Automatic brain tumor segmentation for a computer‐aided diagnosis system. Issue 4 (13th May 2021) - Abdelaziz, Mohammed; Cherfa, Yazid; Cherfa, Assia; Alim‐Ferhat, Fatiha
Abstract: Brain structure segmentation, including tumors, in medical imaging has become a necessity to help neurologists correctly diagnose patients' conditions. The complexity of these structures requires the implementation of automatic segmentation methods, often developed by magnetic resonance imaging. This study aims to design and implement an automatic system for detecting and localizing tumor regions by combining three different methods. Firstly, the region of interest, that is, the pixels belonging to the tumor, is detected using Random Forest's algorithm, while the rest of the pixels of the image are considered to belong to the background. Thus, the tumor and background seeds are obtained, automatically, for segmentation using the Graph Cut method. This segmentation allows to obtain the initial contour, for the level set (LVS) segmentation, which refine the previous segmentation. The proposed method was validated on the Multimodal Brain Tumor Segmentation Challenge (BRATS) database (http://braintumorsegmentation.org ; 2015).