Non-locally regularized segmentation of multiple sclerosis lesion from multi-channel MRI data |
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| Authors: | Jingjing Gao Chunming Li Chaolu Feng Mei Xie Yilong Yin Christos Davatzikos |
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| Institution: | 1. School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China;2. Center of Biomedical Image Computing and Analytics, University of PA, Philadelphia 19104, USA;3. Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, Liaoning 110819, China;4. School of Computer Science and Technology, Shandong University, Jinan, Shandong 250100, China;5. Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA |
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| Abstract: | Segmentation of multiple sclerosis (MS) lesion is important for many neuroimaging studies. In this paper, we propose a novel algorithm for automatic segmentation of MS lesions from multi-channel MR images (T1W, T2W and FLAIR images). The proposed method is an extension of Li et al.'s algorithm in 1], which only segments the normal tissues from T1W images. The proposed method is aimed to segment MS lesions, while normal tissues are also segmented and bias field is estimated to handle intensity inhomogeneities in the images. Another contribution of this paper is the introduction of a nonlocal means technique to achieve spatially regularized segmentation, which overcomes the influence of noise. Experimental results have demonstrated the effectiveness and advantages of the proposed algorithm. |
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| Keywords: | Multi-channel MR images Lesion segmentation Energy minimization Bias field estimation Nonlocal means |
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