Quantitative texture analysis for Glioblastoma phenotypes discrimination

Abstract

A quantitative texture analysis for discriminating GBM phenotypes in brain magnetic resonance (MR) images is proposed. GBM phenotypes captured using semi-automatic segmentation based on 3D Slicer Scripts. Segmentation was applied on the registered images considered the T1-Weighted and FLAIR sequence. Texture feature has been extracted from the gray level co-occurrence matrix (GLCM) based on GBM phenotypes. Feature vectors are then used in training a minimum distance classifier based on Mahalanobis distance metric. Simulation results for 13 patients show the highest accuracy of 67% based on the feature extraction from GLCM with offset =1 and 8 phases. Preliminary texture analysis demonstrated that the texture feature based on the GLCM is promising to distinguish GBM phenotypes.