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dc.contributor.authorPan, H.
dc.contributor.authorLiu, Wan-Quan
dc.contributor.authorLi, L.
dc.contributor.authorZhou, Guanglu
dc.date.accessioned2019-02-19T04:16:58Z
dc.date.available2019-02-19T04:16:58Z
dc.date.created2019-02-19T03:58:21Z
dc.date.issued2019
dc.identifier.citationPan, H. and Liu, W. and Li, L. and Zhou, G. 2019. A novel level set approach for image segmentation with landmark constraints. Optik. 182: pp. 257-268.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/74448
dc.identifier.doi10.1016/j.ijleo.2019.01.009
dc.description.abstract

© 2019 Elsevier GmbH Level set methods are widely used in image segmentation and shape analysis. However, most of the current research focuses on fast computational algorithms, initial value selection, and practical applications in various areas. To the best of our knowledge, no research has been conducted on segmentation with level set models where the segmentation contours have to pass through some prior landmark points. In this paper, we propose a new variational model for image segmentation based on the classical Chan-Vese model for this new problem. The new model incorporates prior landmarks information as constraints in a formulated optimization problem. Then, we investigate the theoretical solvability of the new model and design a new algorithm based on the Split Bregman algorithm for numerical implementation. Finally, we conduct some segmentation experiments on gray images and compare with the original Chan-Vese model. The obtained results show many advantages of the proposed model with broad applications. Additionally, we give some critical analysis of the proposed algorithm.

dc.titleA novel level set approach for image segmentation with landmark constraints
dc.typeJournal Article
dcterms.source.volume182
dcterms.source.startPage257
dcterms.source.endPage268
dcterms.source.issn0030-4026
dcterms.source.titleOptik
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available


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