An Efficient Multi-Scale Local Binary Fitting-Based Level Set Method for Inhomogeneous Image Segmentation
Author
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-08-05
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
An efficient level set model based on multiscale local binary fitting (MLBF) is proposed for image segmentation.
By introducing multiscale idea into the LBF model, the proposed MLBF model can effectively and efficiently segment images with intensity inhomogeneity.
In addition, by adding a reaction diffusion term into the level set evolution (LSE) equation, the regularization of the level set function (LSF) can be achieved, thus completely eliminating the time-consuming reinitialization process.
In the implementation phase, in order to greatly improve the efficiency of the numerical solution of the level set segmentation model, we introduce three strategies: The first is the additive operator splitting (AOS) solver which is used for breaking the restrictions on time step; the second is the salient target detection mechanism which is used to achieve full automatic initialization of the LSE process; the third is the sparse filed method (SFM) which is used to restrict the groups of pixels that need to be updated in a small strip region.
Under the combined effect of these three strategies, the proposed model achieves very high execution efficiency in the following aspects: contour location accuracy, speed of evolution convergence, robustness against initial contour position, and robustness against noise interference.
American Psychological Association (APA)
Wang, Dengwei. 2018. An Efficient Multi-Scale Local Binary Fitting-Based Level Set Method for Inhomogeneous Image Segmentation. Journal of Sensors،Vol. 2018, no. 2018, pp.1-17.
https://search.emarefa.net/detail/BIM-1201790
Modern Language Association (MLA)
Wang, Dengwei. An Efficient Multi-Scale Local Binary Fitting-Based Level Set Method for Inhomogeneous Image Segmentation. Journal of Sensors No. 2018 (2018), pp.1-17.
https://search.emarefa.net/detail/BIM-1201790
American Medical Association (AMA)
Wang, Dengwei. An Efficient Multi-Scale Local Binary Fitting-Based Level Set Method for Inhomogeneous Image Segmentation. Journal of Sensors. 2018. Vol. 2018, no. 2018, pp.1-17.
https://search.emarefa.net/detail/BIM-1201790
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1201790