Fractional Crank-Nicolson finite difference method for benign brain tumor detection and segmentation

Abstract

Benign brain tumor is early stage of cancer in tumor development life cycle. Its detection is hard and most challenging task due to low variability with its surrounding non-cancerous tumor cells. Image segmentation is used as a primary tool in brain tumor detection algorithms to segment the tumorous region. It has been observed that the available methods such as region-based, watershed-based method, cluster-based method and contour and shape-based methods are not able to find such low-intensity variational regions (i.e. benign brain tumor). Current work proposes a novel fractional method for finding such a low intensity variational region. The proposed method uses alternate direction implicit finite difference scheme. The performance analysis has been done on three-dimensional numerical head phantom and BRATS dataset. Results obtained by the proposed tumor detection and segmentation method have been compared with the popular tumor detection and segmentation methods. Hausdorff distance, Jaccard similarity index and Dice coefficient have been used for quantitative comparative performance analysis.