Innovative Features in Pathological Tissues Segmentation and Classification of MRI Brain Images with Aid of Back Propagation Neural Network
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Abstract
The most necessary part of the living things which standardizes and manages other organs is the brain. The brain may get affected through any disease if the patient is not in a normal condition. Therefore it is significant to examine the condition of the brain. In the region of brain MRI image deformity fragmentation, various research works were made. However these research efforts presentations are needed in the image pre-analysis. During pre-analysis brain via MRI brain images for identifying the deformity, it is essential to examine the acquired patient’s image in detail. An error treatment will be specified to the influenced patient if the study may have any error. So there is a necessity to develop precision in the deformity segmentation by achieving the fundamental pre-analysis in the MRI images.
A combined approach with MRI brain image abnormality segmentation and denoising process is proposed in this paper. The proposed technique comprised of five stages namely, (i) Preprocessing, (ii) Feature Extraction, (iii) Image Classification, (iv) Segmentation and (v) Tissues Classification. Initially the database images are given to the preprocessing stage, for removing the noise. In preprocessing, the denoising process is performed it increases the segmentation and feature extraction accuracy. After the preprocessing, the image features are extracted to classify the images in the image database into normal and abnormal. After the image classification, the abnormal MRI images abnormal tissues like stroke, trauma and tumor are segmented. For this, the features are extracted from the segmented abnormal tissues. In the proposed technique, three features such as modified entropy, energy and innovative feature are extracted in the feature extraction stage.
By using these extracted features, the abnormal tissues are classified by using a well known classification technique called Feed Forward Back Propagation Neural Network (FFBNN). The implementation results show the effectiveness of proposed MRI abnormality tissues segmentation technique in segmenting and classifying the MRI images and the achieved improvement in the segmentation and classification result. Furthermore, the performance of the proposed technique is evaluated by comparing with the existing MRI image segmentation techniques
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Kharate, Patil and Bhale, "Selection of Mother Wavelet for Image Compression on Basis of Image", Journal of Multimedia, Vol. 2, No. 6, pp. 44-52, November 2007
Kobus Barnard, Pinar Duygulu, David Forsyth, Nando de Freitas, David M. Blei and Michael I. Jordan, "Matching Words and Pictures", Journal of Machine Learning Research, Vol. 3, pp. 1107–1135, 2003
Luis M. R. Bras, Elsa F. Gomes, Margarida M.M. Ribeiro and Guimaraes, "Drop Distribution Determination in a Liquid-Liquid Dispersion by Image Processing", International Journal of Chemical Engineering, Vol. 2009, pp. 1-6, March 2009
Byeong-Ho KANG, "A Review on Image and Video processing", International Journal of Multimedia and Ubiquitous Engineering, Vol. 2, No. 2, pp. 49-64, April 2007
Daggu Venkateshwar Rao, Shruti Patil, Naveen Anne Babu and Muthukumar, "Implementation and Evaluation of Image Processing Algorithms on Reconfigurable Architecture using C-based Hardware Descriptive Languages", International Journal of Theoretical and Applied Computer Sciences, Vol. 1, No. 1, pp. 9-34, 2006
Ghrare, Mohd. Ali, Jumari and Ismail, "An Efficient Low Complexity Lossless Coding Algorithm for Medical Images", American Journal of Applied Sciences, Vol. 6, No. 8, pp. 1502-1508, 2009
Brintha Therese and Sundaravadivelu, "Bipolar Incoherent Image Processing for Edge Detection of Medical Images", International Journal of Recent Trends in Engineering, Vol. 2, No. 2, pp. 229-232, November 2009
Asma Yasrib and Mohd Adam Suhaimi, "Image Processing in Medical Applications", Journal of Information Technology Impact, Vol. 3, No. 2, pp. 63-68, 2003
Balasubramanian and Porkumaran, “Registration of PET and MR Images of Human Brain Using Normalized Cross Correlation Algorithm and Spatial Transformation Techniques", Journal of Theoretical and Applied Information Technology, Vol. 16. No. 1, pp. 1-8, 2010
Hyder Ali and Sukanesh, "An Edge Preserving Denoising Technique for MR Images Using Curvelet Transform", Interdisciplinarty Journal, Vol. 91, May 2010
AmirEhsan and Lashkari, "A Neural Network based Method for Brain Abnormality Detection in MR Images Using Gabor Wavelets", International Journal of Computer Applications, Vol. 4, No.7, pp. 9-15, July 2010
Rajeev Ratan, Sanjay Sharma and Sharma, "Brain Tumor Detection based on Multi-parameter MRI Image Analysis", ICGST-GVIP Journal, Vol. 9, No. 3, pp. 9-17, June 2009
de Seze, Delalande, Michelin, Gauvrit, Mackowiak, Ferriby, Stojkovic, Defebvre, Pruvo and Vermersch, "Brain MRI in late-onset multiple sclerosis", European Journal of Neurology, Vol. 12, No. 4, pp. 241-244, April 2005
Logeswari and Karnan, "An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Hierarchical Self Organizing Map", International Journal of Computer Theory and Engineering, Vol. 2, No. 4, pp. 591-595, August 2010
Bing Liu, Tianzi Jiang, Songde Ma, Huizhi Zhao, Jun Li, Xingpeng Jiang and Jing Zhang, "Exploring candidate genes for human brain diseases from a brain-specific gene network", Biochemical and Biophysical Research Communications, Vol. 349, No. 4, pp. 1308-1314, November 2006
Capelle, Alata, Fernandez, Lefevre and Ferrie, "Unsupervised Segmentation for Automatic Detection of Brain Tumors in MRI", In proceeding of International Conference on Image Processing, Vol. 1, pp. 613-616, August 2002
Baki Koyuncu and Alper Pahsa, "Contour Profiling of Brain Tumor Areas by Using Image Correlation and Peak Detection Techniques", IJCSNS International Journal of Computer Science and Network Security, Vol. 6, No. 11, pp. 46-48, November 2006
Yongyue Zhang, Michael Brady and Stephen Smith, "Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm", IEEE Transactions on Medical Imaging, Vol. 20, No. 1, pp. 45-57, January 2001
Terrence Chen and Thomas S. Huang, "Region Based Hidden Markov Random Field Model for Brain MR Image Segmentation", World Academy of Science, Engineering and Technology, No. 4, pp. 233-236, April 2005
Kesavamurthy and SubhaRani, "Pattern Classification using imaging techniques for Infarct and Hemorrhage Identification in the Human Brain", Calicut Medical Journal, Vol. 4, No. 3, pp. 1-5, 2006
Nur Faiza Ishak, Rajasvaran Logeswaran and Wooi-Haw Tan, "Artifact and noise stripping on low-field brain MRI", International Journal of Biology and Biomedical Engineering, Vol. 2, No. 2, pp. 59-68, 2008
Dubey, Hanmandlu, Gupta and S. K. Gupta, "An Advanced Technique for Volumetric Analysis", International Journal of Computer Applications, Vol. 1, No. 1, pp. 91-98, February 2010
Logeswari and Karnan, "An improved implementation of brain tumor detection using segmentation based on soft computing", Journal of Cancer Research and Experimental Oncology, Vol. 2, No. 1, pp. 6-14, March 2010
James C. Patterson, David L. Lilien, Amol Takalkar and James B. Pinkston, "Early Detection of Brain Pathology Suggestive of Early AD Using Objective Evaluation of FDG-PET Scans", International Journal of Alzheimer’s Disease, Vol. 2011, pp. 1-9, September 2010
Soumya Maitra, "Morphological Edge Detection Using Bit-Plane Decomposition in Gray Scale Images", In Proceedings of INDIA Com, New Delhi, 2011
http://en.wikipedia.org/wiki/Sensitivity_and_specificity
Radomir S. Stankovic and Bogdan J. Falkowski, "The Haar wavelet transform: its status and achievements", Computers and Electrical Engineering, Vol. 29, pp. 25–44, 2003
Smitha J. C and Suresh Babu . S "A Broad Review of Noteworthy Researches on Brain Abnormality Detection with the Aid of Medical Images ", European Journal of Scientific Research, Vol.85, No. 2 , pp. 279 - 304, 2012
Zhang, S.-L., Pan, Z.-H., One improved kind of λ-resolution for medium predicate logic, (2011) International Review on Computers and Software (IRECOS), 6 (2), pp. 150-154.
Zakrani, A., Idri, A., Applying radial basis function neural networks based on fuzzy clustering to estimate web applications effort, (2010) International Review on Computers and Software (IRECOS), 5 (5), pp. 516-524.
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