Multi Stage Classification and Segmentation of Brain Tumor Images Based on Statistical Feature Extraction Technique

International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169
Volume: 5 Issue: 11 109 – 115
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IJRITCC | November 2017, Available @ http://www.ijritcc.org
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Multi Stage Classification and Segmentation of Brain Tumor Images Based on
Statistical Feature Extraction Technique
1
T. Nalini, 2
Dr. A. Shaik Abdul Khadir
Research scholar, P.G and Research Department of CS, Khatharmohideen College, Athirampattinam, Thanjavur(Dist),
Tamilnadu, India1
Associate Professor, P.G and Research Department of CS, Khatharmohideen College, Athirampattinam,Thanjavur(Dist),
Tamilnadu, India 2
Abstract: Automatic classification of brain images has a censorious act in calm down the burden of manual characterize and developing power
of brain tumor diagnosis. In this paper, Stanchion Vector Machine (SVM) method has been employed to perform classification of brain tumor
images into their variety and grades. Chiefly the target is on four brain tumor categories-Normal, Glioma, Meningioma, Metastasis and the four
grades of Astrocytomas, which is a conventional section of Glioma. We consult segmentation of glioma tumors, which have a large deviation in
size, pattern and appearance inheritance. In this paper images are enlarged and normalized to same range in a pre-functioning stride.The enlarged
images are then segmented positioned on their intensities applying 3D super-voxels. This effort analyze the SVM classifier applying variance
statistical feature set the final analysis shows that for brain tumor categories and grades classification. The analyses are repeated for variance
SVM categories, kernel categories and gamma points of kernel section. Analysis on the misclassification is implemented for each feature set
applying specificity and sensitivity measures. At the end of this effort, we inferred that the Statistical feature Extraction(SFE) method is
classifying the brain tumor categories satisfactorily but comparatively lacks in tumor grade classification. Classifying the brain tumorcan
collection their material in the cloud, the cloud create it attainable to admissionourmaterialin distinction to anywhere at any time.
Keywords: Brain tumors, SVM, normalization, Magnetic Resonance Imaging, 3D super-voxels,brain tumor classification
__________________________________________________*****_________________________________________________
I. INTRODUCTION:
Therapeutic science is one in the middle ofabundantranges
which has strideintocomputerizationmethod by establish a
scheme or instrument for diagnosis. The opportunity of
ancomputerizedtherapeuticfiguredeterminationinstrument,
that is excessexact than personal readers can
conceivablysupremacy to excesstrustworthy and
reproducible brain tumorsymptomaticoperations. With that
as the impieceial this job has been introduced. Brain tumors
are irregular and uncheckedconceptions of units and it is
accepted to be most lethal affliction. This year, asupposed
22,850 developed (12,900 men and 9,950 women) in the
United States apiece will be diseased with primary timorous
tumors of the brain and spinal cord [11]. Give approval to
the enumeration of Brain.org 2015, 15,320 developed (8,940
men and 6,380 women) are afflicted by diseased brain tumor
and their survival extent of time is very less [12] Glioma is
considered as a group of brain and spinal tumors that can
happen in glial units. Very large grade and low gradeare two
ordinarycharacterizations of gliomatumors. Count on
theaggressiveness of these tumors, they reside of
dissimilarpieces, distinguishing as effectivetumor, necrosis
(dead central piece), andedema (swelling). Utilizing
magnetic resonance imaging(MRI), a very large spatial
determinationaspect of brain can beexhibited. Standard
segmentation of exacttumors is timeconsuming, not
repeatable, and prone to error due to thealternative of mass,
environment, shape and attendance of thesetumors.
Therefore done segmentation of gliomatumorsis becoming a
desired instrument for the diagnosis operation.
In distinction to the World Health Organization
(WHO) report, 130 characterizations of brain tumors are
label till this extent of time. Our research jobfor cause on the
quadruplebiggercharacterizations of brain tumor and in
distinction to which one section is glioma, which happens in
glial units and it, is the most aggressive tumorsection
constituting 45% of the brain tumor [7]. Astrocytoma
actuality one most ordinarysection of glioma brain tumor,
constitutes 34% of brain tumor and is broadly categorized
under quadruple grades (Pilocytic Astrocytoma, Low-grade
Astrocytoma, Anaplastic Astrocytoma and Glioblastoma
(GBM)) [8]. This tumorinfluences both developed and
children. The developed and exactdiscovery of the section
and grade of the brain tumor can very largely influence the
life of the patient by giving the right analysis.
Therapeuticfiguredetermination has likely a direction and
way for automating the brain tumorafflictiondiscovery and
planning for analysis. In this determination, figure
acquisition section and its material plays a vital role.
In the middle ofdiffering imaging modalities
distinguishing as Magnetic Resonance Imaging (MRI),
Computed Tomography (CT), Single Photon Emission
Computed Tomography (SPECT), Magnetic Resonance
Spectroscopy (MRS) and Positron Emission Tomography
(PET), MRI is the most suitable method for brain figures as
it is very sensitive and noninvasive. MRI is acquired with
raiseddifference discrimination and in abundant planes, can
benefit to characterize the exactenvironment of a lesion
relative to key neuroanatomical structures [14]. This is
intenselysubstantial for optimum surgical and radiotherapy
planning.
With the benefit of magnetic resonance figures,
Computer Aided Diagnosis (CAD) schemes are developed
for brain tumordiscovery and its determination. In this
determination, discriminative attendance is the substantial
aspects in classification job. The dissimilarcharacterizations

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of attendance that can be excessed in distinction to MRI of
brain are first regulation statistical attendance, second
regulation statistical attendance, shape attendance and
texture attendance [16]. This job centers on evaluating the
best discriminating feature set in the middle of statistical
attendance. Initially the statistical (first regulation and
second regulation) attendance are excessed in distinction to
the clinical axial MRI obtained in distinction to patients.
The excessed attendance is likely as recommendation to the
classifier to produce the train model file, which in turn
secondhand to predict the class of unseen evidence. In
literature, dissimilarcharacterization of classification
methods is available.
As a two-class classification method, SVM is
remarkable, since it gives better performance with respect to
sparse and noisy evidence for abundant applications. SVM
is a supervised learning method secondhand for
evidencedetermination and design recognition, which
decrease computational complexity and has a faster learning
proportion. The evidencedetermination performed utilizing
SVM can be classification or regression. With kernel
capacity’s, binary SVM classifier can be extended for
solving multiclass classification problems. This job
employed SVM as the classifier, for classifying the brain
tumorcharacterization as Normal, Glioma, Meningioma,
Metastasis and brain tumor grades of Astrocytoma (section
of Glioma) as Pilocytic Astrocytoma, Lowgrade
Astrocytoma, Anaplastic Astrocytoma and Glioblastoma
(GBM). This method was repeated for dissimilar SVM
characterization (C-SVC, nu SVC, one-class SVM, epsilon-
SVR, nu-SVR), Kernel characterization (linear, polynomial,
radial basis capacity, and sigmoid, pre-computed kernel),
costs, and values of n-fold cross validation and gamma
values for kernel capacity. In distinction to the
determination, the most preferable kernel capacity’s for
section and grade classification is also inferred.
Classifying the brain tumorcan accumulation their material
in the cloud, the cloud create it attainable to
admissionourmaterialin distinction to anywhere at any
time.Benefit-Oriented Architecture benefits to use
applications as a benefit for other applications regardless the
section of vendor, product or technology. Therefore, it is
possible to exchange of evidence between applications of
dissimilar vendors without additional programming or
making changes to benefits
II. RELATED WORK
The MR personal brain figures are classified into
its distinguishinggrouputilizing supervised techniques like
artificial neuralnet jobs, support vector machine, and
unsupervised techniques like self-organization map (SOM),
fuzzy c means,utilizing the feature set as a discrimination
capacity. Othersupervised classification techniques,
distinguishing as k-nearestneighbors (k-NN) also group
pixels based on theirsimilarities in each feature [3].
Classification of MR figureseither as normal or irregular can
be done via both supervisedand unsupervised techniques
[2].Komal et al., [2] suggest a computerizationscheme
thatperforms binary classification to detect the attendance of
braintumor. The evidence set constitutes 212 brain MR
figures. It takesMR brain figures as recommendation,
performs pre-mothering, excessctstexture attendancein
distinction to segments and classification is
performedutilizing machine learning algorithms
distinguishing as Multi-LayerPerceptron (MLP) and Naive
Bayes. It has been concludedwith an accuracy of 98.6% and
91.6% respectively.
NamithaAgarwal et al., [4] suggest a method where
first and second regulation statistical attendance is
secondhand for classification of figures. In this paper,
investigations have been performed to compare texture
based attendance and wavelet-based attendance with
ordinarily secondhand classifiers for the classification of
Alzheimer’saffliction based on T2-weighted MRI brain
figure. It has been concluded that the first and second
regulation statistical attendance are significantly better than
wavelet based attendance in terms of all performance
measures distinguishing as sensitivity, accuracy, training
and testing time of classifiers. [17] Suggest the brain tumor
discovery and its section classification schemeutilizing MR
figures. In distinction to the figures, the tumor region is
segmented and then texture attendance of that region is
excessed utilizing Gray Level Co-appendence
Matrix(GLCM) like energy, difference, correlation and
homogeneity [4].
For classification, neuro-fuzzy classifier is adopted.
GladisPushpa et al., [19] suggest a methodology that
combines the intensity, texture and shape based attendance
and classifies the tumor region as white matter, Gray matter,
CSF, irregular and normal area utilizing SVM. Principle
Component Determination (PCA) and Linear Discriminant
Determination (LDA) are secondhand to reduce the number
of attendance in classification. [13] Performed a binary
classification to investigate the use of design classification
methods for distinguishing primary gliomasin distinction to
metastases, and very large grade tumor (section3 and
section4) in distinction to low grade (section2). This scheme
has a sequence of steps including ROI definition, feature
excessction, feature selection and classification. The
excessed attendance includes tumor shape and intensity
characteristics as well as rotation invariant texture
attendance. Feature subset selection is performed utilizing
Support Vector Machines (SVMs) with recursive feature
elimination. Our job is compared with this job, since both
jobs are related to tumorcharacterization and grades.
In our research job, the classification method has been
secondhand to classify brain tumorcharacterization and
grades of distinguishingtumorsectionutilizingdissimilar
levels of statistical feature excessctionmethods. For
classification, the supervised machine learningalgorithm–
Support Vector Machine (SVM) has beenemployed. In
distinction to the determination, the suitable feature set
thatdiscriminates a tumorcharacterization and grades with
improvedperformance has been label. Accuracy,
distinguishingity andsensitivity measures have been
secondhand to analyze the result ofeach section and grade
III. PROJECT DESIGN
The suggest scheme initially takes the axial MRI of
brain obtained in distinction to patients for classification and

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evaluation. Thebrain tumor section figures as well as brain
tumor grade figuresof distinguishing section are divided into
training and test evidence set. Theattendance distinguishing
as first regulation and second regulation statistical
attendanceare excesses in distinction to the training set.
Then the feature set islikely as recommendation to the SVM
classifier to produce the model file.In distinction to the
testing figures evidence set, attendance are excesses
andlikely to the produced model file to identify the section
and gradeof brain tumor at both levels.
A.Data Set
The evidence set of axial Magnetic Resonance Imaging
(MRI), are collected in distinction to the subjects of
differing brain tumor characterization and grades to perform
classification utilizing SVM.
The brain tumor characterization considered in our scheme
is Normal, Glioma, Meningioma and Metastasis as shown in
Figure.
2. The environment and size of dissimilar section of brain
tumors are clearly visible in the following Figure
Figure 1,An overall system design
Figure.2, Brain tumor types
The brain tumor grades of Astrocytoma which is
the most ordinary section of Glioma brain tumor are Grade I
– PilocyticAstrocytoma, Grade II - Low-grade Astrocytoma,
Grade III -Anaplastic Astrocytoma and Grade IV -
Glioblastoma (GBM)[5].
Figure 3,Grades of Astrocytoma.
The figures collected in distinction to dissimilar patients are
grouped into two sets for utilizing it during training and
testing stages ofthe scheme.
Table 1,Evidence set Enumeration for Brain Tumor Section
No of Figures
Brain
tumorcharacterization
Training
Figures
Testing
Figures
Total
Figures
Section 1 34 14 48
Section2 45 19 64
Section3 28 10 38
Section4 41 17 58
The evidence set (Table II) for brain tumor section
identification resides of about 208 figures out of which 70%
of figures are considered for training and 30% of figures are
secondhand as test set. The 208 brain tumor section figures
have the composition as shown in the table below
Table 2, Evidence set enumeration for brain tumorgrades of
astrocytoma
No of Figures
Brain
tumorgrades
Training
Figures
Testing
Figures
Total
Grade 1 38 16 54
Grade 2 37 20 57

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Grade 3 15 6 21
Grade 4 54 27 81
IV FEATURE ENHANCEMENT
Finally in this stage, the segmentation job is
encoded by classification via neural net job. Artificial neural
net jobs (ANNs) are powerful computational models
inspired by biological personal neural scheme. They have
been widely secondhand in real-time applications
distinguishing as differing therapeutic diagnosis issues,
thanks to their parallel architecture. In this case we label our
results into two classes, i.e. tumor core and everything else.
Figure4, Extracted features likely as input to the classifier.
The statistical figure attendance namely first
regulation attendance (mean, variance, skewness, kurtosis
and entropy) and secondregulation attendance (difference,
correlation, homogeneity and energy)are excesscted in
distinction to the figures. The first regulation attendance
iscalculated utilizing the histogram of the recommendation
figure. The secondregulation attendance is excessed in
distinction to the GLCM (Gray Level CohappenenceMatrix)
of recommendation figures.
Attendance excessed and classification of brain
tumor characterization and grades utilizing this attendance
are pictorially depicted in the Figure
V.SUPPORT VECTOR MACHINE
Support vector machine is a supervised method
secondhand to find design and perform classification and
regression determination.
Fig. 5, SVM classifier.
Likely a set of training evidence marked with class,
the SVM classifier builds a model that assigns new unseen
evidence into a group. This can be secondhand for
multiclass classification utilizing kernel tricks. Jobing - The
excessed attendance distinguishing as statistical attendance
(first regulation and second regulation) of brain tumor
section training evidence set are maintained as three
dissimilar sets distinguishing as first regulation attendance,
second regulation attendance and together as one set.
Exactly the same sets of attendance are excessed in
distinction to test figure set also. Attendance of training
figures is likely to SVM classifier sepal proportionally. The
model file produced is secondhand to classify the test figure
feature set. The accuracy is obtained in distinction to the
classifier. The algorithm is illust proportioned in Figure.
This method is repeated for dissimilar SVM characterization
(C-SVC, nu-SVC, one-class SVM, epsilon-SVR, nu-SVR),
kernel characterization (linear, polynomial, radial basis
capacity, and sigmoid, pre-computed kernel), costs, and
values of n-fold cross validation and gamma values for
kernel capacity. The confusion matrix is computed utilizing
the output file of SVM classifier. Utilizing the confusion
matrix performance measures like sensitivity and
distinguishingity are calculated as shown in Experiments
and Results section. Similar method is repeated for brain
tumor grade figure classification method.
VI.EXPERIMENTS AND RESULTS
Evidence set mainly comprising of axial MR brain
tumorfigures collected in distinction to Harvard Therapeutic
School [9],Radiopedia [10] and local scan centers. The
evidence set is dividedinto training and test set. A total of 9
attendance- 5 first regulationstatistical attendance and 4
second regulation statistical attendance - as discussed in the
previous section are excessed in distinction to bothtraining
and test set of brain tumor figures.

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The results of brain tumor characterization and
grades classification utilizing SVM with dissimilar
statistical feature set is likely in Table. For ease of
understanding the details of the SVM characterization and
kernel characterization are likely in the Table. The very
largest accuracy achieved by applying SVM utilizing first
regulation attendance apiece, second regulation attendance
apiece and both together are tabulated in Table and in
Figure. Regulation attendance performs far better when
compared to other feature sets.
Table 3,Results of Brain Tumor Characterization
and GradesClassification
Table 4.S Values and the Corresponding SVM
Characterization.
S SVM Section
0 C-SVC
1 nu-SVC
2 one-class
3 epsilon -SVR
4 Nu-SVR
Table 5,T Values and the Corresponding Kernel
Characterization.
t Kernel Section
0 Linear
1 Polynomial
2 Radial
3 Sigmoid
4 Pre-computed kernel
Table 6,Accuracy of SVM Classifier
1st
Regulation
2nd
Regulation
Both
Grade 62.31 78.26 68.1
Section 65.51 85 84.48
Figure 6, Pictorial representation of Feature Extraction Vs
Accuracy
It is observed that the accuracy
achieved utilizing first regulation attendance is very low
when compared to other two feature sets.Also in distinction
to the accuracy of utilizing both feature sets together
inclassification reveals that it results with misclassification
andreducing the performance of second regulation feature
set. The firstregulation attendance hold material of each
pixel individuallywhereas the second regulation attendance
are computed in distinction to GrayLevel Co-appendence
Matrix (GLCM) which stores theneighborhood details of
each pixel. Hence that is clearly the reason behind the raised
discrimination power of secondregulation attendance.
Interestingly it can be concluded that textureattendance
which also hold details of neighborhood design mayalso be
a good discriminatory feature set and be suitable forbrain
tumor section and grade. Since the second regulation feature
setshows significantly good performance the confusion
matrixhas been computed for it, to observe substantial
results distinguishing asidentifying the very largely
misclassified brain tumor characterization andgrades.
Confusion Matrix: is a m x m matrix where m stands for
the number of classes in the multiclass classification
problem. Here m=4 in case of both section and grade
classification.Confusion matrix of section and grade
classification for secondregulation statistical feature set is
shown in Tables IX and X.

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1, if I=J and if a class I figure is
Correctly Label to belong to class I.
C[I][J]=
0, if I J and if a class I figure is
Incorrectly Label to belong to class J.
In distinction to the confusion matrix, sensitivity
and distinguishingity parameters are calculated. The
calculation is based on the assumption that when one class is
taken as positive the other three classes are considered as
negative. This assumption holds true during
distinguishingity and sensitivity calculation for both brain
tumor characterization and grades.
The performance determination of SVM classifier
in brain tumor characterization and grades classification is
further evaluated utilizing two measures: distinguishingity
and sensitivity.
Table 7.Confusion Matrix OfSection Classification For
SecondRegulation Statistical Feature Set
ConfusionMatr
ix
Sectio
n 1
Sectio
n 2
Sectio
n 3
Sectio
n 4
Section 1 11 0 2 1
Section2 0 19 0 0
Section3 0 2 7 1
Section4 1 1 1 14
A.Distinguishingity
(Also called the true negative proportion) measures the
proportion of negatives that are correctly label [18].
B. Sensitivity
(Also called the true positive proportion, or the recall in
some ranges) measures the proportion of positives that are
correctly label [18].
Table 8.Confusion Matrix of Grade Classification for
Second Regulation Statistical Feature Set
Confusion
Matrix
Grade
1
Grade
2
Grade
3
Grade
4
Grade 1 14 0 0 2
Grade 2 1 18 0 1
Grade 3 1 0 3 2
Grade 4 4 2 2 19
But in case of sensitivity it performs worse for
grade 3 (Anaplastic Astrocytoma) classifications. This is
mainly due to persistence of excess uncertainty with respect
to grade 3 and 4 as those two classes have very little
alternative. In distinction to the distinguishingity and
sensitivity values calculated utilizing confusion matrix for
classification results of utilizing first regulation set and both
set together show that the performance of first regulation is
poor for section classificationand also it supremacy’s to
misclassification when secondhand together with second
regulation. In case of grade classification it is seen that
sensitivity is low, for grades 2, 3 and 4 classifications. Also
when bothare secondhand together misclassification is very
large with respect to grades 2 and 3. Comparing this job
with Evangelic et al. [13] it can be noticed that the binary
SVM classification accuracy,sensitivity, and
distinguishingity, assessed by leave-one-out cross
validation, were respectively 85%, 87%, and 79%
fordiscrimination of metastases in distinction togliomas, and
88%, 85%, and 96% for discrimination of very large grade
(grade III and IV) in distinction to low grade (grade II)
neoplasms. Classification is not done for either all
characterization or grades. Whereas, our job achieves an
accuracy of 85% and 78.26% for classifying all brain tumor
characterization and brain tumor grades respectively
utilizing second regulation statistical feature set.
SVM classifier speed is linear to its size [21]. So
SVM classifier for non-linear classification utilizing kernel
capacity’slike RBF produces good result when small
evidence set is employedwith very largely dimension
space[20] since its speed and memorytrade-offs are explicit
only for large evidence set of industrial scale.The decrease
in speed was observed to be minimal and thememory
required was not any larger than the desktop pc’smemory
for the considered evidence set. One most
substantialadvantage of kernel capacity method (SVM) is
that the methodenables the user to deal with over-fitting by
carefully tuningthe regularization parameters. Hence SVM
is a suitableclassifier for experimenting the classifying of
the dissimilarbrain tumor characterization and grades
utilizing small evidence set.
VII.CONCLUSION AND FUTURE WORK;
In this paper, the brain figures acquired utilizing
MRI for dissimilar tumor characterization and one
distinguishing tumor section with quadruplegrades are
classified utilizing multi-class SVM for identifying
thesuitable feature set, which improves the
classificationperformance. After the determination, we
inferred that n-SVM andc-SVM are excess suitable for
Astrocytoma grade classificationutilizing RBF kernel and c-
SVM utilizing polynomial kernel is bestfor tumor section
classification. In distinction to the job done utilizing
differing SVM-characterization, kernel characterization and
dissimilar statisticalfeature set it is clear that second
regulation attendance obtained theaccuracy of 85% for brain
tumor section and 78.26% for braintumor grade
classification which is the very largest in the middle of
theother two feature sets. In addition, the sensitivity of
grades 2,3 and 4 are very low.

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In distinction to the determination, it is clear that
the general classification methods do not show satisfactory
performance during brain tumor grade classification.
Evangelia et al. [13] job is related to tumor section and
grade classification, but it is limited to binary classification.
In [13], metastases are discriminated in distinction to glioma
and the grades are classified as either very large grade or
low grade, it actually does not classify all characterization
and grades. Hence this issue gives space for research jobs to
find and devise an excess focsecondhand and exact method
for tumor and grade classification.
Also, the inopportunity of global bench mark
evidence set for brain tumor section and grade classification
makes it difficult to compare the existing jobs. As a future
job, to improve the performance of grade classification,
semantic based techniques with knowledge base as rules can
be incorporate proportioned. A large amount of jobs have
been done in to improvise the speed and memory
requirement of SVM classifier foremploying it for large
evidence set namely by utilizing SequentialMinimal
Optimization (SMO) techniques [21] and GPU Accelerator
REFERENCES
[1] Ahmed Kharrat, KarimGasmi, Mohamed Ben
Messaoud, NacraBenamrane and Mohamed Abid,
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[2] Komal Sharma, AkwinderKaura and ShrutiGujral,
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[3] Walaa Hussein Ibrahim, Ahmed Abdel Rhman
Ahmed Osman and Yusra Ibrahim Mohamed, 2013,
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[5] De Angelis L M, 2001, “Brain Tumors New England
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Multi Stage Classification and Segmentation of Brain Tumor Images Based on Statistical Feature Extraction Technique

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Multi Stage Classification and Segmentation of Brain Tumor Images Based on Statistical Feature Extraction Technique