A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans. (September 2018)

• Record Type: Journal Article
• Authors: Ibrahim, Rabha W.; Hasan, Ali M.; Jalab, Hamid A.
• Is Part Of: Computer methods and programs in biomedicine. Volume 163(2018)
• Page Start: 21
• Page End: 28
• Publication Date: 2018-09
• Subjects: Fractional calculus -- Wright function -- Segmentation -- Active contour -- MRI scan; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.05.031
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A new Fractional Wright Energy Function is proposed for Tumor segmentation. The proposed method minimized the energy function more than gradient-decent method. Performance is evaluated on standard multimodal brain tumor dataset (BRATS 2013). Experimental results show the effectiveness of our Energy Function for Segmentation. Abstract: Background and objectives: The MRI brain tumors segmentation is challenging due to variations in terms of size, shape, location and features' intensity of the tumor. Active contour has been applied in MRI scan image segmentation due to its ability to produce regions with boundaries. The main difficulty that encounters the active contour segmentation is the boundary tracking which is controlled by minimization of energy function for segmentation. Hence, this study proposes a novel fractional Wright function (FWF) as a minimization of energy technique to improve the performance of active contour without edge method. Method: In this study, we implement FWF as an energy minimization function to replace the standard gradient-descent method as minimization function in Chan–Vese segmentation technique. The proposed FWF is used to find the boundaries of an object by controlling the inside and outside values of the contour. In this study, the objective evaluation is used to distinguish the differences between the processed segmented images and ground truth using a set of statistical parameters; true positive, true negative, false positive, and false negative. Results: The FWF as a minimization of energy was successfully implemented on BRATS 2013 image dataset. The achieved overall average sensitivity score of the brain tumors segmentation was 94.8 ± 4.7%. Conclusions: The results demonstrate that the proposed FWF method minimized the energy function more than the gradient-decent method that was used in the original three-dimensional active contour without edge (3DACWE) method.