Simultaneous segmentation and bias field estimation using local fitted images. (February 2018)

• Record Type: Journal Article
• Authors: Wang, Lei; Zhu, Jianbing; Sheng, Mao; Cribb, Adriena; Zhu, Shaocheng; Pu, Jiantao
• Is Part Of: Pattern recognition. Volume 74(2018:Feb.)
• Page Start: 145
• Page End: 155
• Publication Date: 2018-02
• Subjects: Level set -- Image segmentation -- Local fitted images -- Intensity inhomogeneity -- Bias field; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.08.031
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: In this paper, a new region-based active contour model is proposed by defining a hybrid region image fitting (HRIF) energy functional based on two different local fitted images. Two different local fitted images are constructed to approximate the original image and its square version, respectively. The first fitted image is an extension version of local fitted image (LFI) defined in paper " K.H. Zhang, H.H. Song, L. Zhang, Active contours driven by local image fitting energy, Pattern Recognition, 43 (4) (2010) 1199–1206 " and called extended fitted image (EFI); the second one is originally introduced and called square fitted image (SFI). Experimental results on synthetic images and a publicly available dataset demonstrate that the proposed model has the capability of handling intensity inhomogeneity and is competent for segmenting the regions of interest and estimating the bias field. Abstract: Level set methods often suffer from boundary leakage and inadequate segmentation when used to segment images with inhomogeneous intensities. To handle this issue, a novel region-based level set method was developed, in which two different local fitted images are used to construct a hybrid region intensity fitting energy functional. This novel method enables simultaneous segmentation of the regions of interest and estimation of the bias fields from inhomogeneous images. Our experiments on both synthetic images and a publicly available dataset demonstrate the feasibility and reliability of the proposed method.