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  • 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 IN ENGINEERING AND TECHNOLOGY (IJARET) ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 5, Issue 6, June (2014), pp. 27-33 © IAEME: http://www.iaeme.com/IJARET.asp Journal Impact Factor (2014): 7.8273 (Calculated by GISI) www.jifactor.com IJARET © I A E M E
  • 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 28 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
  • 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 29 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 View slide
  • 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 30 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. View slide
  • 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 31 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
  • 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 32 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. REFERENCES [1] Geenu Paul, Tinu Varghese, K.V.Purushothaman, Albert Singh N, A Fuzzy C Mean clustering algorithm for automated Segmentation of Brain MRI, Springer, Advances in Intelligent and Soft Computing journal. 247, 2014, pp 59-65. [2] Tinu Varghese, Sheela Kumari R, Mathuranath.P.S, Volumetric Analysis of Regional Atrophy for the Differential Diagnosis of AD and FTD, International Journal of Computer Applications 2013, 62, 43-48 [3] Chen Yunjie, W.S., Zhang Jianwei, A new fast brain skull stripping method. In Biomedical Engineering and Informatics, an IEEE International Conference, 2009. 28(35): p. 1-5. [4] Shanhi K.J, S.M.,”Skull striing and automatic segmentation on brain MRI using seed growing and thresholding techniques. In Intelligent and advanced Computational Systems”, IEEE International Conference 2007. 28(89): p. 422-426. [5] Ahmed, M.N.—Yamany, S.M.—Mohamed, N.—Farag, A.A.—Moriarty, T.: A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data. IEEE Trans. on Medical Imaging, Vol. 21, 2002, pp. 193–199. [6] Ambroise, C.Govaert, G.: Convergence of an EM-Type Algorithm for Spatial Clustering. Pattern Recognition Letters, Vol. 19, 1998, pp. 919–927. [7] E.A. Zanaty. “Determining the number of clusters for kernelized fuzzy C-means algorithms for automatic medical image segmentation”, Egyptian Informatics Journal, 13,pp. 39–58, (2012). [8] Hammouda, K., Texture Segmentation Using Gabor Filters. University of Waterloo, Waterloo, Ontario, Canada, 2005: p. 1-8. [9] Michael Lindenbaum, R.S., Gabor Filter Analysis for Texture Segmentation. CIS-Technion: Technical Report, 2005: p. 1-58. [10] Chen Yunjie, W.S., Zhang Jianwei, A new fast brain skull stripping method. In Biomedical Engineering and Informatics, an IEEE International Conference, 2009. 28(35): p. 1-5. [11] K. Van Leemput, F. Maes, D. Vandeurmeulen, and P. Suetens, “Automated model-based tissue classification of MR images of the brain,” IEEE Trans. Med. Imag., vol. 18, no. 10, pp. 897–908, Oct. 1999. [12] Clarke, L.P., et al., MRI segmentation: methods and applications. Magn Reson Imaging, 1995. 13(3): p. 343-68 [13] A.K. Jain and J. Mao, Neural Networks and Pattern Recognition, in Computational Intelligence: Imitating Life. J.M. Zurada, R. J. Marks 11, and C.J. Robinson, eds., IEEE Press Piscataway., 1994: p. 194-212
  • 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 33 [14] Ashjaei B., S.-Z.H., “A Comparative analysis of neural network methodologies for segmentation of magnetic resonance images”. Proceedings of International Conference on Image Processing, 1996. 2 p. 257-260 [15] J Dheeba, N Albert Singh, J Amar Pratap Singh, “Breast Cancer Diagnosis: An Intelligent Detection System Using Wavelet Neural Network”, Advances in Intelligent Systems and Computing, Volume 247, 2014, pp 111-118. [16] Hagan, M.T.M., M., Training feed-foward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 1999. 5(6): p. 989-993. [17] Lisboa PJ, T.A., “The use of artificial neural networks in decision support in cancer: a systematic review”. Neural Networks, 2006. 19(4): p. 408–15. [18] J. Dheeba, G Wiselin Jiji, “Detection of Microcalcification Clusters in Mammograms using Neural Network”, International Journal of Advanced Science & Technology, 2010, Vol. 19, pp: 13-22. [19] Rahul S Desikan, Howard, J.C, “Automated MRI measures identify individuals with MCI and AD,” Brain, vol.132, pp. 2048-2057 (2009). [20] Sumesh M. S., Gopakumarc., Rejirajan Varghese and Abraham Varghese, “Level Based Normal- Abnormal Classification of MRI Brain Images”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, 2013, pp. 403 - 409, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [21] Gunwanti S. Mahajan and Kanchan S. Bhagat, “Medical Image Segmentation using Enhanced K-Means and Kernelized Fuzzy C- Means”, International Journal of Electronics and Communication Engineering &Technology (IJECET), Volume 4, Issue 6, 2013, pp. 62 - 70, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. [22] Priyanka Baruah and Dr. Anil Kumar Sharma, “Study & Analysis of Magnetic Resonance Imaging (MRI) With Compressed Sensing Techniques”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 7, 2013, pp. 130 - 138, ISSN Print: 0976-6480, ISSN Online: 0976-6499. [23] Mayur V. Tiwari and D. S. Chaudhari, “An Overview of Automatic Brain Tumor Detection from Magnetic Resonance Images”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 2, 2013, pp. 61 - 68, ISSN Print: 0976-6480, ISSN Online: 0976-6499.