Multiple Sclerosis Diagnosis Based on Analysis of Subbands of 2-D Wavelet Transform Applied on MR-images

In this study, we have proposed a novel approach to investigate the features of four subbands of 2-D wavelet transform in magnetic resonance images (MRIs) for normal and abnormal brains which defected by multiple sclerosis (MS). Concurrently, another method extracts different kinds of features in spatial domain. Totally, 116 features have been extracted. Before applying the algorithm, we have to use a registration method because of variety in size of brain images. All extracted features have been passed over the principal component analysis (PCA) and have been pushed to an artificial neural network (ANN) that is a feed-forward type. According to changing in position of defected parts of brain, we have analyzed four different MRI datasets in different stages of MS progression, including 101 MRIs of normal and abnormal brain images. In all cases, certain diagnosis is gained. Meantime, 40 percent of the datasets have been reserved as the "test data ".