Brain MR Image Segmentation and Bias Field Estimation Using Coherent Local and Non-local Spatial Constraints
作者单位：Academy of Information Science and Engineering Northeastern University
会议名称：《第25届中国控制与决策会议》
主办单位：IEEE;NE Univ;IEEE Ind Elect Chapter;IEEE Harbin Sect Control Syst Soc Chapter;Guizhou Univ;IEEE Control Syst Soc;Syst Engn Soc China;Chinese Assoc Artificial Intelligence;Chinese Assoc Automat;Tech Comm Control Theory;Chinese Assoc Aeronaut;Automat Control Soc;Chinese Assoc Syst Simulat;Simulat Methods & Modeling Soc;Intelligent Control & Management Soc
会议日期：2013年
学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by Fundamental Research Funds for the Central Universities under grant N110404003
关 键 词：Bias field fuzzy clustering MR image segmentation non-local constraint coherent local constraint
摘 要：Clinical brain MR images usually contain noise and bias field （BF）, which make the brain tissue segmentations difficult. Most of the current segmentation methods only focus on one unfavorable factor. The Coherent local intensity clustering algorithm （CLIC） algorithm proposed recently is good at dealing with the BF problem in images, but it has a poor anti-noise ability, for it doesn’t consider non-local spatial constraint. In this paper, taking care of all these unfavorable factors simultaneously, we introduce the non-local spatial constraint into CLIC algorithm for brain MR image segmentations. Therefore, the proposed algorithm drives by both the coherent local and non-local spatial constraints. The coherent local information ensures the smoothness of the bias field estimation and the non-local spatial information reduces the noise effect during the segmentation. The proposed method has been successfully applied to brain MR images, and experiment results show that this method has stronger anti-noise property, smoother bias field estimation and higher segmentation precision than other reported fuzzy clustering algorithms.