Daubechies 4 wavelet with a support vector machine as an efficient method for classification of brain images

Recently there has been a great need for efficient classification techniques in the field of medical imaging to accurately detect various human brain diseases. Extracting essential features from the magnetic resonance (MR) images of the brain is imperative for the proper diagnosis of the disease. We show that the classification success percentage is higher using features obtained from the wavelet transforms than using features obtained from the independent component analysis (ICA) for the MR human brain image data. Wavelet features that represent diseased portions are well localized and distinct, resulting in high classification accuracy. Due to their better generalization performance than neural network-based classification techniques, support vector machines (SVMs), are used for the purpose of classification. We concentrate on the stagewise classification of coronal versus sagittal images for normal versus diseased brain images, and our technique can be extended to multicategory classification, which involves various sections and disorders