocessing and enhancement techniques are used to
improve the detection of the suspicious region from
Magnetic Resonance Image (MRI).This section presents the
gradient-based image enhancement method for brain MR
images which is based on the first derivative and local
statistics. 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. [21]Second, the removal of high
frequency components using weighted median filtering
technique. It gives highresolutionMRIcomparethanmedian
filter, Adaptive filter and spatial filter. The performance of
the proposed method is also evaluated by means of peak
single-to noise-ratio (PSNR), Average Signal-to-Noise Ratio
(ASNR).[22]
Image segmentation is the process of partitioning a digital
image into multiple segments. Image Segmentation is
typically used to locate objects and boundaries in image,
image segmentation is the process of assigning a label to
every pixel in an image such that pixels with the same label
share certain visual characteristics. [23]
Table -1: Various Image Segmentation Techniques
Various
Techniques
Advantages Disadvantages
Active
contour
method
• Use active contour
Models.
• Preserves global line
shapes efficiently.
•Should find
strong image
gradients to drive
the contour.
•Lackingaccuracy
with weak image
boundaries and
image noise.
Watersheds
method
•Based on
mathematical
morphology
•Over
segmentation
• Helps to improve the
capture range
Threshold
method
• Try to find edge
pixels while eliminate
the noise influence.
•Use gradient
magnitude to find the
potential edge pixels.
•The detected
edges are
consisted of
discrete pixels
and may be
Incomplete or
discontinuous.
• Computationally
Expensive
Seed region
growing
• Correctly separate
the regions that have
the same properties
• Determine the seed
points
•It requires
manual
interaction to
obtain seed point
Marker
based
Watershed
• It remove the over
segmentation
problem, which occur
in watershed
segmentation
3. CONCLUSION
Research in the field of medical imaging in recent years a
great effort has been focused on segmentation of brain
tumors. In this paper,wehaveproposeddifferenttechniques
to detect and segment Brain tumor from MRI images. To
extract and segment the tumor we used differenttechniques
such as SOM Clustering, k-mean clustering, Fuzzy C-mean
technique, curvelet transform. It can be seen that detection
of Brain tumor from MRI images is donebyvariousmethods,
also in future work different automatic methods achieve
more accuracy and more efficiency.
ACKNOWLEDGEMENT
The authors would approximate to thank the reviewers for
many useful comments and suggestions which improve the
presentation of the paper.
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