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Low-rank and sparse decomposition with spatially adaptive filtering for sequential segmentation of 2D+t vessels
This letter proposes to extract contrast-filled vessels from overlapped noisy complex backgrounds in an x-ray coronary angiogram image sequence using low-rank and sparse decomposition. A refined vessel segmentation is finally achieved by implementing a radon-like feature filtering plus local-to-glob...
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Published in: | Physics in medicine & biology 2018-08, Vol.63 (17), p.17LT01-17LT01 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | This letter proposes to extract contrast-filled vessels from overlapped noisy complex backgrounds in an x-ray coronary angiogram image sequence using low-rank and sparse decomposition. A refined vessel segmentation is finally achieved by implementing a radon-like feature filtering plus local-to-global adaptive thresholding to tackle the spatially varying noisy residuals in the extracted vessels. Based on real and synthetic XCA data, the experiment results demonstrate the superiority of the proposed method over the state-of-the-art methods. |
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ISSN: | 0031-9155 1361-6560 1361-6560 |
DOI: | 10.1088/1361-6560/aad8e0 |