ANALYSIS OF DIFFERENT WAVELETS FOR BRAIN IMAGE CLASSIFICATION USING SUPPORT VECTOR MACHINE

Abstract

Automated classification of medical images with high accuracy is crucial when dealing with human life. In this paper, Discrete Wavelet Transform (DWT) based classification of Magnetic Resonance Images (MRI) of the brain is presented. The given input images are de-noised using a median filter in the preprocessing stage. Then, the de-noised images are given as inputs to the wavelet transform. The wavelet transform is used for feature extraction purpose. Most transformation techniques produce coefficient values with their dimension same as the original image. Further processing of the coefficient values must be applied to extract the image feature vectors. Predefined families of wavelets such as Daubechies (db8), Symlets (sym8) and Biorthogonal (bio3.7) are used. From that energy information's are extracted and provided as input to the recognition or classification stage. Finally, the brain images are classified by Support Vector Machine (SVM) classifier whether it is normal or abnormal. Results show that db8 filter provides higher accuracy than other wavelets.