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Vessel segmentation in medical imaging using a tight-frame-based algorithm

Vessel segmentation in medical imaging using a tight-frame-based algorithm
Vessel segmentation in medical imaging using a tight-frame-based algorithm
Tight-frame, a generalization of orthogonal wavelets, has been used successfully in various problems in image processing, including inpainting, impulse noise removal, and superresolution image restoration. Segmentation is the process of identifying object outlines within images. There are quite a few efficient algorithms for segmentation such as model-based approaches, pattern recognition techniques, tracking-based approaches, and artificial intelligence-based approaches. In this paper, we propose applying the tight-frame approach to automatically identify tube-like structures in medical imaging, with the primary application of segmenting blood vessels in magnetic resonance angiography images. Our method iteratively refines a region that encloses the potential boundary of the vessels. At each iteration, we apply the tight-frame algorithm to denoise and smooth the potential boundary and sharpen the region. The cost per iteration is proportional to the number of pixels in the image. We prove that the iteration converges in a finite number of steps to a binary image whereby the segmentation of the vessels can be done straightforwardly. Numerical experiments on synthetic and real two-dimensional (2D) and three-dimensional (3D) images demonstrate that our method is more accurate when compared with some representative segmentation methods, and it usually converges within a few iterations. © 2013 Society for Industrial and Applied Mathematics.
Automatic image segmentation, Medical imaging, Tight-frame, Wavelet transform
464-486
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Chan, Raymond
9185af9b-f073-4e3d-8f22-1dac8d28db58
Morigi, Serena
6ec66b6d-8e27-43bf-84ec-c5069f73e8a5
Sgallari, Fiorella
31472b49-c051-4ee0-ac56-915034c4af74
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Chan, Raymond
9185af9b-f073-4e3d-8f22-1dac8d28db58
Morigi, Serena
6ec66b6d-8e27-43bf-84ec-c5069f73e8a5
Sgallari, Fiorella
31472b49-c051-4ee0-ac56-915034c4af74

Cai, Xiaohao, Chan, Raymond, Morigi, Serena and Sgallari, Fiorella (2013) Vessel segmentation in medical imaging using a tight-frame-based algorithm. SIAM Journal on Imaging Sciences, 6 (1), 464-486. (doi:10.1137/110843472).

Record type: Article

Abstract

Tight-frame, a generalization of orthogonal wavelets, has been used successfully in various problems in image processing, including inpainting, impulse noise removal, and superresolution image restoration. Segmentation is the process of identifying object outlines within images. There are quite a few efficient algorithms for segmentation such as model-based approaches, pattern recognition techniques, tracking-based approaches, and artificial intelligence-based approaches. In this paper, we propose applying the tight-frame approach to automatically identify tube-like structures in medical imaging, with the primary application of segmenting blood vessels in magnetic resonance angiography images. Our method iteratively refines a region that encloses the potential boundary of the vessels. At each iteration, we apply the tight-frame algorithm to denoise and smooth the potential boundary and sharpen the region. The cost per iteration is proportional to the number of pixels in the image. We prove that the iteration converges in a finite number of steps to a binary image whereby the segmentation of the vessels can be done straightforwardly. Numerical experiments on synthetic and real two-dimensional (2D) and three-dimensional (3D) images demonstrate that our method is more accurate when compared with some representative segmentation methods, and it usually converges within a few iterations. © 2013 Society for Industrial and Applied Mathematics.

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More information

Published date: 26 February 2013
Keywords: Automatic image segmentation, Medical imaging, Tight-frame, Wavelet transform

Identifiers

Local EPrints ID: 438606
URI: http://eprints.soton.ac.uk/id/eprint/438606
PURE UUID: 1ef39b17-d980-484a-a6f3-e0fd02ccb0dc

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Date deposited: 18 Mar 2020 17:32
Last modified: 19 May 2020 17:02

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Contributors

Author: Xiaohao Cai
Author: Raymond Chan
Author: Serena Morigi
Author: Fiorella Sgallari

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