Non Sub-sampled Contourlet Transform Based Feature Extraction Technique for Differentiating Glioma Grades Using MRI Images

摘要

More distinguishable features can greatly improve the performance of any classification system. In this study a feature extraction method using shift and rotation-invariant non-subsampled contourlet transform (NSCT) and isotropic gray level co-occurrence matrix (GLCM) is proposed for the classification of three glioma grades (II, III and IV). The classification is done using support vector machines (SVMs). A dataset of 93 MRI brain tumor images containing three grades of glioma are classified using 10 fold cross validation scheme. The proposed method is also compared with Discrete Wavelet Transform (DWT) approach. The highest accuracy of 83.33% for grade III, sensitivity of 86.95% and specificity of 92.82% achieved in case of grade II.