文章基本信息

标题：An Improved Gaussian Mixture Model based on NonLocal Information for Brain MR Images Segmentation
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作者：Yunjie Chen ; Bo Zhao ; Jianwei Zhang 等
期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2014
卷号：7
期号：4
页码：187-194
DOI：10.14257/ijsip.2014.7.4.18
出版社：SERSC
摘要：Brain image segmentation is an important part of medical image analysis. Due to the effect of imaging mechanism, MR images usually intensity in homogeneity, which is also named as bias field. Traditional Gaussian Mixed Model (GMM) method is hard to obtain satisfied segmentation results with the effect of noise and bias field. We propose a novel model based on GMM and nonlocal information. The improved method coupled segmentation and bias field correction that can manage the bias field while segmenting the image. In order to obtain a smooth bias field, we employed the Legendre Polynomials to fit it and merged it to the EM framework. We also use the non local information to deal with the noise and preserve geometrical edges information. The results show that our method can obtain more accurate results and bias field.
关键词：MRI; GMM; Bias filed; Non local information