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
Main Authors: Jin, Mingxin, Hao, Dongdong, Ding, Song, Qin, Binjie
Format: Article
Language:English
Subjects:
local-to-global adaptive thresholding
radon-like filtering
robust principal component analysis
vessel segmentation
x-ray coronary angiography
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creator Jin, Mingxin
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description 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.
doi_str_mv 10.1088/1361-6560/aad8e0
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subjects local-to-global adaptive thresholding
radon-like filtering
robust principal component analysis
vessel segmentation
x-ray coronary angiography
title Low-rank and sparse decomposition with spatially adaptive filtering for sequential segmentation of 2D+t vessels
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