Brain Tumor Segmentation on MRI using a GVF Snake Model

One objective of neuroimaging is to study the brain structures of healthy and pathological subjects. The considerable variation of structures requires implementing specific study methods, often addressed through Magnetic Resonance Imaging (MRI). Currently, segmentation constitutes a big step in the treatment and interpretation of medical images. Many approaches have been proposed for segmentation tasks. Accordingly, several methods have resulted. Among these methods, we find the Gradient Vector Flow GVF Snake Model method, which presents the subject of our work. The basic idea of GVF Snake Model is to evolve an initial contour according to specific equations to reach the desired object boundaries; this method helps us get a closed and skinny contour (one pixel of thickness). We opted for a contour segmentation method to refine the initial segmentation; parametric deformable models have been successfully applied to tumour segmentation, so we use the active contours guided by GVF with constraints from spatial relations. The results show that our method significantly improves brain tumour extraction and segmentation. Through this humble search, we can finally look forward to the generalization of this method on 3D space to have the global information of the pathology to help the practitioner in the diagnosis.