Convex‐relaxed active contour model based on localised kernel mapping. Issue 11 (4th October 2017)

• Record Type: Journal Article
• Authors: Cui, Wenchao; Gong, Guoqiang; Lu, Ke; Sun, Shuifa; Dong, Fangmin
• Is Part Of: IET image processing. Volume 11:Issue 11(2017)
• Page Start: 976
• Page End: 985
• Publication Date: 2017-10-04
• Subjects: image segmentation -- minimisation; convex‐relaxed active contour model -- localised kernel mapping -- intensity inhomogeneity -- intensity‐based segmentation -- noisy image segmentation -- localised KM method -- LKM energy functional -- energy functional minimisation -- convex‐relaxed problem -- optimisation -- split Bregman method -- two‐phase segmentation -- multiphase segmentation; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0132
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Intensity inhomogeneity is one of the major obstacles for intensity‐based segmentation in many applications. The recently proposed kernel mapping (KM) method has exhibited excellent performance on segmenting various types of noisy images while it is not effective to handle intensity inhomogeneity. To overcome this drawback, this study presents a localised KM (LKM) method based on the fact that intensity inhomogeneity can be ignored in a local neighbourhood. The authors' method first reconstructs the KM formulation of image segmentation in a neighbourhood of each pixel, and then such formulations for all pixels can be integrated together to derive the LKM energy functional. Minimisation of the energy functional is implemented by solving an equivalent convex‐relaxed problem whose optimisation can be quickly achieved via the split Bregman method. Experimental results on two‐phase segmentation and multiphase segmentation demonstrate competitive performance of the LKM method in the presence of intensity inhomogeneity and severe noise.