Analysis of brain white matter hyperintensities using pattern recognition techniques

The brain white matter is responsible for the transmission of electrical signals through the central nervous system. Lesions in the brain white matter, called white matter hyperintensity (WMH), can cause a significant functional deficit. WMH are commonly seen in normal aging, but also in a number of neurological and psychiatric disorders. We propose here an automatic method for WHM analysis in order to distinguish regions of interest between normal and non-normal white matter (identification task) and also to distinguish different types of lesions based on their etiology: demyelinating or ischemic (classification task). The method combines texture analysis with the use of classifiers, such as Support Vector Machine (SVM), Nearst Neighboor (1NN), Linear Discriminant Analysis (LDA) and Optimum Path Forest (OPF). Experiments with real brain MRI data showed that the proposed method is suitable to identify and classify the brain lesions. KEYWORD LIST: White Matter Hyperintensity; Brain White Matter; Magnetic Resonance Imaging; Lesions Etiology; Demyelinating; Ischemic; Texture analysis; Classifiers