A new method based on Dempster–Shafer theory and fuzzy c-means for brain MRI segmentation. (10th September 2015)

• Record Type: Journal Article
• Authors: Liu, Jie; Lu, Xi; Li, Yunpeng; Chen, Xiaowu; Deng, Yong
• Is Part Of: Measurement science & technology. Volume 26:Number 10(2015:Oct.)
• Publication Date: 2015-09-10
• Subjects: Dempster–Shafer theory -- MRI image segmentation -- fuzzy cluster -- data fusion; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/0957-0233/26/10/105402
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: In this paper, a new method is proposed to decrease sensitiveness to motion noise and uncertainty in magnetic resonance imaging (MRI) segmentation especially when only one brain image is available. The method is approached with considering spatial neighborhood information by fusing the information of pixels with their neighbors with Dempster–Shafer (DS) theory. The basic probability assignment (BPA) of each single hypothesis is obtained from the membership function of applying fuzzy c-means (FCM) clustering to the gray levels of the MRI. Then multiple hypotheses are generated according to the single hypothesis. Then we update the objective pixel's BPA by fusing the BPA of the objective pixel and those of its neighbors to get the final result. Some examples in MRI segmentation are demonstrated at the end of the paper, in which our method is compared with some previous methods. The results show that the proposed method is more effective than other methods in motion-blurred MRI segmentation.