Weighted variational model for selective image segmentation with application to medical images. (April 2018)

• Record Type: Journal Article
• Authors: Liu, Chunxiao; Ng, Michael Kwok-Po; Zeng, Tieyong
• Is Part Of: Pattern recognition. Volume 76(2018:Apr.)
• Page Start: 367
• Page End: 379
• Publication Date: 2018-04
• Subjects: Selective segmentation -- Mumford-Shah model -- Thresholding -- Medical images -- Iterative algorithm; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.11.019
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A new variational selective segmentation model which contains both the edge information and the marker points is proposed. Existence and uniqueness of the minimizer can be guaranteed. Fast and efficient numerical algorithm can be developed and the linear convergence can be proved under mild conditions. Numerical experiments and comparisons for some challenging medical images demonstrate the usefulness of the proposed method. Abstract: Selective image segmentation is an important topic in medical imaging and real applications. In this paper, we propose a weighted variational selective image segmentation model which contains two steps. The first stage is to obtain a smooth approximation related to Mumford-Shah model to the target region in the input image. Using weighted function, the approximation provides a larger value for the target region and smaller values for other regions. In the second stage, we make use of this approximation and perform a thresholding procedure to obtain the object of interest. The approximation can be obtained by the alternating direction method of multipliers and the convergence analysis of the method can be established. Experimental results for medical image selective segmentation are given to demonstrate the usefulness of the proposed method. We also do some comparisons and show that the performance of the proposed method is more competitive than other testing methods.