Classification of 3D magnetic resonance brain images using texture measures from orthogonal planes

Abstract

In this paper, the performance of two new promising operators for the analysis of 3D textures based on feature extraction is validated. The first operator is the decimal descriptor patterns from three orthogonal planes (DDP-TOP), a new feature descriptor that considers the co-occurrences on three orthogonal planes. The second operator is the grey level co-occurrence matrix from three orthogonal planes (GLCM-TOP) which is an extension of the 2D grey level co-occurrence matrix method. In order to classify the MR images of brain into healthy and diseased, several tests are performed in the same conditions of work using the classifier multiclass support vector machines (SVMs). The local binary pattern (LBP), a best known method of texture analysis is used for comparison. Using the DDP-TOP operator, excellent experimental results are obtained that prove the robustness of our approach with respect to the noise level and to different image contrasts.