Overview

Automated and accurate classification of MR brain images is extremely important for medical analysis and interpretation. Over the last decade numerous methods have already been proposed. In this thesis presented a novel method to classify a given MRI brain image as normal or abnormal. The proposed method first employed wavelet transform to extract features from images, followed by applying principle component analysis (PCA) to reduce the dimensions of features.The reduced features were submitted to a kernel support vector machine (KSVM). The strategy of K-fold stratified cross validation was used to enhance generalization of KSVM. Chose seven common brain diseases (glioma, meningioma, Alzheimer's disease, Alzheimer's disease plus visual agnosia, Pick'sdisease, sarcoma, and Huntington's disease) as abnormal brains, and collected 160 MR brain images (20 normal and 140 abnormal) from Signal and Image Processing Institute Electrical Engineering and Biomedical Engineering University of Southern California and Gokuldash Hospital Laborites Indore India. We performed our proposed methods with four different kernels, and found that the GRB kernel achieves the highest classification accuracy as 99.38%.We also compared our method to those from literatures in the last decade, and the results showed our DWT+PCA+KSVM with GRB kernels till achieved the best accurate classification results. The averaged processing time for a 256£256 size image on a laptop of I3 HP with 3 GHz processor and 4 GB RAM is 0.0448 s.It could be applied to the field of MR brain image classification and can assist the doctors to diagnose where a patient is normal or abnormal to certain degrees.

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