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    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 5, September – October (2013), pp. 232-243 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET ©IAEME APPLICATION OF ARTIFICIAL NEURAL NETWORK TOWARDS THE DETERMINATION OF PRESENCE OF DISEASE CONDITIONS IN ULTRASOUND IMAGES OF KIDNEY Karthik Kalyan1, Suvigya Jain1, Dr. Ramachandra Dattatraya Lele1, 2*, Dr. Mukund Joshi 3, Dr. Abhay Chowdhary1 1 Systems Biomedicine Division,Haffkine Institute for Training, Research and Testing (HITRT), Parel, Mumbai, India, Pincode: 400 012. 2 Nuclear Medicine Department, Jaslok Hospital and Research Centre, Pedder Road, Mumbai, India, Pincode: 400 026. 3 Ultrasound Department,Jaslok Hospital and Research Centre, Pedder Road, Mumbai, India, Pincode: 400 026. ABSTRACT Ultrasound (US) imaging modality is an important tool for diagnosis of kidney diseases such as Chronic Kidney disease, Renal Calculus and Cortical Cyst. It is easy to perform because of its non-invasive nature and lower costs. However due to the use of various ultrasound equipments, the image of ultrasound is prone to several noises such as ‘Speckle Noise’, which makes the diagnosis of disease conditions difficult for biomedical specialist such as radiologists. The accuracy of visual observation depends on the expertise of radiologists; however it is highly subjective. In order to provide more objective analysis and diagnosis, various features have been extracted from kidney ultrasound images. In this study many important features of ultrasound kidney images have been extracted such as Intensity histogram (IH) feature, Invariant moments (IM), Gray level co-occurrence matrices (GLCM), Gray level run length matrices (GLRLM) and ‘COMBINED’ feature set was develop from combination of all the four features. In total, 48 features of each image were calculated. Classification of ultrasonic kidney images is studied utilizing these extracted features by means of feature extraction methods and then optimal feature amongst them were selected by means of feature selection using a tool known as Waikato Environment for Knowledge Analysis (WEKA). Selected features from these methods are used to classify two sets of ultrasound kidney images – Normal, abnormal (cyst, Chronic Kidney disease, renal calculus). A neural network (ANN) based pattern recognition tool is employed to evaluate the performance of each feature on their classification accuracy rate. 232
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME Keywords - Artificial Neural Networks (ANNs), Kidney Ultrasound (US) Image Classification, Machine Learning Techniques, Medical Image Processing, Specific Disease Condition Applications. 1. INTRODUCTION Image processing techniques are usually applied in medical images to enhance the quality of representation of medical image and towards the better understanding of hidden information for proper objective diagnosis. By using techniques such as feature extraction, image enhancement (part of medical image processing), it is also possible to extract some parameters or features that will be very helpful for the diagnosis of the medical images [1]. Diagnostic ultrasound has gained widespread acceptance as an effective diagnostic tool for imaging organs and soft tissues in human abdominal wall [2]. Well-known facts of abdominal US imaging such as real-time, non-invasive, non-radioactive and inexpensive properties make it useful in diagnosing soft tissues [3, 4]. The gray-scale type of display is useful in the detection of abnormality. One of its important applications within abdomen is kidney imaging. Since ultrasound images suffer from multiplicative noise forms such as speckle noise, which makes the signal difficult to differentiate an organ towards its specific pathological changes [2]. When the reflected echoes from human kidney tissues are displayed as a B-scan image, they form a texture pattern because of the characteristic of both the imaging system and tissue [3, 5]. Texture is the main feature utilized in medical image processing and computer vision to characterize the surface and object identification [6]. This indicates the diagnosis of US kidney images could be achieved by means of interpretation of texture pattern [7] as it could provide some vital information that may not be inaccessible through visual interpretation of US images [6]. Quantitative evaluations included distribution of gray-level scales of pixels in image to describe tissue characteristics of kidney, liver etc. [8]. Sonographic (US) evaluations are made based on the distribution of echogenicity that reflects tissue characteristics. For better echo visualization, the longitudinal cross section of kidney is taken to include renal sinus, medulla and cortex regions as suggested by the radiologists. This ensures better visual interpretation of the normal and diseased kidney. Normal kidney has a bright area surrounding it, which is made up of Gerota’s fascia and peri-nephric fat. The periphery of the kidney will appear grainy gray, which is made up of the renal cortex and pyramids. Sometimes you can see the individual pyramids, but this is not always the case. The central area of the kidney, the renal sinus, will appear bright (echogenic) and consists of the calyces, renal pelvis and the renal sinus fat. If the echogenic patterns of the liver and kidney are the same then it is certainly considered as normal as suggested by radiologists and center of the kidney shows grey white or white in color, which indicates increased echoes. This is the hilum of the kidney also known as the central sinus. The kidney diseases are usually categorized as hereditary, congenital or acquired. The most common disease are frequently performing which detected by US on patients is chronic kidney disease, cyst and calculi. The most common hereditary disorder is cystic diseases, which include simple renal cyst and complex renal cyst or poly-cyst, and congenital disease is medical renal disease. Picture archiving and communication system (PACS) is relevant to medical imaging informatics. It is a comprehensive network of digital devices designed for acquisition, data transmission, storage, display, communication routes to other electronic system and management of diagnostic imaging studies. PACS are usually based on DICOM standards. Most PACSs handle images from various medical imaging instruments, including ultrasound (US), magnetic resonance (MR), positron emission tomography (PET), computed tomography (CT),endoscopy (ES)[18,19]. 233
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME Figure 1: Image depicting Normal and Abnormal Kidney Images. 2. METHODOLOGY The paper aims to classify the normal and abnormal conditions of kidney from the ultrasonic kidney images using ANNs. First, we performed image-preprocessing techniques such as cropping, rotation, edge detection and background subtraction to eliminate the disturbance factors from the images using MATLAB® and image processing toolbox. In order to perform the edge detection techniques we utilized imageJ software. Secondly, features were extracted from the background subtracted images by means of 4 feature extraction techniques namely the gray level run length matrix, intensity histogram, gray-level co-occurrence matrix and invariant moments. These are used to compute the adequate texture features. Thirdly, feature selection technique was employed using WEKA software in which optimal features are selected from the four feature extraction methods. The resultant output from the feature selection phase was stored in ‘.arff file’. Finally, artificial neural network utilizing a back-propagation algorithm was employed and then used it to classify the optimal feature set that resulted from the feature selection phase. These techniques are discussed in further detail within the later sections. 234
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME Figure 2: Workflow of Image Classification Using ANN 235
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME 2.1 Image Data Acquisition The data that was used in our experiments was collected from Jaslok Hospital & Research Centre, Mumbai. The images used for the analysis are acquired from two types of scanning systems using curvilinear probe with transducer frequency range of 2 – 5 MHz. The ultrasound images of 47 normal and 47 abnormal with mean age of 55(±15*) were collected, the abnormal images belong to three categories chronic kidney disease, renal calculus and cortical cyst. Out of these three categories there are 6 renal calculi, 31 chronic kidney diseases, 10 cortical cysts images. The images of both right and left kidneys are considered for the analysis. During the image acquisition, sonographer looks for better visualization of the image in the screen and freezes to store those images within the PACS. We then retrieve those images from PACS system for further analysis; here the images are stored in JPG format. 2.2 Image Preprocessing The image preprocessing methods are used to get the efficient results for further analysis. We applied four image preprocessing techniques such as Cropping, Rotation, Edge detection and Background removal to all images: Cropping eliminates the undesirable parts of the image usually peripheral to the area of interest. The cropping operation is performed in MATLAB® by sweeping through the images and cutting the image components in horizontal and vertical directions. Rotation of the cropped image is performed such that the major axis is aligned to zero degrees of image. Edge Detection is performed using ‘absnake_.jar’ external plugins which is based upon Adaptive active contours in Image J version 1.46r software [20]. After edge detection, Background removal is performed to remove the pixels that are present outside the contour region. Those pixels are regarded as unbounded pixels, whereas the pixels that are enclosed by the contour are considered as bounded pixel or pixel of interest. Figure 3: Workflow depicting the Kidney Image preprocessing; Step - a).Image after cropping operation; Step - b).Image after rotation; Step - c). Image after edge detection and Step - d).Final image after background subtraction 2.3 Feature Extraction After pre-processing of the images, which represents the data-cleaning phase, features relevant to the classification are extracted from the cleaned images by means of techniques such as Intensity Histogram features, Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM) and Rotation Invariant Moments 236
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME 2.3.1 Intensity Histogram (IH) Features Intensity Histogram texture measures are calculated from the original image values and it falls under the category of first-order statistics. They do not consider the relationships with neighborhood pixel. Features derived from this approach consist of mean, variance average energy, entropy, skewness and kurtosis [9]. 2.3.2 Gray level co-occurrence matrix (GLCM) The gray tone spatial dependence approach characterizes texture by the co-occurrence of its gray tones [10]. The gray-level co-occurrence matrix (GLCM) or gray-level spatial dependence matrix, a frequency matrix based calculations that fall under the category of second-order statistics. GLCM is a useful method for enhancing texture details and is used as an aid for interpretation of an image, which can be extracted from the co-occurrence matrix. The GLCM is a tabulation of how often different combinations of pixel brightness values occur in an image [11, 10]. We can calculate the following statistics elements as texture features: Autocorrelation, Contrast, Difference variance, Entropy, Correlation, Cluster Prominence, Cluster Shade, Homogeneity, Maximum probability, Sum of squares, Dissimilarity Energy, Sum average, Information measure of correlation, Sum variance, Sum entropy, information measure of correlation, Inverse difference normalized. 2.3.3 Gray level Run length matrix (GLRLM) The gray level run length matrix is another method of extracting the higher order statistics of the texture of image and has been a major descriptor of regularity and periodicity of the texture pattern. GLRLM characterizes coarse textures as having many pixels in a constant gray tone run and ‘fine textures’ as having few pixels in a constant gray tone run [12]. A gray level run length primitive is a maximal collinear connected set of pixels all having the same gray tone [10]. We can calculate the following statistics as texture features from image: Short Run Emphasis (SRE), Long Run Emphasis (LRE), Gray-Level Non-uniformity (GLN), Run Length Non uniformity (RLN), Run Percentage (RP), Low Gray-Level Run Emphasis (LGRE), High Gray-Level Run Emphasis (HGRE), Short Run Low Gray-Level Emphasis (SRLGE), Short Run High Gray-Level Emphasis (SRHGE) Long Run Low Gray-Level Emphasis (LRLGE), Long Run High Gray-Level Emphasis (LRHGE). 2.3.4 Rotation Invariant Moments (IM) There are many applications for texture analysis in which rotation-invariance is important, but the problem is that many of the existing texture features are not invariant with respect to the rotations. Hu’s 7 moment invariants are invariant under translation, changes in scale, and also rotation [13, 14]. It describes the image inspite of its location, size, and rotation. The moment invariants are generally specified in terms of normalized central moments [15]. In order to evaluate the efficiency of the texture features, we use four feature extraction module’s namely Intensity histogram feature, GLCM feature, GLRLM feature and Invariant Moments Features were extracted from each of the total 94 preprocessed images of kidney in MATLAB® using their module. Invariant moments include 7 features, GRLM includes 11 features, GLCM includes 22 features, and Intensity Histogram includes 6 features, therefore total: 6 + 22 + 11 + 7 = 46 features were extracted for each image and four features were extracted from each image therefore a total of 376 feature files were extracted each image. In total, 4324 features were extracted from all the images. 237
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME 2.4 Feature Selection All features were extracted from image and the resultant data contains many redundant or irrelevant features. Features selection technique is used to remove those redundant and irrelevant features and to find the significant features, which are useful in further analysis. Feature Selection was performed using Waikato Environment for Knowledge Analysis (WEKA) [16] software Version 3.6.9. WEKA is compatible with and recognizes only ‘.arff’ data files. Therefore ‘.arff’ file was generated which contains the value of features, that were extracted (including both normal as well as abnormal). Feature Extraction Method No. of selected feature/total feature Intensity Histogram features 3/6 GLCM features 11/22 GLRLM features 5/11 Invariant Moments 3/7 Combined Features 14/48 Table 1: Optimal Features selected in each Texture Algorithm 2.5 Training and Testing Of ANN After feature selection in order to classify both abnormal and normal conditions of kidney from optimized feature set neural network pattern recognition tool (nprtool) was used in MATLAB® [21]. A two-layer feed-forward network, with sigmoid hidden and output neurons was used. The samples were divided into training, validation and testing data on the basis of performance of each set and then we decide the final percentage of each sample. The samples were divided into 70% of Training, 15% of validation and 15% of Testing for this study. Number of hidden neurons present in the hidden layer is very important in the training of the dataset. For selecting hidden neuron there is no algorithm as it is based upon trial and error method [17]. On the basis of that, training trials were performed for selecting the hidden neuron members for various feature extraction method (see table no. 2). Figure 4: Screenshot demonstrating the architecture of ANN 238
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME The network was trained for 1000 number of epochs to classify the inputs according to the targets. Scaled conjugate gradient Back-propagation (trainscg) algorithm was used to train network then. There are two parameters Mean Squared Error (MSE) and percent error which tell us about how good our data is in terms of classification by seeing that value. Features Number of Hidden Neurons Intensity Histogram 2 GLCM 11 GLRLM 5 Invariant moments 4 Combined Features 13 Table 2: Number of hidden neuron for each feature extraction 3. RESULTS AND DISCUSSION In general, measures of quality of classification are built from a confusion matrix which records correct and incorrect recognition, such as the true positive (TP), false positive (FP), false negative (FN) and the true negative (TN). In neural network, the diagonal cell shows the number of classes that were correctly classified and the off diagonal cells show the misclassified cases. The blue cell in the bottom right shows the total percent of correctly classified cases in green and the total percent of misclassified cases in red. The results show very good recognition. The ROC curve can manifest the relationship between the true-positive rate (TPR) and false-positive rate (FPR) with the variations in decision threshold. If curve is on diagonal or off diagonal then the diagnostic system is not considered to be effective. If the curve is near the axis of true positive rate or it touches to axis completely then the diagnostic system is considered to be excellent. If the curve exists between diagonal or in the middle axis of that then it is considered to be a good diagnostic system. To investigate the performance of classification of selected feature’s, performance measures such as accuracy, sensitivity, specificity and false negative rate were computed. Figure 5: Image depicting Confusion Matrix and ROC plot of Intensity histogram feature of training data 239
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME Figure 6: Image depicting Confusion Matrix and ROC plot of INVARINTS MOMENTS feature of training data Figure 7: Image depicting confusion matrix & ROC plot of GRLRM of training data Figure 8: Image depicting confusion matrix & ROC plot of GLCM feature of training data Figure 9: Image depicting Confusion Matrix and ROC plot of COMBINED feature of training data 240
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME Performance measures were computed from confusion matrix of COMBINED features as seen in table 3. The classification accuracy of COMBINED feature was 100 percent at the time of training and 87.5 percent at the time of testing, which is better than that of other feature extraction’s accuracy. The sensitivity and specificity values were 1 and 1 of training, respectively, which were also better than those of the other features. Specificity of COMBINED feature at testing was 0.75, which was not better than GLRLM feature. Specificity of GLRLM feature was 0.8, and as a result of it large difference between the two values cannot be observed. In addition, an effective classification method should decrease the possibility of misclassification, especially for the false-negative rate. A high false-negative rate represents the risk of underestimating the disease severity in a patient when the clinicianis making of use the classification system. Therefore, the false-negative rate may be considered as an index for evaluating the performances of the features [9]. On the basis of that the value of false negative word (FNR) of COMBINED feature was zero, which was also better from other feature extraction methods. FEATURE Training Accuracy TNR Testing TPR FNR Accuracy TNR TPR FNR Intensity Histogram 0.8298 0.8511 0.8085 0.1489 0.825 0.85 0.8 0.2 Invariant Moments 0.5426 0.2766 0.8085 0.7235 0.475 0.75 0.2 0.8 GLRLM 0.0898 0.8085 0.8511 0.1915 0.85 0.85 0.9 0.1 GLCM 0.8298 0.8085 0.8511 0.1915 0.825 0.85 0.8 0.2 Combined Feature 1 0.875 0.75 1 0 1 1 0 Table 3: Performance measure of all feature extraction methods 4. CONCLUSION The study was undertaken to evaluate which of the different feature extraction methods give high recognition rate and for classifying abnormalities in US kidney images using Artificial neural network. The Intensity Histogram feature, GLCM feature, GLRLM feature, Invariant moments feature and COMBINED feature were considered for performance evaluation. According to the results (see table 3) obtained, it is difficult to make a claim between GLRLM and COMBINED sets, as to which individual feature is superior because the specificity value of COMBINED feature of testing is less than that of GLRLM. By considering other parameters such as sensitivity, false negative rate and total accuracy rate of training and testing which are far better than that of GRLRM features from that it provides conclusive evidence that COMBINED feature performs well and high recognition rates have been achieved as compared to other feature extraction methods. COMBINED feature reached a 100% accuracy rate in training datasets. It correctly classified 94 instances from 94 instances. On testing datasets it correctly classified 35 instances out of 40 with an 87.5% accuracy rate. This revealed that the usage of COMBINED feature was relatively effective in abnormal classification of kidney. 241
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME 5. ACKNOWLEDGEMENTS We gratefully acknowledge Mr. Sandeepan Mukherjee (Scientific Officer from HITRT) and Mr. Samrit Maity (Senior Technical Officer from CDAC Pune) for their thoughtful reviews of this paper. 6. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] C. Karthikeyini, B.K. Raja, M. Madheswaran, Study on Ultrasound Kidney Images using Principal Component Analysis: A Preliminary Result. In:Proc. of Fourth Indian Conference on Computer Vision, Graphics and Image Processing, 2004; 190 – 195. M. Singh, S. Singh, S. Gupta, A New Quantitative Metric for Liver Classification from Ultrasound Images. International Journal of Applied Physics and Mathematics, 2(6),2012. S.A. Hagen, Urinary System in Diagnostic Ultrasound, 4(St. Louis: Mosby/Elsevier, 1995). H.M. Pollack, B.L. McClennan, Clinical Urography, 2(Philadelphia:W B saunders/Elsevier, 2000). A.Ahumada, C. Null, Image Quality: A Multidimensional Problem. Digital Images and Human Vision, Branford press, Cambridge Mass, 1993. R.M. Haralick, K. Shanmugam, I. Dinstein, Texture features for image classification, IEEE Trans. Syst. Man. Cybern., SMC-3, 1973, 610-621. K.B. Raja, M. Madheswaran, K.Thyagarajah, Evaluation of Ultrasound Kidney Images Using Dominant Gabor Wavelet (Dom-Gw) for Computer Assisted Disorder Identification and Classification”. Biomedical Engineering: Applications, Basis and Communications, 19(6), 2007, 395–407. Ahmadian A, Mostafa A, bolhassani MA, Alam NR, Gitti M. “An efficient texture feature extraction method for classification of liver sonography based on Gabor Wavelet”. Medicon, 2004. S. Helgason, “The Radon Transform”, 2(Boston: John Wiley and Sons, 1983). R. M. Hawlick, “Statistical and Structural Approaches to Texture”. Proc. of the IEEE, 67(5),1979, 786-810. S. Selvarajah, S. R.Kodituwakku, Analysis and Comparison of Texture Features for Content Based Image Retrieval, International Journal of Latest Trends in Computing, 2(1), 2011, 108-113. S. Poonguzhali, G.Ravindran, Automatic classification of focal lesions in ultrasound liver images using combined texture features, Information Technology Journal, 7(1), 2008, 205–209. M. Pietikäinen, T. Ojala, Z. Xu, “Rotation-Invariant Texture Classification Using Feature Distributions” J. Flusser, T. Suk, “Rotation Moment Invariants for Recognition of Symmetric Objects”. IEEE Transactions on Image Processing, 15(12),2006. B.F. Branstetter, Basics of Imaging Informatics: Part1, Radiology, 243, 2007, 656-667. N. Sharma, A. Bajpai, R. Litoriya, Comparison the various clustering algorithms of weka tools, International Journal of Emerging Technology and Advanced Engineering, 2(5), 2012, 73-80. I. A. Basheer, M.Hajmeer, Artificial neural networks: fundamentals, computing, design and application. Journal of Microbiological Methods, 43, 2014, 3 -31. M. H. Hood, H. Scott, Introduction to Picture Archive and Communication Systems”. J RadiolNurs, 25, 2006, 69-74. 242
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME [19] R.Choplin, Picture archiving and communication systems: an overview. Radiographics, 12, 1992, 127-129. [20] P. Andrey, T. Boudier, Adaptive active contours (snakes) for the segmentation of complex structures in biological images, In:Proc.ImageJ Conference, Innov. Techn. Biol. Med., 18, 2004. [21] V. Arulmozhi, Classification task by using Matlab Neural Network Tool Box – A Beginner’s View, International Journal of Wisdom Based Computing, 1 (2),2011,59-60. [22] Ratil Hasnat Ashique, Md Imrul Kayes, M T Hasan Amin and Badrun Naher Liya, “Speckle Noise Reduction from Medical Ultrasound Images using Wavelet Thresholding and Anisotropic Diffusion Method”, International Journal of Electronics and Communication Engineering &Technology (IJECET), Volume 4, Issue 4, 2013, pp. 283 - 290, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. [23] Dr.Muhanned Alfarras, “Early Detection of Adult Valve Disease–Mitral Stenosis using the Elman Artificial Neural Network”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 3, 2012, pp. 255 - 264, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [24] Abhishek Choubey, Omprakash Firke and Bahgwan Swaroop Sharma, “Rotation and Illumination Invariant Image Retrieval using Texture Features”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 3, Issue 2, 2012, pp. 48 - 55, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. AUTHORS PROFILE 1. Karthik Kalyan: He currently works as a Scientific Officer at Systems Biomedicine Division, Haffkine Institute for Training Research and Testing (HITRT) in Mumbai. His research interests include Hi Performance Computation, Agent Based Modelling and Simulation (ABMS), Complex Adaptive Systems, Bio-Complexity, Intelligent Software Systems Development, Artificial Neural Networks, Bio-Medical Image Processing, Complex Systems and Emergence and Decision Making. 2. Suvigya Jain: He is a Short Term Research (STRIP) Intern at Haffkine Institute for Training Research and Testing (HITRT) in Mumbai. His research interests include Artificial Neural Networks, Medical Image Processing, and Sequence Analysis. 3. Dr. Ramachandra Dattatraya Lele: He is the Chairman of Research Advisory Council at Haffkine Institute for Training Research and Testing and Hon. Chief Physician and Director of Nuclear Medicine at Jaslok Hospital and Research Centre. His research interests include Bioinformatics, Biomedical Imaging, Nuclear Medicine and Medical Informatics. 4. Dr. Mukund Joshi: He is the Head of the Department of Ultrasound Department at Jaslok Hospital and Research Center in Mumbai. His research interests include Pediatric Urology, Prostrate Imaging, Newer Trends in Breast Ultrasound, Newer Trends in Prostratic Ultrasound, Acoustic Radiation Force Impulse Imaging (ARFI), Elastographic Studies. 5. Dr. Abhay Chowdhary: He is the Director of Haffkine Institute for Training Research and Testing (HITRT) in Mumbai. His research interests include Medical Virology and Vaccinology. 243