Chapter title |
A Robust Energy Minimization Algorithm for MS-Lesion Segmentation
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Chapter number | 47 |
Book title |
Advances in Visual Computing
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Published in |
Advances in visual computing : 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings. Part I. International Symposium on Visual Computing (11th : 2015 : Las Vegas, Nev.), December 2015
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DOI | 10.1007/978-3-319-27857-5_47 |
Pubmed ID | |
Book ISBNs |
978-3-31-927856-8, 978-3-31-927857-5
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Authors |
Zhaoxuan Gong, Dazhe Zhao, Chunming Li, Wenjun Tan, Christos Davatzikos |
Abstract |
The detection of multiple sclerosis lesion is important for many neuroimaging studies. In this paper, a new automatic robust algorithm for lesion segmentation based on MR images is proposed. This method takes full advantage of the decomposition of MR images into the true image that characterizes a physical property of the tissues and the bias field that accounts for the intensity inhomogeneity. An energy function is defined in term of the property of true image and bias field. The energy minimization is proposed for seeking the optimal segmentation result of lesions and white matter. Then postprocessing operations is used to select the most plausible lesions in the obtained hyperintense signals. The experimental results show that our approach is effective and robust for the lesion segmentation. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 6 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 2 | 33% |
Professor | 1 | 17% |
Unknown | 3 | 50% |
Readers by discipline | Count | As % |
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Engineering | 2 | 33% |
Medicine and Dentistry | 1 | 17% |
Unknown | 3 | 50% |