Adaptive multilayer level set method for segmenting images with intensity inhomogeneity. Issue 10 (15th July 2019)

• Record Type: Journal Article
• Authors: Huang, Guopeng; Ji, Hongbing; Zhang, Wenbo; Zhu, Zhigang
• Is Part Of: IET image processing. Volume 13:Issue 10(2019)
• Page Start: 1714
• Page End: 1724
• Publication Date: 2019-07-15
• Subjects: pattern clustering -- minimisation -- image segmentation -- set theory; bias correction -- fixed scale clustering criterion -- adaptive multilayer level set method -- severe intensity inhomogeneity -- improved global adaptive scale operator -- local adaptive scale operator -- adaptive multilayer level set structure -- designed scale operators -- multilayer structure -- appropriate candidate scales -- dual minimisation method -- severe inhomogeneous image segmentation -- gentle intensity inhomogeneity -- hybrid bias field initialisation procedure -- clustering kernel function; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0315
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : The level set method based on bias correction can segment images with gentle intensity inhomogeneity effectively. However, most level set methods fail to segment severe inhomogeneous images due to the use of fixed scale clustering criterion. To deal with this problem, an adaptive multilayer level set method is proposed to segment images with severe intensity inhomogeneity. First, an improved global adaptive scale operator and a local adaptive scale operator are designed to adaptively adjust the scale of clustering kernel function according to the degree of intensity inhomogeneity. Then, an adaptive multilayer level set structure is constructed with the two designed scale operators. The number of layers and the scale of each layer in the multilayer structure are adaptively determined based on the degree of intensity inhomogeneity, which not only provides appropriate candidate scales in each pixel but also allows the model to detect global contrast information. With the dual minimisation method, image segmentation and bias correction can be achieved simultaneously. In addition, a hybrid bias field initialisation procedure is proposed to enhance the robustness of the proposed method. Experimental results demonstrate the effectiveness and robustness of the proposed method in segmenting images with intensity inhomogeneity.