BRAIN TUMOR MRIIMAGE CLASSIFICATION WITH FEATURE SELECTION AND EXTRACTION USING LINEAR DISCRIMINANT ANALYSIS

International Journal of Information Sciences and Techniques (IJIST) Vol.2, No.4, July 2012
DOI : 10.5121/ijist.2012.2413 131
BRAIN TUMOR MRI IMAGE CLASSIFICATION WITH
FEATURE SELECTION AND EXTRACTION USING
LINEAR DISCRIMINANT ANALYSIS
V.P.Gladis Pushpa Rathi1
and Dr.S.Palani2
1
Department of Computer Science and Engineering, Sudharsan Engineering College
Sathiyamangalam, Pudukkottai , India
gladispushparathi@gmail.com
2
Department of Electronics and Communication Engineering , Sudharsan Engineering
College Sathiyamangalam, Pudukkottai , India
palani_keeranur@yahoo.co.in
ABSTRACT
Feature extraction is a method of capturing visual content of an image. The feature extraction is the
process to represent raw image in its reduced form to facilitate decision making such as pattern
classification. We have tried to address the problem of classification MRI brain images by creating a
robust and more accurate classifier which can act as an expert assistant to medical practitioners. The
objective of this paper is to present a novel method of feature selection and extraction. This approach
combines the Intensity, Texture, shape based features and classifies the tumor as white matter, Gray matter,
CSF, abnormal and normal area. The experiment is performed on 140 tumor contained brain MR images
from the Internet Brain Segmentation Repository. The proposed technique has been carried out over a
larger database as compare to any previous work and is more robust and effective. PCA and Linear
Discriminant Analysis (LDA) were applied on the training sets. The Support Vector Machine (SVM)
classifier served as a comparison of nonlinear techniques Vs linear ones. PCA and LDA methods are used
to reduce the number of features used. The feature selection using the proposed technique is more
beneficial as it analyses the data according to grouping class variable and gives reduced feature set with
high classification accuracy.
KEYWORDS
Linear Discriminant Analysis, BrainTumor, Shape, Intensity, Texture, PCA, SVM, MRI
1. INTRODUCTION
Brain tumors are abnormal and uncontrolled proliferations of cells. Some originate in the brain
itself, in which case they are termed primary. Others spread to this location from somewhere else
in the body through metastasis, and are termed secondary. Primary brain tumors do not spread to
other body sites, and can be malignant or benign. Secondary brain tumors are always malignant.
Both types are potentially disabling and life threatening. Because the space inside the skull is
limited, their growth increases intracranial pressure, and may cause edema, reduced blood flow,
and displacement, with consequent degeneration, of healthy tissue that controls vital functions.
Brain tumors are, in fact, the second leading cause of cancer- related deaths in children and young
adults. According to the Central Brain Tumor Registry of the United States (CBTRUS), there

132
will be 64,530 new cases of primary brain and central nervous system tumors diagnosed by the
end of 2011. Overall more than 600,000 people currently live with the disease. [2]
Early and accurate diagnosis of brain tumor is the key for implementing successful therapy and
treatment planning. However the Diagnosis is a very challenging task due to the large variance
and complexity of tumor characterization in images, such as size, shape, location and intensities
and can only be performed by professional neuro radiologists. In the recent past several research
works have been done for the diagnosis and treatment of brain tumor. The most important
advantage of MR imaging is that it is non-invasive technique.
The use of computer technology in medical decision support is now widespread and pervasive
across a wide range of medical area such as cancer research, gastroenterology, brain tumors etc.
MRI is the viable option now for the study of tumor in soft tissues. The method clearly finds
tumor types, size and location. MRI is a magnetic field which builds up a picture and has no
known side effects related to radiation exposure. It has much higher details in soft tissues.
Researcher had proposed various features for classifying tumor in MRI. The statistical, Intensity,
Symmetry, Texture features etc, which utilize gray value of tumors are used here for classifying
the tumor. However the gray values of MRI tend to change due to over –enhancement or in the
presence of noise.[4]
In image processing, feature extraction is a special form of dimensionality reduction. When the
input data to an algorithm is too large to be processed and it is suspected to be notoriously
redundant (much data, but not much information) then the input data will be transformed into a
reduced representation set of features (also named features vector). Transforming the input data
into the set of features is called feature extraction. If the features extracted are carefully chosen it
is expected that the features set will extract the relevant information from the input data in order
to perform the desired task using this reduced representation instead of the full size input.[3]
This paper presents a novel approach for feature extraction and selection. Feature extraction
involves simplifying the amount of resources required to describe a large set of data accurately.
When performing analysis of complex data, one of the major problems stems from the number of
variables is involved. Analysis with a large number of variables generally requires a large amount
of memory and computation power or a classification algorithm which over fits the training
sample and generalizes poorly to new samples. Feature extraction is a general term for methods
of constructing combinations of the variables to get around these problems while still describing
the data with sufficient accuracy.
Feature selection is the technique of selecting a subset of relevant features for building robust
learning models by removing most irrelevant and redundant features from the data, feature
selection helps improve the performance of learning models by:
• Alleviating the effect of the curse of dimensionality.
• Enhancing generalization capability.
• Speeding up learning process.
• Improving model interpretability.
Feature selection also helps people acquire better understanding about their data by telling them
which are the important features and how they are related with each other. In the proposed
method by using PCA+ LDA, we obtain a combining process for feature reduction. The first
processing step is PCA transformation without dimension reduction, in other words, all the
eigenvalues are kept in a matrix. Then numbers of eigen values, which have highest and effective
values, are computed. The average cumulative sum of the eigenvalues, obtained from PCA, is

133
depicted against the number of eigenvalues. It shows that the sum of two largest eigenvalues has
the value of 99.99 percentages of the whole eigenvalues. This means that the third eigenvalue
will not affect the results. Therefore, we have an action of LDA in second step where feature
matrix dimensionality reduction discounts features from 15 to 2. Limiting the feature vectors by
such a combining process leads to an increase in accuracy rates and a decrease in complexity and
computational time.
This Paper is organized as follows. Section 2 describes the related works .In section 3 we
describe normalization, and feature extraction , selection and comparative analysis of PCA and
LDA In section 4 tumor classification and experimental results are discussed. The conclusions are
given in section 5.
2. RELATED WORKS
For the diagnostic process in pathology, we can discern two main steps. First pathologists observe
tissue and recognize certain histological attributes related to the degree of tumor malignancy. In a
second step interpret their histological findings and come up with a decision related to tumor
grade. In most of the cases, pathologists are unaware of precisely how many attributes have been
considered in their decision but they are able to classify tumors almost instantly and unconscious
of the complexity of the task performed.
Pathologists are capable to verbalize their impression of particular features. For example, they can
call mitosis and apoptosis as “present” or “absent” but they do not know how precisely these
concepts have to be taken into account in the decision process. To this end, although the same set
of features is recognized by different histopathologists, each one is likely to reach to a different
diagnostic output. To confine subjectivity, considerable efforts have been made based on
computer-assisted methods with a considerable high level of accuracy. It proposes data-driven
grading models such as statistical vector machines, artificial neural networks, and decision trees
coupled with image analysis techniques to incorporate quantitative histological features.
However, besides the retention and enhancement of achieved diagnostic accuracies in supporting
medical decision, one of the main objectives, is to enlarge the inter-operability and increase
transparency in decision-making. The latter is major importance in clinical practice, where a
premium is placed on the reasoning and comprehensibility of consulting systems.
A number of approaches have been used to segment and predict the grade and volume of the brain
tumor. EI papageevgious et.al (applied soft computing 2008) in their work proposed a fuzzy
cognitive map (FCM) to find the grade value of tumor. Authors used the soft computing method
of fuzzy cognitive maps to represent and model expert’s knowledge FCM grading model
achieved a diagnostic output accuracy of 90.26% & 93.22 % of brain tumors of low grade and
high grade respectively. They proposed the technique only for Characterization and accurate
determination of grade [1].
Shafab Ibrahim, Noor Elaiza in their work proposed an implementation of evaluation method
known as image mosaicing in evaluating the MRI brain abnormalities segmentation study. 57
mosaic images are formed by cutting various shapes and size of abnormalities and pasting it onto
normal brain tissue. PSO, ANFIS, FCM are used to segment the mosaic images formed.
Statistical analysis method of receiver operating characteristic (ROC) was used to calculate the
accuracy [7].

134
S.Karpagam, S.Gowri, in their work proposed detection of tumor growth by advanced diameter
technique using MRI data. To find the volume of brain tumor they proposed diameter and graph
based methods. The result shows tumor growth and volume [8].
Matthew C.clrk Lawrence et.al proposed a system that automatically segments and lables tumor
in MRI of the human brain. They proposed a system which integrates knowledge based
techniques with multispectral analysis. The results of the system generally correspond well to
ground truth, both on a per state basis and more importantly in tracking total volume during
treatment over time [5].
Carlos A.Patta, Khan IbleKharuddin and Robert, in their work suggested a enhanced
implementation of artificial neural network algorithm to perform segmentation of brain MRI data
learning vector quantization and is used for segmentation. Their result suggests excellent brain
tissue segmentation [6].
In this paper a new and improved method is implemented by combining LDA & PCA for feature
reduction and SVM is used for classification of MRI images. Compared to the previous work
suggested in the literature discussed above high accuracy is achieved for feature selection and
extraction.
3. PROPOSED METHOD
The architecture of our system is illustrated in Figure 1.The major components of our system are
Brain tumor Database, Normalisation, Feature selection, Feature extraction and Classification.
Figure 1. Architecture of proposed method

135
3.1. Data Description
Experiments are conducted on MR images collected from 20 different patients with gliomas.
Each patient has 3 sequences of MR images T1, T2 and FLAIR. Each volume contains 24 slices
in axial plain with 5 mm slice thickness. MR imaging was performed on 3.0T Siemens devices.
The imaging conditions of different protocols are; T1 weighted, T2 weighted, and Flair weighted.
The MRI image data description of the proposed method is shown in table 1.Each set of features
are individually normalized to the range of 0 to 255.
Table 1. Data Description
Attribute Description Value
Age Age in Years 17 to 83
Sex Sex Men -46, Women -52
Matrix size Size of the
matrix
192*256*192
Voxel size Size of the
voxel
0.98*0.98*1mm
Sequences MRI image
sequences
Axial 3D T1 weighted , Sagittal 3D T2
weighted , Fluid Attenuated Inversion
Recovery (FLAIR)
3.2. Normalization
Initially, these MRI images are normalized to gray level values from 0 to 1 and the features are
extracted from the normalized images. Since normalization reduces the dynamic range of the
intensity values, feature extraction is made much simpler.
3.3. Feature Extraction
Features, the characteristics of the objects of interest, if selected carefully are representative of the
maximum relevant information that the image has to offer for a complete characterization of a
lesion. Feature extraction methodologies analyse objects and images to extract the most
prominent features that are representative of the various classes of objects. Features are used as
inputs to classifiers that assign them to the class that they represent. The purpose of feature
extraction is to reduce the original data by measuring certain properties, or features, that
distinguish one input pattern from another pattern. The extracted feature should provide the
characteristics of the input type to the classifier by considering the description of the relevant
properties of the image into feature vectors. In this proposed method we extract the following
features.
Shape Features - circularity, irregularity, Area, Perimeter, Shape Index
Intensity features – Mean, Variance, Standard Variance, Median Intensity, Skewness, and
Kurtosis
Texture features –Contrast, Correlation, Entropy, Energy, Homogeneity, cluster shade,
sum of square variance.
Accordingly, 3 kinds of features are extracted, which describe the structure information of
intensity, shape, and texture. These features certainly have some redundancy, but the purpose of

136
this step is to find the potential by useful features. In the next step the feature selection will be
performed to reduce the redundancy.
3.4. Feature Selection
Feature selection (also known as subset selection) is a process commonly used in machine
learning, wherein a subset of the features available from the data is selected for application of a
learning algorithm. The best subset contains the least number of dimensions that contributes to
high accuracy; we discard the remaining, unimportant dimensions.
3.4.1. Forward Selection
This selection process starts with no variables and adds them one by one, at each step
adding the one that decreases the error the most, until any further addition does not
significantly decrease the error. We use a simple ranking based feature selection criterion,
a two –tailed t-test, which measures the significance of a difference of means between
two distributions, and therefore evaluates the discriminative power of each individual
feature in separating two classes.
Figure 2. Steps for forward selection

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The features are assumed to come from normal distributions with unknown, but equal
variances. Since the correlation among features has been completely ignored in this
feature ranking method, redundant features can be inevitably selected, which ultimately
affects the classification results. Therefore, we use this feature ranking method to select
the more discriminative feature, e.g.by applying a cut-off ratio (p value<0.1), and then
apply a feature subset selection method on the reduced feature space, as detailed below.
Figure 2 shows the procedure for forward selection
3.4.2. Backward Selection
This selection process starts with all the variables and removes them one by one, at each step
removing the one that decreases the error the most (or increases it only slightly), until any further
removal increases the error significantly. To reduce over fitting, the error referred to above is the
error on a validation set that is distinct from the training set. The support vector machine
recursive feature elimination algorithm is applied to find a subset of features that optimizes the
performance of the classifier. This algorithm determines the ranking of the features based on a
backward sequential selection method that remove one feature at a time. At each time, the
removed feature makes the variation of SVM based leave-one-out error bound smallest, compared
to removing other features
3.5. Classification
There are many possible techniques for classification of data. Principal Component Analysis
(PCA) and Linear Discriminant Analysis (LDA) are the two commonly used techniques for data
classification and dimensionality reduction. Linear Discriminant Analysis easily handles the case
where the within-class frequencies are unequal and their performance has been examined on
randomly generated test data. This method maximizes the ratio of between-class variance to the
within-class variance in any particular data set thereby guaranteeing maximal separability. The
use of Linear Discriminant Analysis for data classification is applied to classification problem in
speech recognition We decided to implement an algorithm for LDA in hopes of providing better
classification compared to Principal Components Analysis. The prime difference between LDA
and PCA is that PCA does more of feature classification and LDA does data classification. In
PCA, the shape and location of the original data sets change when transformed to a different
space whereas LDA doesn’t change the location but only tries to provide more class separability
and draw a decision region between the given classes. The classification process is divided into
the training phase and the testing phase. In the training phase known data are given. In the testing
phase, unknown data are given and the classification is performed using the classifier after
training. The accuracy of the classification depends on the efficiency of the training.
3.5.1. Principal Component Analysis
Principal components are the projection of the original features onto the eigenvectors and
correspond to the largest eigenvalues of the covariance matrix of the original feature set.
Principle components provide linear representation of the original data using the least
number of components with the mean squared error minimized
PCA can be used to approximate the original data with lower dimensional feature vectors. The
basic approach is to compute the eigenvectors of the covariance matrix of the original data, and
approximate it by a linear combination of the leading eigenvectors. By using PCA procedure, the
test image can be identified by first, projecting the image onto the eigen space to obtain the

138
corresponding set of weights, and then comparing with the set of weights of the faces in the
training set.
The problem of low-dimensional feature representation can be stated as follows:
Let X=(x1 , x 2, x 3, x 4…… x i …… x n) represent the n×N data matrix, where each xi is a face
vector of dimension n, concatenated from a p×q face image. Here n represents the total number
of pixels(p,q) in the face image and N is the number of face images in the training set . The PCA
can be considered as a linear transformation from the original image vector to a projection feature
vector, i.e
Y = WT
X (1)
where Y is the m×N feature vector matrix, m is the dimension of the feature vector, and
transformation matrix W is an n×m transformation matrix whose columns are the eigenvectors
corresponding to the m largest eigen values computed using equation(2)
λei= Sei (2)
where ei and λ are eigenvectors and eigen values of the matrix respectively. Here the total
scatter matrix S and the mean image of all samples are defined as
s=ΣN
i=1 (xi-µ) (xi-µ)T ,
µ=1/N ΣN
i=1 xi (3)
after applying the linear transformation WT
the scatter of the transformed feature vectors
{ y1,y2,…..yN} is WT
SW. In PCA , the projection Wopt is chosen to maximize the determinant
of the total scatter matrix of the projected samples, i.e.,
Wopt =arg MAX –w
| W T
SW | = [w1, w2 …..wm ] (4)
where { w,i=1,2,….m} is the set of n-dimensional eignvectors of S corresponding to the m
largest eigen values. In other words, the input vector (face) in an n-dimensional space is reduced
to a feature vector in an m- dimensional subspace. We can see that the dimension of the reduced
feature vector m is much less than the dimension of the input faces vector n.
3.5.2. Linear Discriminant Analysis
LDA methods are used in statistics, pattern recognition, and machine learning to find a linear
combination of features. LDA attempts to express 1ess one dependent variable as a linear
combination of other features or measurements. LDA is also closely related to PCA and factor
analysis in that they both look for linear combination of variables which best explain the data.
LDA explicitly attempts to model the difference between the classes of data. PCA on the other
hand does not take into account of any difference in class, and factor analysis builds the feature.
Combination is based on differences rather than similarities. LDA searches for those vectors in
the underlying space that best discriminable among classes. More formally given a number of
independent features relative to which the data is described, LDA creates a linear combination of
those which yields the largest mean differences between the desired classes. We define two
measures: 1) one is called within- class scatter matrix as given by
Sw= ( )( )T
j
i
c
j
Nj
i
j
i x
x j
1 1
j µ
µ −
−
∑∑
= =
(5)
where xi
j
is the ith
sample of class j, µj is the mean of class j, c is the number of classes, and µj is
the number of samples in class j and 2)between class scatter matrix

139
Sb= ( )( )T
c
j
µ
µ
µ
µ j
1
j −
−
∑
=
(6)
where µ represents the mean of all classes.
2.5.2. Support Vector Machine
Support vector machines are a state of the art pattern recognition technique grown up from
statistical learning theory. The basic idea of applying SVMs for solving classification problems
can be stated briefly as follows: a) Transform the input space to higher dimension feature space
through a non-linear mapping function and b) Construct the separating hyperplane with maximum
distance from the closest points of the training set.
In the case of linear separable data, the SVM tries to find among all hyper planes that minimize
the training error, the one that separates the training data with maximum distance from their
closest points
0
=
+
• b
x
w (7)
with w and b are weight and bias parameters respectively.
In order to define the maximal margin hyperplane (MMH) the following constrains must be
fulfilled:
Minimize ( ) 1
||
||
2
1 2
≥
+
• b
x
w
withy
w i
i (8)
This is a classic nonlinear optimization problem with inequality constraints. It can be solved by
the karush-kuhn-Tucker (KKT) theorem by introducing Lagrange multipliers
maximize j
T
i
j
i
j
l
j
i
i
l
i
i x
x
a
a
y
y
a ∑
∑ =
=
−
1
,
1 2
1
(9)
subject to 0
0
1
≥
=
∑
=
i
i
l
i
i anda
y
a (10)
The solution of w is:
w= i
i
l
i
i x
y
a
∑
=1
(11)
The only nonzero solutions define those training data (usually a small percentage of the initial
data set) that are necessary to form the MMH and are called support vectors. The optimal hyper
plane theory is generalized for non-linear overlapping data by the transformation of the input
vectors into a higher dimensional feature space through a mapping function
( ) [ ] f
T
n
n
n
i R
x
a
x
a
x
a
x
z
R
x ∈
Φ
Φ
Φ
=
→
∈ )
(
),.....,
(
),
( 2
2
1
1 (12)
The KKT conditions transform to
Maximize )
(
2
1
1
,
1
j
i
j
i
j
l
j
i
i
l
i
i x
x
K
a
a
y
y
a ∑
∑ =
=
− (13)
Subject to 0
0
1
≥
=
∑
=
i
l
i
i
i anda
y
a (14)

140
The optimization problem is solved using the MATLAB optimization toolbox
4. EXPERIMENT RESULTS
In all the selected 60 features, there are 22 Intensity based features, 5 Shape based features, 33
texture based features. It is found that there are 3 kinds of features extracted in our work and are
all useful for the classification. Besides, the distribution of T1, T2, and FLAIR are 10, 20,30
respectively. It means FLAIR provides the most information for tumor segmentation, T2
provides less and T1provides the least. This result is in accordance with the conclusion in
Medical Imaging that FLAIR and T2 are more sensitive in pathological discrimination than T1.
The distribution of selected features is shown in table 2.
Table 2: Distribution of Selected Features
Features T1 T2 FLAIR TOTAL
Intensity 6 5 11 22
Shape 1 1 3 5
Texture 8 5 20 33
Total 10 20 30 60
Efficiency or accuracy of the classifiers for each texture analysis method is analysed based on the
error rate. This error rate can be described by the terms true and false positive and true and false
negative as follows:
True Positive (TP): The test result is positive in the presence of the clinical abnormality.
True Negative (TN): The test result is negative in the absence of the clinical abnormality.
False Positive (FP): The test result is positive in the absence of the clinical abnormality.
False Negative (FN): The test result is negative in the presence of the clinical abnormality
FP= false positive pixels number /tumor size (15)
FN=false negative pixel number / tumor size (16)
Correct rate=FP+FN (17)
Figure 3 shows the result of pre-processed image details original, blurred, edge detection and
segmented images . The average correct rate by the method presented is 97.82% with FP of
1.0% and FN of 2.50%. All the features produce classification accuracy of 98.87% using LDA.
The extracted four PCA components are classified using LDA and SVM classification and the
accuracy achieved is 96%. . The overall accuracy percentage details are shown in fig 4. The
comparative analysis of the proposed method and the existing algorithms are shown in table 3.
Comparative analysis of the proposed method and the existing systems are shown in figure 5.

141
(a) (b)
(c ) (d)
(e )
Fig: 3 Pre-processing results a) Original image b) blurred image c) edge detection d)
Segmentation e) normalization and all process

142
Fig: 4 overall accuracy performance of the proposed method
Table 3.Comparative analysis
Classification accuracy FP FN Correct rate With FS Without FS
Proposed method 1.00% 2.50% 97.82% 98.87% 98.77%
KNN 2.75% 7.51% 93.50% 98.48% 95.47%
Fuzzy connectedness 2.95% 5.02% 92.04% 98.35% 97.47%
AdaBoost 3.15% 6.07% 90.05% 98.74% 98.55%
Fig: 5 Comparative analysis of existing algorithms and the proposed method
In this proposed system we used SVM for classification . Here we use two steps for
classification one is SVM without continuous training another one is SVM with
continuous training. The corresponding outputs are shown in figure 6 and figure 7 .Using

143
this process we can easily identify the classification process and the accuracy. Continuous
training gives more identification of the similar properties.
Fig 6 SVM without continuous training: Random weights, Random data and Sepeable
data

144
(a) (b)
( C ) ( d)
Fig 7. SVM with continuous training: Random weights, Random data and Seperable data
5. CONCLUSIONS
Brain Tumor MRI image Classification with feature selection and extraction have been carried
out in the past with limited successs. The method suggested in this paper for the above work
includes the steps, Image collection, Normalization, Intensity, shape and Texture feature
extraction, feature selection and classification. In this method the shape, Intensity and Texture
features are extracted and used for classification. Vital features are selected using LDA. The
results are compared with PCA dimension reduction techniques. The number of features selected
or features extracted by PCA and the classification accuracy by SVM is 98.87%. In this method
we train the system by both continuous and without continuous data. So we minimize the error
rate as well as increase the classification accuracy. Thus the proposed method performs better

145
than the existing works. It is expected that the information of new imaging technique fMRI and
the Image MOMENTS when added into the scheme will give more accurate results.
ACKNOWLEDGEMENTS
The work done by V.P.Gladis Pushpa Rathi, Dr. S.Palani is supported by Sudharsan Engineering
College Sathiyamangalam, Pudukkottai, India
REFERENCES
[1] E.I papageorgiou, P.P Spyridonos, “ Brain Tumor characterization using the soft computing
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[3] M.Egmont –Petersen, D.de ridder ,” Image processing with neural networks- a
[4] V.P.GladisPushparathi, S.Palani, “Linear Discriminant analysis for brain tumor
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[5] Matthew C.Clark, Lawrence O.Hall ,” Automatic tumor segmentation using Knowledge
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MRI data”, Proc.The world congress of Engineering 2011 Vol I WEC 2011, London, U.K
[9] V.P.GladisPushparathi, S.Palani, “A novel approach for feature extraction and Selection on
MRI images for brain tumor classification “,Proc, CCSEA, SEA,CLOUD, DKMP, CS & IT-
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[10] Dr.S.Palani, V.P.GladisPushpaRathi,” Detection and characterization of braintumor using
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Authors
V.P.GladisPushpaRathi graduated from Cape Institute of Technow doing her Ph.D in
AnnaUniversity,Trichy. She is a faculty member of the department of Computer Science
and Engineering, Sudharsan Engineering College. She has 5 years of teaching
experience. Her field of Interest includes Digital Image Processing, Soft computing, and
Datamining.
Dr.S Palani graduated from P.S.G. College of Technology, Coimbatore, did his post
graduate studies at IIT Kharagpur and the doctoral degree from Regional Engineering
College Trichy. He is a faculty member of the department of Electrical and Electronics
Engineering, Sudharsan Engineering College, Pudukkottai, India. He has more than 40
years of teaching experience. His field of Interest includes Control Systems, Electrical
Engineering and Digital signal Processing.

BRAIN TUMOR MRIIMAGE CLASSIFICATION WITH FEATURE SELECTION AND EXTRACTION USING LINEAR DISCRIMINANT ANALYSIS

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BRAIN TUMOR MRIIMAGE CLASSIFICATION WITH FEATURE SELECTION AND EXTRACTION USING LINEAR DISCRIMINANT ANALYSIS