Level set method for image segmentation based on local variance and improved intensity inhomogeneity model. Issue 12 (1st December 2016)

• Record Type: Journal Article
• Authors: Li, Zhongguo; Zeng, Lei; Xu, Yifu; Chen, Jian; Yan, Bin
• Is Part Of: IET image processing. Volume 10:Issue 12(2016)
• Page Start: 1007
• Page End: 1016
• Publication Date: 2016-12-01
• Subjects: image segmentation -- Gaussian distribution -- set theory; level set method -- image segmentation -- intensity inhomogeneity -- Gaussian distribution -- local intensity descriptor -- transformed domain -- level set energy function -- image term -- local variance -- level set evolution; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.0352
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : This study proposes an improved level set method for segmenting images with intensity inhomogeneity. One of the improvements is to consider the difference between an original image and an estimated image without bias field in the image model. Apart from using this difference, Gaussian distribution with means and variance is utilised as the local intensity descriptor to map the original image into another domain so the object and the background can be better separated in the transformed domain. Then, an improved level set energy function that combines the image term, local variance, and the above difference is defined. The minimisation of the function can be processed by level set evolution. The proposed method is compared with existing methods, and experiments on both synthetic and real images demonstrate that authors' method has superior performance.