Convex Hull in Brain Tumor Segmentation

Abstract

Tumors are the second leading cause of death. Among the tumors, brain tumors constitute one of the most complex tumor categories with a high mortality rate. Therefore, brain tumor detection and segmentation from non-invasive imaging like MRI is an important research area. Although most recent researches for brain tumor detection are focused on deep learning methods, machine learning, geometrical approaches, thresholding and hybrid models are also explored frequently. In this paper, a novel brain tumor segmentation method containing thresholding, computational geometry and heuristics is proposed. The proposed model is tested with two brain tumor datasets to show comparative results for brain tumor segmentation with thresholding, convex hull and an area heuristic. The application of different filtering on a direct convex hull model and a heuristic-based convex hull model shows that the convex area based heuristic with the convex hull approach is able to segment brain tumors more accurately than previous approaches.