Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/103295
Title: Frame based segmentation for medical images
Authors: Dong, B.
Chien, A.
Shen, Z. 
Keywords: Image segmentation
Level set method
Sparse approximation
Tight frames
Issue Date: Jun-2011
Citation: Dong, B.,Chien, A.,Shen, Z. (2011-06). Frame based segmentation for medical images. Communications in Mathematical Sciences 9 (2) : 551-559. ScholarBank@NUS Repository.
Abstract: Medical image segmentation is an important but difficult problem that attracts tremendous attention from researchers in various fields. In this paper, we propose a frame based model, as well as a fast implementation, for general medical image segmentation problems. Our model combines ideas of the frame based image restoration model of [J. Cai, S. Osher, and Z. Shen, Multiscale Modeling and Simulation: A SIAM Interdisciplinary Journal, 8(2), 337-369, 2009] with ideas of the total variation based segmentation model of [T. Chan and L. Vese, Scale-Space Theories in Computer Vision, 141-151, 1999], [T. Chan and L. Vese, IEEE Transactions on image processing, 10(2), 266-277, 2001], [T. Chan, S. Esedoglu and M. Nikolova, ALGORITHMS, 66(5), 1632-1648], and [X. Bresson, S. Esedoglu, P. Vandergheynst, J. Thiran and S. Osher, Journal of Mathematical Imaging and Vision, 28(2), 151-167, 2007]. Numerical experiments show that the proposed frame based model outperforms the total variation based model in terms of capturing key features of biological structures. Successful segmentations of blood vessels and aneurysms in 3D CT angiography images are also presented. © 2011 International Press.
Source Title: Communications in Mathematical Sciences
URI: http://scholarbank.nus.edu.sg/handle/10635/103295
ISSN: 15396746
Appears in Collections:Staff Publications

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