Classification of brain MRI using the LH and HL wavelet transform sub-bands

The problem of automatic classification of brain images obtained by magnetic resonance imaging (MRI) is considered. In order to design the classification system, a three- stage approach is used. It consists of wavelet decomposition of the image under study, feature extraction from the LH and HL sub- bands using first order statistics, and final classification by support vector machines (SVM). The proposed approach shows higher performance than when using features extracted from the LL sub-band. It is concluded that the horizontal and vertical sub- bands of the wavelet transform can effectively encode the discriminating features of normal and pathological images.