A hybrid of active contour model and convex hull for automated brain tumor segmentation in multimodal MRI

Abstract

Segmenting tumor automatically in human brain Magnetic Resonance (MR) images is challenging because of uneven, irregular and unstructured size and shape of the tumor. This paper proposes an automated two stage brain tumor segmentation method. In the first stage, coarse estimation of the brain tumor is carried out using convex hull approach. The coarse estimate thus obtained is employed as the initialization for the active contour model applied in the second stage thereby eliminating the need of human intervention. Multiscale Harris energy is estimated at different levels to identify high-energy regions and thereafter constructing convex hull over the selected key-points in order to detect the abnormality in the input MR images. The proposed method is applied to 2-d axial images of fluid attenuated inversion recovery (FLAIR) and post-contrast T1-weighted (T1c) MRI images from the brain tumor segmentation benchmark challenge 2015 (BRATS2015) dataset. Different sub-compartments of the tumor such as enhanced tumor, edema, and necrosis are segmented and in addition combined in the form of tumor core, complete tumor, and enhanced tumor labels. The proposed method is evaluated in terms of Dice Similarity Coefficient (DSC), Sensitivity and Positive Predictive Value (PPV). Average DSC score of 81% for brain tumor core, 92% for complete brain tumor, and 83% for enhanced brain tumor are achieved which is better than several state-of-the-art methods.