A Segmentation Method for Multiple Sclerosis White Matter Lesions on Conventional Magnetic Resonance Imaging Based on Kernel Fuzzy Clustering

Abstract:

Multiple sclerosis (MS) is a chronic disease that affects the central nervous system and impacts substantially on patients. MS lesions are visible in conventional magnetic resonance imaging (cMRI) and the automatic segmentation of MS lesions enables the efficient processing of images for research studies and in clinical trials. A new method for the segmentation of MS white matter lesions (WML) on cMRI is presented in this paper. Firstly the Kernel Fuzzy C-Means Clustering (KFCM) is applied to the preprocessed T1-weight (T1-w) image for extracting the white matter (WM) region. Then region growing algorithm is applied to the WM region image to make a binary mask which is then superimposed on the corresponding T2-weight (T2-w) image to yield a masked image only containing WM structures and lesions. The KFCM is then reapplied to the masked image to obtain MS lesions. The testing results show that the proposed method is able to segment WML on cMRI automatically and effectively.

• Periodical: Applied Mechanics and Materials (Volume 339)
• Pages: 361-365
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.339.361
• Online since: July 2013
• Authors: Yan Xiang, Jian Feng He, Lei Ma, San Li Yi, Jia Ping Xu
• Keywords: Conventional Magnetic Resonance Image, Kernel Fuzzy C-Means Clustering, Multiple Sclerosis, Segmentation

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