Level Set Framework of Multi Labels Fusion for Multiple Sclerosis Lesion Segmentation
Authors: 
Zhaoxuan Gong, Wei Guo, Jia Guo, Zhenyu Zhu, Yoohwan Kim, Guodong ZhangAuthors Info & Claims
Pages 12 - 15
Abstract
Multiple sclerosis (MS) lesion segmentation is important in estimating the progress of the disease and measuring the impact of new clinical treatments. In this paper, we present a multi-label fusion embedded level set method for White Matter (WM) lesion segmentation from Multiple Sclerosis (MS) patient images. Specifically we focus on the validation of the variational level set method. Lesion segmentation is achieved by extending the level set contour which consists of a label fusion term, an image data term and a regularization term. Labels are obtained from the fuzzy C-means model and embedded into the label fusion term. To compare the performance of our method with other state-of-the-art methods, we evaluated the methods with 20 MRI datasets of MS patients. Our approach exhibits a significantly higher accuracy on segmention of WM lesions over other evaluated methods.
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Index Terms
- Level Set Framework of Multi Labels Fusion for Multiple Sclerosis Lesion Segmentation
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Published In
October 2018
166 pages
ISBN:9781450365338
DOI:10.1145/3285996
Copyright © 2018 ACM.
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- University of Electronic Science and Technology of China: University of Electronic Science and Technology of China
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Association for Computing Machinery
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Publication History
Published: 13 October 2018
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ISICDM 2018
ISICDM 2018: The 2nd International Symposium on Image Computing and Digital Medicine 2018
October 13 - 14, 2018
Chengdu, China
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