Chinese Journal of Tissue Engineering Research ›› 2011, Vol. 15 ›› Issue (13): 2408-2411.doi: 10.3969/j.issn.1673-8225.2011.13.030

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Segmentation of multiple sclerosis lesions in brain magnetic resonance images with modified fuzzy C-means algorithm

Huang Xiao, Li Bin, Feng Qian-jin   

  1. School of Biomedical Engineering, Southern Medical University, Guangzhou  510515, Guangdong Province, China
  • Received:2010-10-25 Revised:2011-02-14 Online:2011-03-26 Published:2013-10-23
  • Contact: Feng Qian-jin, Associate professor, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, Guangdong Province, China qianjinfeng08@gmail.com
  • About author:Huang Xiao★, Studying for master’s degree, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, Guangdong Province, China rebeccahuang87@ gmail.com
  • Supported by:

    the National 973 Program, No. 2010CB732505*; the National Natural Science Foundation of China, No. 30900380*

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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 10 patients 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.

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