Journal
FILOMAT
Volume 32, Issue 5, Pages 1507-1512Publisher
UNIV NIS, FAC SCI MATH
DOI: 10.2298/FIL1805507S
Keywords
Fuzzy clustering; Level set methods; Medical image segmentation
Categories
Funding
- National Natural Science Foundation of China [61571176, 61511140099]
- Anhui Natural Science Foundation [1608085J04]
- International Science and Technology Cooperation Plan of Anhui Province [1503062015]
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Level set methods are a kind of general numerical analysis tools that are specialized for describing and controlling implicit interface dynamically. It receives widespread attention in medical image computing and analysis. There have been a lot of level set models designed and regularized for medical image segmentation. For the sake of simplicity and clarity, we merely concentrate on our recent works of regularizing level set methods with fuzzy clustering in this paper. It covers two most famous level set models, namely Hamilton-Jacobi functional and Mumford-Shah functional, for variational segmentation and region competition respectively. The strategies of fuzzy regularization are elaborated in detail and their applications in medical image segmentation are demonstrated with examples.
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