Classification of magnetic resonance images

The aim of the paper is to compare classification error of the classifiers applied to magnetic resonance images for each descriptor used for feature extraction. We compared several Support Vector Machine (SVM) techniques, neural networks and k nearest neighbor classifier for classification of Magnetic Resonance Images (MRIs). Different descriptors are applied to provide feature extraction from the images. The dataset used for classification contains magnetic resonance images classified in 9 classes.

[1]  Dejan Gjorgjevikj,et al.  A Multi-class SVM Classifier Utilizing Binary Decision Tree , 2009, Informatica.

[2]  Samy Bengio,et al.  Torch: a modular machine learning software library , 2002 .

[3]  Gidudu Anthony,et al.  Image Classification Using SVMs: One-against-One Vs One-against-All , 2007, ArXiv.

[4]  Chuin-Mu Wang,et al.  Classification for Breast MRI Using Support Vector Machine , 2008, 2008 IEEE 8th International Conference on Computer and Information Technology Workshops.

[5]  Ron Kikinis,et al.  Adaptive Template Moderated Spatially Varying Statistical Classification , 1998, MICCAI.

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

[7]  D. Louis Collins,et al.  Model-based 3-D segmentation of multiple sclerosis lesions in magnetic resonance brain images , 1995, IEEE Trans. Medical Imaging.

[8]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[9]  Tshilidzi Marwala,et al.  Image Classification Using SVMs: One-against-One Vs One-against-All , 2007, ArXiv.

[10]  Y. M. Latha.  Content Based Color Image Retrieval via Wavelet Transforms , 2007 .

[11]  Johan Montagnat,et al.  Automated Estimation of Brain Volume in Multiple Sclerosis with BICCR , 2001, IPMI.

[12]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[13]  Ron Kikinis,et al.  Adaptive, template moderated, spatially varying statistical classification , 2000, Medical Image Anal..

[14]  Dejan Gjorgjevikj,et al.  Multi-class classification using support vector machines in decision tree architecture , 2009, IEEE EUROCON 2009.

[15]  D. Selvathi,et al.  Brain MRI Slices Classification Using Least Squares Support Vector Machine , 2007 .

[16]  Alan C. Evans,et al.  Structural maturation of neural pathways in children and adolescents: in vivo study. , 1999, Science.

[17]  Yi Liu,et al.  One-against-all multi-class SVM classification using reliability measures , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[18]  Alan C. Evans,et al.  Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis , 2002, IEEE Transactions on Medical Imaging.

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

[20]  Dejan Gjorgjevikj,et al.  MULTI-CLASS CLASSIFICATION USING SUPPORT VECTOR MACHINES IN BINARY TREE ARCHITECTURE , 2009 .

[21]  B. S. Manjunath,et al.  Introduction to MPEG-7: Multimedia Content Description Interface , 2002 .

[22]  J. Rapoport,et al.  Progressive cortical change during adolescence in childhood-onset schizophrenia. A longitudinal magnetic resonance imaging study. , 1999, Archives of general psychiatry.

[23]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[24]  Alan C. Evans,et al.  Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI , 2000, NeuroImage.