A Two-Stage Image Segmentation Method Using a Convex Variant of the Mumford-Shah Model and Thresholding
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香港中文大學研究人員

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摘要The Mumford-Shah model is one of the most important image segmentation models and has been studied extensively in the last twenty years. In this paper, we propose a two-stage segmentation method based on the Mumford-Shah model. The first stage of our method is to find a smooth solution g to a convex variant of the Mumford-Shah model. Once g is obtained, then in the second stage the segmentation is done by thresholding g into different phases. The thresholds can be given by the users or can be obtained automatically using any clustering methods. Because of the convexity of the model, g can be solved efficiently by techniques like the split-Bregman algorithm or the Chambolle-Pock method. We prove that our method is convergent and that the solution g is always unique. In our method, there is no need to specify the number of segments K (K = 2) before finding g. We can obtain any K-phase segmentations by choosing (K-1) thresholds after g is found in the first stage, and in the second stage there is no need to recompute g if the thresholds are changed to reveal different segmentation features in the image. Experimental results show that our two-stage method performs better than many standard two-phase or multiphase segmentation methods for very general images, including antimass, tubular, MRI, noisy, and blurry images.
著者Cai XH, Chan R, Zeng TY
期刊名稱SIAM Journal on Imaging Sciences
出版年份2013
月份1
日期1
卷號6
期次1
出版社SIAM PUBLICATIONS
頁次368 - 390
國際標準期刊號1936-4954
語言英式英語
關鍵詞image segmentation; Mumford-Shah model; split-Bregman; total variation
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; Computer Science, Software Engineering; COMPUTER SCIENCE, SOFTWARE ENGINEERING; Imaging Science & Photographic Technology; IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY; Mathematics; Mathematics, Applied; MATHEMATICS, APPLIED

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