A REVIEW ON CURRENT MRI BRAIN TISSUE SEGMENTATION , FEATURE EXTRACTION AND CLASSIFICATION TECHNIQUES

MRI brain image plays a vital role in assisting radiologists to access patients for diagnosis and treatment. Studying of medical image by the Radiologist is not only a tedious and time consuming process but also accuracy depends upon their experience. So, the use of computer aided systems becomes very necessary to overcome these limitations. Even though several automated methods are available, still segmentation of MRI brain image remains as a challenging problem due to its complexity and there is no standard algorithm that can produce satisfactory results. In this review paper, various current methodologies of brain image segmentation using automated algorithms that are accurate and requires little user interaction are reviewed and their advantages, disadvantages are discussed. This review paper guides in combining two or more methods together to produce accurate results.

[1]  Wen-Xiong Kang,et al.  The Comparative Research on Image Segmentation Algorithms , 2009, 2009 First International Workshop on Education Technology and Computer Science.

[2]  Miao Qi,et al.  A Modified FCM Algorithm for MRI Brain Image Segmentation , 2008, 2008 International Seminar on Future BioMedical Information Engineering.

[3]  Pan Lin,et al.  An efficient automatic framework for segmentation of MRI brain image , 2004, The Fourth International Conference onComputer and Information Technology, 2004. CIT '04..

[4]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[5]  M. H. Chowdhury,et al.  Image thresholding techniques , 1995, IEEE Pacific Rim Conference on Communications, Computers, and Signal Processing. Proceedings.

[6]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[7]  Satish Chandra,et al.  A PSO based method for detection of brain tumors from MRI , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[8]  Y. A. Tolias,et al.  On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system , 1998, IEEE Signal Processing Letters.

[9]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[10]  R. Dhanasekaran,et al.  Segmentation of Cerebrospinal Fluid and Internal Brain Nuclei in Brain Magnetic Resonance Images , 2013 .

[11]  Ehab F. Badran,et al.  An algorithm for detecting brain tumors in MRI images , 2010, The 2010 International Conference on Computer Engineering & Systems.

[12]  Yan Li,et al.  MR Brain Image Segmentation Based on Self-Organizing Map Network , 2005 .

[13]  Umi Kalthum Ngah,et al.  Image classification of brain MRI using support vector machine , 2011, 2011 IEEE International Conference on Imaging Systems and Techniques.

[14]  Koenraad Van Leemput,et al.  Automated model-based tissue classification of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[15]  R P Velthuizen,et al.  MRI: stability of three supervised segmentation techniques. , 1993, Magnetic resonance imaging.

[16]  K. Thanushkodi,et al.  Exploration on Selection of Medical Images employing New Transformation Technique , 2010 .

[17]  Chin-Teng Lin,et al.  Support-vector-based fuzzy neural network for pattern classification , 2006, IEEE Transactions on Fuzzy Systems.

[18]  V. Lopez,et al.  Brain tumour diagnosis with Wavelets and Support Vector Machines , 2008, 2008 3rd International Conference on Intelligent System and Knowledge Engineering.

[19]  Irfan Mehmood,et al.  Bayesian Classification Using DCT Features for Brain Tumor Detection , 2010, KES.

[20]  Gilles Venturini,et al.  AntTree: a new model for clustering with artificial ants , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[21]  Chia-Feng Juang,et al.  A Self-Organizing TS-Type Fuzzy Network With Support Vector Learning and its Application to Classification Problems , 2007, IEEE Transactions on Fuzzy Systems.

[22]  Richa Mishra,et al.  MRI based brain tumor detection using wavelet packet feature and artificial neural networks , 2010, ICWET.

[23]  Richard A. Robb,et al.  Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction , 1998, IEEE Transactions on Medical Imaging.

[24]  Yanbo Li,et al.  General Tendencies in Segmentation of Medical Ultrasound Images , 2009, 2009 Fourth International Conference on Internet Computing for Science and Engineering.

[25]  Benoit M. Macq,et al.  Segmentation using a region-growing thresholding , 2005, IS&T/SPIE Electronic Imaging.

[26]  Chin-Tu Chen,et al.  Segmentation of dual-echo MR images using neural networks , 1993 .

[27]  Navin Rajpal,et al.  Comparative study of image segmentation techniques and object matching using segmentation , 2009, 2009 Proceeding of International Conference on Methods and Models in Computer Science (ICM2CS).

[28]  Debnath Bhattacharyya,et al.  Brain Tumor Detection Using MRI Image Analysis , 2011, UCMA.

[29]  P. Karch,et al.  An experimental comparison of modern methods of segmentation , 2010, 2010 IEEE 8th International Symposium on Applied Machine Intelligence and Informatics (SAMI).

[30]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[31]  Rajesh N. Davé,et al.  Characterization and detection of noise in clustering , 1991, Pattern Recognit. Lett..

[32]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[33]  Earl Cox,et al.  Fuzzy Modeling And Genetic Algorithms For Data Mining And Exploration , 2005 .

[34]  Liu Feng-yu,et al.  Application of Support Vector Machines on Network Abnormal Intrusion Detection , 2006 .

[35]  Jie Yang,et al.  Degree prediction of malignancy in brain glioma using support vector machines , 2006, Comput. Biol. Medicine.

[36]  W. Eric L. Grimson,et al.  Adaptive Segmentation of MRI Data , 1995, CVRMed.

[37]  D. Tian,et al.  A Brain MR Images Segmentation Method Based on SOM Neural Network , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[38]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[39]  Xiaochun Yang,et al.  An Improved Clustering Algorithm Based on Ant-Tree , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[40]  Jerry L. Prince,et al.  A Survey of Current Methods in Medical Image Segmentation , 1999 .

[41]  A. Kharrat,et al.  A Hybrid Approach for Automatic Classification of Brain MRI Using Genetic Algorithm and Support Vector Machine , 2010 .

[42]  Sabine Van Huffel,et al.  A combined MRI and MRSI based multiclass system for brain tumour recognition using LS-SVMs with class probabilities and feature selection , 2007, Artif. Intell. Medicine.

[43]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[44]  Shanthi Mahesh,et al.  A Comparative Study of Different Segmentation Techniques for Brain Tumour Detection , 2013 .

[45]  Z.J. Koles,et al.  Medical Image Segmentation: Methods and Software , 2007, 2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging.

[46]  K. Nithya,et al.  Brain Tumor Detection Using Modified Histogram Thresholding-Quadrant Approach , 2012 .

[47]  Scott T. Acton,et al.  Scale space classification using area morphology , 2000, IEEE Trans. Image Process..

[48]  V. Vapnik Pattern recognition using generalized portrait method , 1963 .

[49]  MRI BRAIN TUMOUR DETECTION BY HISTOGRAM AND SEGMENTATION BY MODIFIED GVF MODEL , 2013 .

[50]  Deepali Kelkar,et al.  Improved Quadtree Method for Split Merge Image Segmentation , 2008, 2008 First International Conference on Emerging Trends in Engineering and Technology.

[51]  P. Lions,et al.  Image selective smoothing and edge detection by nonlinear diffusion. II , 1992 .

[52]  Lalit M. Patnaik,et al.  Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network , 2006, Biomed. Signal Process. Control..

[53]  Abdel-Badeeh M. Salem,et al.  A HYBRID TECHNIQUE FOR AUTOMATIC MRI BRAIN IMAGES CLASSIFICATION , 2009 .

[54]  Dao-Qiang Zhang,et al.  A novel kernelized fuzzy C-means algorithm with application in medical image segmentation , 2004, Artif. Intell. Medicine.

[55]  Nahla Ibraheem Jabbar,et al.  Application of Fuzzy Neural Network for Image Tumor Description , 2008 .

[56]  Zeng Wenhua,et al.  An improved AntTree algorithm for MRI brain segmentation , 2008, 2008 IEEE International Symposium on IT in Medicine and Education.

[57]  Renjie-Zhang Xin-Jiang Image Segmentation Based on PDEs Model: a Survey , 2009 .

[58]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[59]  Hui Zhang,et al.  Image segmentation evaluation: A survey of unsupervised methods , 2008, Comput. Vis. Image Underst..

[60]  Guido Gerig,et al.  Model-based brain and tumor segmentation , 2002, Object recognition supported by user interaction for service robots.

[61]  Panos Kotsas Non-Rigid Registration of Medical Images using an Automated Method , 2005, IEC.

[62]  James C. Bezdek,et al.  A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain , 1992, IEEE Trans. Neural Networks.

[63]  S. Murugavalli,et al.  An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Neuro Fuzzy Technique , 2007 .

[64]  Aly A. Farag,et al.  A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data , 2002, IEEE Transactions on Medical Imaging.

[65]  Shohreh Kasaei,et al.  Automatic Brain Tissue Detection in Mri Images Using Seeded Region Growing Segmentation and Neural Network Classification , 2011 .

[66]  Javad Alirezaie,et al.  Automatic segmentation of cerebral MR images using artificial neural networks , 1996 .