A Two-Stage Image Segmentation Model for Multi-Channel Images. (12th April 2016)

• Record Type: Journal Article
• Authors: Li, Zhi; Zeng, Tieyong
• Is Part Of: Communications in computational physics. Volume 19:Number 4(2016:Apr.)
• Page Start: 904
• Page End: 926
• Publication Date: 2016-04-12
• Subjects: 52A41, -- 65K10, -- 68U40, -- 90C25, -- 90C47; Image segmentation, -- minimal surface, -- multi-channel, -- primal-dual method, -- total variation; Mathematical physics -- Data processing -- Periodicals; Physics -- Data processing -- Periodicals; 530.150285
• DOI: 10.4208/cicp.260115.200715a
• Languages: English
• ISSNs: 1815-2406

Abstract

Abstract: This paper introduces a two-stage model for multi-channel image segmentation, which is motivated by minimal surface theory. Indeed, in the first stage, we acquire a smooth solution u from a convex variational model related to minimal surface property and different data fidelity terms are considered. This minimization problem is solved efficiently by the classical primal-dual approach. In the second stage, we adopt thresholding to segment the smoothed image u . Here, instead of using K-means to determine the thresholds, we propose a more stable hill-climbing procedure to locate the peaks on the 3D histogram of u as thresholds, in the meantime, this algorithm can also detect the number of segments. Finally, numerical results demonstrate that the proposed method is very robust against noise and superior to other image segmentation approaches.