A Combination of Global and Local Features for Brain White Matter Lesion Classification

Abstract

In the present time, the development of medical images and the increasing use of digital images have played a crucial role in medical diagnosis. In this sense, the rapid growth of magnetic resonance images (MRI) technology increased the necessity to store, analyze and describe this amount of information more efficiently. By improving these processes, decision making for clinicians may reach a more accurate and a well-informed diagnosis. To achieve this aim, machine learning approaches have been considered as a complementary pillar of image processing. It has become essential to find efficient descriptors, which characterize the images such as texture, shape and color. Although there are many representations of known features, they are difficult to use in their unchanged forms. The proposed approach combines the global and local features. Global features are extracted using the combination of magnitude and phase features of the descriptor angular radial transform (ART). Local features are obtained through local binary pattern (LBP). Four most known machine-learning approaches are then applied (SVM, ANN, NB, KNN) on both features and the combination of both techniques yielded the best tumor detection performance.