中国组织工程研究与临床康复2011，Vol.15Issue(13)：2408-2411,4.DOI:10.3969/j.issn.1673-8225.2011.13.030
改进模糊C-均值分割算法在多发性硬化症MR脑部图像中的应用
Segmentation of multiple sclerosis lesions in brain magnetic resonance images with modified fuzzy C-means algorithm
摘要
Abstract
BACKGROUND: Brain magnetic resonance image is a non-texture image, characterized as piecewise constant for the gray value of MR images. Therefore, the gray value in clustering process has tended to relatively close in the same area.OBJECTIVE: To find a modified fuzzy C-means (FCM) algorithm method to segment the multiple sclerosis (MS) automatically that can support a tool to confirm MS easily.METHODS: A novel modified FCM framework is proposed by filtering membership data sets in the iterate process of FCM. The proposed algorithm denoise by making use of the property that the probability of the neighboring pixels which belong to the same cluster are similar.RESULTS AND CONCLUSION: We test our method on brain MR T1 and T2 fluid-attenuated inversion recovery images of 10patients with MS. The testing experiments on brain MR images show that the proposed algorithm is able to segment the images correctly, which is important to assist the diagnosis of MS in clinic.关键词
图像分割/改进模糊C-均值算法/多发性硬化症/MR图像/辅助诊断分类
医药卫生引用本文复制引用
黄骁,李彬,冯前进..改进模糊C-均值分割算法在多发性硬化症MR脑部图像中的应用[J].中国组织工程研究与临床康复,2011,15(13):2408-2411,4.基金项目
国家973项目(2010CB732505) （2010CB732505）
国家自然科学青年基金(30900380). （30900380）
