Brain Tumor Segmentation Using Support Vector Machines

One of the challenging tasks in the medical area is brain tumor segmentation which consists on the extraction process of tumor regions from images. Generally, this task is done manually by medical experts which is not always obvious due to the similarity between tumor and normal tissues and the high diversity in tumors appearance. Thus, automating medical image segmentation remains a real challenge which has attracted the attention of several researchers in last years. In this paper, we will focus on segmentation of Magnetic Resonance brain Images (MRI). Our idea is to consider this problem as a classification problem where the aim is to distinguish between normal and abnormal pixels on the basis of several features, namely intensities and texture. More precisely, we propose to use Support Vector Machine (SVM) which is within popular and well motivating classification methods. The experimental study will be carried on Gliomas dataset representing different tumor shapes, locations, sizes and image intensities.

[1]  M. Stella Atkins,et al.  Fully automatic segmentation of the brain in MRI , 1998, IEEE Transactions on Medical Imaging.

[2]  Terrence J. Sejnowski,et al.  Comparison of machine learning and traditional classifiers in glaucoma diagnosis , 2002, IEEE Transactions on Biomedical Engineering.

[3]  Jérémy Lecoeur,et al.  Segmentation d'images cérébrales : État de l'art , 2007 .

[4]  Christoph Meinel,et al.  Segmentation and quantification of brain tumor , 2004, 2004 IEEE Symposium on Virtual Environments, Human-Computer Interfaces and Measurement Systems, 2004. (VCIMS)..

[5]  Konstantina S. Nikita,et al.  Computer aided diagnosis based on medical image processing and artificial intelligence methods , 2006 .

[6]  Guido Gerig,et al.  Automatic brain tumor segmentation by subject specific modification of atlas priors. , 2003, Academic radiology.

[7]  R. Kikinis,et al.  Automated segmentation of MR images of brain tumors. , 2001, Radiology.

[8]  Michal Strzelecki,et al.  Texture Analysis Methods - A Review , 1998 .

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

[10]  Alan C. Evans,et al.  An MRI-based stereotactic atlas from 250 young normal subjects , 1992 .

[11]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[12]  Mario Fritz,et al.  On the Significance of Real-World Conditions for Material Classification , 2004, ECCV.

[13]  Guido Gerig,et al.  A brain tumor segmentation framework based on outlier detection , 2004, Medical Image Anal..

[14]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[15]  Nicholas Ayache,et al.  Medical Image Analysis: Progress over Two Decades and the Challenges Ahead , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[17]  Barry T. Thomas,et al.  Using Neural Networks to Automatically Detect Brain Tumours in MR Images , 1997, Int. J. Neural Syst..

[18]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[19]  Mark Schmidt,et al.  Automatic Brain Tumor Segmentation , 2005 .

[20]  Jiří Matas,et al.  Computer Vision - ECCV 2004 , 2004, Lecture Notes in Computer Science.

[21]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  S. Gunn.  Support Vector Machines for Classification and Regression , 1998 .

[23]  Lawrence O. Hall,et al.  Automatic tumor segmentation using knowledge-based techniques , 1998, IEEE Transactions on Medical Imaging.