计算机工程2012，Vol.38Issue(17)：242-244,248,4.DOI:10.3969/j.issn.1000-3428.2012.17.065
基于改进高斯混合模型的MR图像分割
MR Image Segmentation Based on Improved Gaussian Mixed Model
摘要
Abstract
Traditional Gaussian Mixed Model(GMM) seriously depended on the initial value and it is easily affected by the bias field and noise when segmenting Magnatic Resonance(MR) image. Aiming at this problem, this paper proposes a kind of improved Gaussian Mixed Model(GMM) based on patch information, which can manage the bias field and noise while segmenting the image. It utilizes the Fuzzy C-Means(FCM) method to optimize the initial value and accelerates the convergence. In order to obtain a smooth bias field, it employs the Legendre Polynomials to fit it and merges it to the EM framework. This paper introduces neighbor information of each point to reduce the effect of noise so that slender topological objects can be reserved. Experiments results show that the model can bring out bias field and has good segmentation results.关键词
核磁共振成像/图像分割/高斯混合模型/偏移场/区域信息Key words
Magnatic Resonance Imaging(MRI)/ image segmentation/ Gaussian Mixed Model(GMM)/ bias field/ patch information分类
信息技术与安全科学引用本文复制引用
陈亮,陈允杰..基于改进高斯混合模型的MR图像分割[J].计算机工程,2012,38(17):242-244,248,4.基金项目
国家自然科学基金青年基金资助项目(61003209) （61003209）
江苏省自然科学基金资助项目(BK2011824) （BK2011824）
江苏省高校自然科学研究基金资助项目(10KJB520012) （10KJB520012）
南京信息工程大学八期教改课题基金资助项目(N1885011039) （N1885011039）
