A segmentation of brain MRI images utilizing intensity and contextual information by Markov random field. (31st October 2017)

• Record Type: Journal Article
• Authors: Chen, Mingsheng; Yan, Qingguang; Qin, Mingxin
• Is Part Of: Computer assisted surgery. Volume 22(2017)Supplement 1
• Page Start: 200
• Page End: 211
• Publication Date: 2017-10-31
• Subjects: Brain MRI -- image segmentation -- maximum a posteriori Markov random field; Computer-assisted surgery -- Periodicals
• DOI: 10.1080/24699322.2017.1389398
• Languages: English
• ISSNs: 2469-9322

Abstract

Abstract: Background and objective: Image segmentation is a preliminary and fundamental step in computer aided magnetic resonance imaging (MRI) images analysis. But the performance of most current image segmentation methods is easily depreciated by noise in MRI images. A precise and anti-noise segmentation of MRI images is desired in modern medical image diagnosis. Methods: This paper presents a segmentation of MRI images which combines fuzzy clustering and Markov random field (MRF). In order to utilize gray level information sufficiently and alleviate noise disturbance, fuzzy clustering is carried out on the original image and the coarse scale image of multi-scale decomposition. The spatial constraints between neighboring pixels are modeled by a defined potential function in the MRF to reduce the effect of noise and increase the integrity of segmented regions. Spatial constraints and the gray level information refined by Fuzzy C-Means (FCM) algorithm are integrated by maximum a posteriori Markov random field (MAP-MRF). In the proposed method, the fuzzy clustering membership obtained from the original image and the coarse scale image is integrated into the single-site clique potential functions by MAP-MRF. The defined potential functions and the distance weight are introduced to model the neighborhood constraint with MRF. Results: The experiments are carried out on noised synthetic images, simulated brain MR images and real MR images. The experimental results show that the proposed method has strong robustness and satisfying performance. Meanwhile the method is compared with FCM, FGFCM and FLICM algorithms visually and statistically in the experiments. In the comparison, the proposed method has achieved the best results. In the statistical comparison, the proposed method has an average similarity index of 36.8%, 33.7%, 2.75% increase against FCM, FGFCM and FLICM. Conclusions: This paper proposes a MRI segmentation method combining fuzzy clustering and Markov random field. The method is tested in the noised image databases and comparison experiments, which shows that it is a precise and robust MRI segmentation method.