1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 6, June (2014), pp. 27-33 © IAEME
27
ARTIFICIAL NEURAL NETWORK BASED MR BRAIN IMAGE
SEGMENTATION IN THREE NEURODEGENERATIVE DISEASES
Geenu Paul
Assistant Professor, Department of ECE, St Thomas Institute for Science and Technology,
Thiruvananthapuram, Kerala, India
Tinu Varghese
Research Scholar, Noorul Islam University, Kumara coil, Thuckalay, Tamilnadu
Dr. K.V. Purushothaman
HOD, Department of ECE, Heera College of Engineering and Technology, Thiruvananthapuram,
Kerala, India
N. Albert Singh
Professor, Noorul Islam University, Kumara coil, Thuckalay, Tamilnadu
ABSTRACT
Brain tissue segmentation of MRI helps in the possibility of improved clinical decision
making and diagnosis, and it also gives a new insight into the mechanism of the disease. Manual
interaction is time consuming and it may be bias and variable. This automated scheme is done on
different degenerative diseases, including Alzheimer’s disease (AD), Parkinson’s Diseases (PD) and
Epilepsy (EP). On studying the segmented image, the reduction in the GM in the brain image
indicates the presence of degenerative disease. Segmentation procedure was done with real time data.
Automated segmented images were analyzed by the treating physician, by manually segmenting it.
Accuracy, Sensitivity, Specificity and Youden index can be improved to a large extend by using the
ANN technique. In this proposed study, we have investigated the classification from AD, PD and EP
with ANN technique. Comparison of the results with three different study groups displays the
promise of our approach. The contribution of this work is an approach for automatically segmenting
the brain tissues into White Matter (WM), Gray Matter (GM), Cerebro-Spinal Fluid (CSF). The
highest accuracy rates for the classification of AD were 96.13% and the classification accuracy of
PD was 93.26% and classification of EP was 91.33% respectively. The algorithm developed achieves
INTERNATIONAL JOURNAL OF ADVANCED RESEARCH
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ISSN 0976 - 6480 (Print)
ISSN 0976 - 6499 (Online)
Volume 5, Issue 6, June (2014), pp. 27-33
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2. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 6, June (2014), pp. 27-33 © IAEME
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better performance comparable to the expert segmentation. However, in the clinical analysis accurate
segmentation of MR image is very important and crucial for the early diagnosis.
Keywords: Gray Matter, White Matter, Cerebro Spinal Fluid, Magnetic Resonance Imaging,
Artificial Neural Network.
1. INTRODUCTION
Segmentation of MRI has important implications for the study and treatment of degenerative
diseases. Segmentation of the brain is useful in determining the progress of various diseases, such as
Alzheimer’s disease, epilepsy, multiple sclerosis, etc [1]. Classifying the brain voxels into one of the
main tissue type: grey matter (GM), white matter (WM), and Cerebro spinal fluid (CSF) and the
background can be done by an automated MRI segmentation system. The segmentation helps to
diagnose the disease and help in starting early treatment for patients [2].
Identifying brain structural changes from Magnetic Resonance (MR) images can make
possible early diagnosis and treatment of neurological and psychiatric diseases. Many existing
methods require an accurate diagnosis, which is difficult to accomplish and therefore prevents them
from achieving high accuracy. We develop a Artificial Neural Network (ANN) approach to detect
brain structural changes as potential biomarkers. The proposed study using an ANN classifier is
obtaining better prediction accuracy in all the neurodegenerative diseases. We apply this study to 3D
MR images of Alzheimer’s disease, Parkinson’s disease and PD. The ANN techniques identify the
disease-specific brain regions and comparing the highly predictive regions in each disease. Thus, this
approach will be a shows potential tool for assisting the automatic diagnosis and progress mechanism
studies of neurological and psychiatric diseases.
The ANN to discriminate AD patients from PD and EP based on MRI techniques. There are
different ways to extract features from MRI for ANN classification: based on Gabor filter. The aim
of the present research is to propose an effective automated method for the segmentation using ANN
technique to classify them as GM, WM, and CSF, In this method, skull stripping [3, 4] is done in the
pre-processing section and then feature extraction is done prior to the implementation of
segmentation using the ANN technique for classification of brain tissues as GM, WM, and CSF.
2. LITERATURE REVIEW
There are many existing methods based on the segmentation of MR Images some of them are,
A Maximum Likelihood (ML) or Maximum A Posterior (MAP) approach, Expectation-
Maximization (EM) algorithm, Mean-shift algorithm, Fuzzy rule-based scheme called the rule-based
neighborhood enhancement, Penalized FCM (PFCM) algorithm [5]. A Maximum Likelihood or
Maximum a Posteriori approach and the Expectation- Maximization algorithm approach is used in
the optimization process [6] where the statistical model parameters are usually estimated. In
considering the mean-shift algorithm approach the key points include the fact that no initial clusters
are required and that the number of distinct tissue clusters is estimated from the data. A fuzzy rule-
based scheme called the rule-based neighborhood enhancement system was developed to impose
spatial continuity by post-processing of the clustering results obtained using the FCM algorithm [7].
3. MATERIALS AND METHODS
3.1 Subjects
The proposed MSVM classifier is tested on real MR image datasets collected from TIMER,
Trivandrum, and Kerala, India for three neurorelated diseases. We use the real MR datasets for

3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 6, June (2014), pp. 27-33 © IAEME
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Alzheimer’s disease (n=10), Parkinson’s disease (n=10), and Epilepsy (n=10). Each dataset contains
10 subjects aged 45-65. All images are acquired on a 1.5 Tesla Siemens Magneto – Avanto, SQ MRI
scanner.
3.2 MR Acquisition
T1 weighted axial view of the DICOM MR Images was used as test images. MRI scanning
was performed on a 1.5 Tesla Siemens Magneto – Avanto, SQ MRI scanner. In all subjects, MR
images of the entire brain were obtained using a three dimensional T1 weighted, spin echo sequence
with the standard parameters. (TR=11msec, TE=4. 95, flip angle=150, slice thickness=1mm and
matrix size =256x256). Images including flash 3D sequence were taken.
3.3 System Model & Problem Statement
A proposed system and its block diagram are shown below in Figure 1. It consists of MR
Images that are fed as input images, then pre-processing, feature extraction, clustering by
implementing Artificial Neural Network are done, so the segmented output as WM, GM and CSF is
obtained. , then analysis of WM, GM and CSF are done with that of the experts.
3.3.1 Pre-processing
These test images were undergone through the process called Pre-processing. The process of
removing these non cortical tissues is called skull stripping [4]. The skull removed tissues of the MR
image is used for further classification of brain tissues into White matter, Grey matter and
cerebrospinal fluid.
3.3.2 Feature Extraction
One of the fundamental principles of conventional image segmentation is the use of attribute
characteristics of text, image, and background objects [8]. Here we are using Gabor filter for the
extraction of features [9-10]. Totally we are extracting 24 features.
The principles rest on the observation that image pixel colors are lighter than those of
background in gray scale level weighted images show a characteristic pattern of values for the brain
tissues. Highest intensity values correspond to the WM [11].
Figure 1: Block diagram of proposed system
The pixels with slightly lesser values than WM represents GM. The pixels with lowest values
represent CSF. The skull removed tissues of the MR image is used for further classification of brain

4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 6, June (2014), pp. 27-33 © IAEME
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tissues into White matter, Grey matter and cerebrospinal fluid [12]. This can be differentiated using
the primary colours such as Red, Green and Blue.
3.3.3 Neural Network Based MRI Segmentation
Artificial neural networks can be thought of as a model which approximates a function of
multiple continuous inputs and outputs. The network consists of a group of neurons, each of which
computes a function (called an activation function) of the inputs carried on the in-edges and sends
the output on its out-edges. The inputs and outputs are weighed by weights wij and shifted by a bias
factor specific to each neuron [13-15]. It has been shown that for certain neural network topologies,
any continuous function can be accurately approximated by some set of weights and biases [16].
Therefore, we would like to have an algorithm which, when given a function f, learns a set of
weights and biases which accurately approximate the function. For feed-forward neural
networks (artificial neural networks where the topology graph does not contain any directed cycles),
the back-propagation algorithm described later does exactly that [17-18].
The Fig.2 shows the basic principle of our proposed ANN structure. The proposed ANN was
designed with 100 hidden layer (Nh) neurons, with learning rate (Lr) is, 0.01 and momentum
constant (µc) is 0. 9. From the image Gabor features are extracted for 24 orientations and were given
to the input layer of ANN.
Fig.2: Basic principle of proposed system structure
A three layer Neural Network was created with 24 nodes or neurons in the first (input) layer,
the number of input nodes in the network is equal to the number of features, 100 neurons in the
hidden layer and one output neuron in the output layer. In this case, a tan sigmoid transfer function
was used in the hidden layer, and a linear function was used in the output layer.
4. EXPERIMENTAL RESULTS AND DISCUSSION
The algorithm was implemented using MATALAB 7.7 tool boxes such as image processing
tool box and neural network tool box. To demonstrate the extensive applicability of our proposed
method to the neurological and psychiatric diseases, we have applied the proposed approach to three
diseases: AD, PD and EP [19]. To quantify the results we measured the accuracy, the ratio of the
number of test volumes correctly classified to the total of tested volumes. An ROC curve describes
how the true positive rate and false positive rate change as the threshold of the classifier changes.

5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 6, June (2014), pp. 27-33 © IAEME
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Neuroimaging studies in patients with AD/PD/EP can be quantities by ANN methods.
Segmentation into different tissue types using pixel intensity–based Gabor features and identify the
brain structural changes in each study groups. The performance of a supervised ANN segmentation
method, which has been applied on real brain MR datasets, has been presented in Fig.3. ANN
clustering, segments all the brain tissues into GM, WM and CSF. On observing the segmented output
we can observe the degeneration of the area of WM, GM and CSF.
Fig. 3: Segmentation results of AD, EP and PD patients using ANN classification method
The Fig.3 represents the segmentation results AD, PD and EP patients. The original image
skull is striped as shown in Fig.3(a). The skull striped image features as extracted using Gabor filter
and segmented in to GM, WM and CSF using our proposed ANN classification algorithm. The
Performance measures of ANN Classifiers in AD, PD and EP study groups are represented in
Table.1
Table 1: Performance measures of ANN Classifiers in AD, PD and EP study groups
In summary, our proposed ANN approach represents a significant advancement in analyzing
structural changes in the brain images and segmentation of GM, WM and CSF. All the disease
groups have significant reduction in the GM and enlargement of ventricle in the comparison of mild
to moderate stages. The comparison of our proposed ANN will be a promising tool for assisting
Classifier
Accuracy (%) Sensitivity
(%)
Specificity
(%)
Youden
Index(%)
GM WM CSF Over all
ANN
AD 96.5 96 95.9 96.13 93.78 96.97 93.34
PD 93.21 93.56 93.01 93.26 91.42 93.53 91.12
EP 91.71 91.32 90.98 91.33 89.9 91.43 90.12

6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
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automatic diagnosis and advancing mechanism studies of neuro-logical and psychiatric diseases
based on the ground truth images. The AD patients have larger accuracy (96.13%) compared to
PD(93.26%) or EP(91.33%).
5. CONCLUSION
This paper demonstrates the applicability of supervised learning approach for solving AD,
PD and prediction problem. The different study groups’ prediction task is modeled as classification
problem and solved using a powerful supervised learning algorithm, ANN. The performance of ANN
based automated segmentation prediction models is evaluated and the comparison results are
analyzed. The results indicate that the ANN machine with AD gives the high prediction accuracy
compared to other study groups. The outcome of the experiments indicates that ANN models are
capable of maintaining the stability of predictive accuracy.
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