Efficient and robust segmentation and correction model for medical images. Issue 12 (19th September 2019)

• Record Type: Journal Article
• Authors: Yang, Yunyun; Jia, Wenjing
• Is Part Of: IET image processing. Volume 13:Issue 12(2019)
• Page Start: 2245
• Page End: 2254
• Publication Date: 2019-09-19
• Subjects: minimisation -- image segmentation -- medical image processing; robust segmentation -- correction model -- medical images -- segmentation technology -- severe intensity inhomogeneity -- accurate segmentation difficult -- efficient contour model -- robust active contour model -- simultaneous image segmentation -- segment images -- homogeneous correction images -- synthetic images -- inhomogeneous images; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0698
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Accurate segmentation of medical images plays a very important role in clinical diagnosis so that the segmentation technology for medical images attracts more and more attention. However, most medical images usually suffer from severe intensity inhomogeneity and make accurate segmentation difficult. In this study, the authors propose an efficient and robust active contour model for simultaneous image segmentation and correction. The proposed model not only can accurately segment images with severe intensity inhomogeneity and serious noise but also can eliminate the intensity varying information to get the homogeneous correction images. They first present the level set formulation of the two‐phase model, which is then extended to the multi‐phase formulation. The split Bregman method is applied to efficiently minimise the proposed energy functionals. The proposed model is tested with lots of synthetic images and medical images with promising results. Experimental results demonstrate that the proposed model can accurately segment and correct the inhomogeneous images with serious noise. Quantitative comparison results of the proposed model and other models illustrate the proposed model is more accurate and more efficient. What's more, the proposed model not only is insensitive to the initial contour, but also is robust to the noise.