Multi-resolution transforms-based hybrid feature extraction technique for differentiating glioma grades

Abstract

The field of medical image classification has been one of the most attention-gaining research areas in the recent times due to the increasing demand for an efficient tool that can help doctors in making quick and correct diagnoses. In this paper, a hybrid feature extraction technique is proposed, which is based on discrete wavelet transform (DWT), non-subsampled contourlet transform (NSCT) and isotropic gray level co-occurrence matrix (GLCM) for the categorization of grade II, III, and IV gliomas. The proposed method was applied on a dataset of 93 MRI brain images containing three glioma grades (23 grade II, 45 grade III, and 25 grade IV). The efficiency of proposed methodology is evaluated in terms of classification accuracy, sensitivity and specificity. The highest accuracy of [Formula: see text] for grade III, sensitivity of [Formula: see text] and specificity of [Formula: see text] were achieved in case of grade II.