标题:A Modified Fuzzy C-Means Algorithm for Brain MR Image Segmentation and Bias Field Correction
作者:Deng, Wen-Qian; Li, Xue-Mei; Gao, Xifeng; Zhang, Cai-Ming
通讯作者:Li, Xue-Mei(xmli@sdu.edu.cn)
作者机构:[Deng, W.-Q] School of Computer Science and Technology, Shandong University, Jinan, 250101, China;[ Li, X.-M] School of Computer Science and Technolog 更多
会议名称:4th International Conference on Computational Visual Media (CVM)
会议日期:APR 06-08, 2016
来源:计算机科学技术学报(英文版)
出版年:2016
卷:31
期:3
页码:501-511
DOI:10.1007/s11390-016-1643-5
关键词:image segmentation;fuzzy c-means;bias field correction;anti-noise
摘要:In quantitative brain image analysis, accurate brain tissue segmentation from brain magnetic resonance image (MRI) is a critical step. It is considered to be the most important and di?cult issue in the field of medical image processing. The quality of MR images is influenced by partial volume effect, noise, and intensity inhomogeneity, which render the segmentation task extremely challenging. We present a novel fuzzy c-means algorithm (RCLFCM) for segmentation and bias field correction of brain MR images. We employ a new gray-difference coe?cient and design a new impact factor to measure the effect of neighbor pixels, so that the robustness of anti-noise can be enhanced. Moreover, we redefine the objective function of FCM (fuzzy c-means) by adding the bias field estimation model to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. We also construct a new spatial function by combining pixel gray value dissimilarity with its membership, and make full use of the space information between pixels to update the membership. Compared with other state-of-the-art approaches by using similarity accuracy on synthetic MR images with different levels of noise and intensity inhomogeneity, the proposed algorithm generates the results with high accuracy and robustness to noise.
收录类别:CPCI-S;EI;CSCD;SCOPUS;SCIE
WOS核心被引频次:2
Scopus被引频次:4
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84969983909&doi=10.1007%2fs11390-016-1643-5&partnerID=40&md5=f4225aef087e065caf99c30ea745a533
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