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Conference Paper/Proceeding/Abstract 110 views 14 downloads

3D interactive coronary artery segmentation using random forests and Markov random field optimization / Xianghua, Xie; Jingjing, Deng

2014 IEEE International Conference on Image Processing (ICIP), Pages: 942 - 946

Swansesa University Authors: Xianghua, Xie, Jingjing, Deng

Abstract

Coronary artery segmentation plays a vital important role in coronary disease diagnosis and treatment. In this paper, we present a machine learning based interactive coronary artery segmentation method for 3D computed tomography angiography images. We first apply vessel diffusion to reduce noise int...

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Published in: 2014 IEEE International Conference on Image Processing (ICIP)
ISBN: 978-1-4799-5751-4
ISSN: 1522-4880 2381-8549
Published: Paris, France 2015
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URI: https://cronfa.swan.ac.uk/Record/cronfa49670
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spelling 2019-04-08T10:35:57.0913595 v2 49670 2019-03-20 3D interactive coronary artery segmentation using random forests and Markov random field optimization b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 6f6d01d585363d6dc1622640bb4fcb3f 0000-0001-9274-651X Jingjing Deng Jingjing Deng true false 2019-03-20 SCS Coronary artery segmentation plays a vital important role in coronary disease diagnosis and treatment. In this paper, we present a machine learning based interactive coronary artery segmentation method for 3D computed tomography angiography images. We first apply vessel diffusion to reduce noise interference and enhance the tubular structures in the images. A few user strokes are required to specify region of interest and background. Various image features for detecting the coronary arteries are then extracted in a multi-scale fashion, and are fed into a random forests classifier, which assigns each voxel with probability values of being coronary artery and background. The final segmentation is carried through an MRF based optimization using primal dual algorithm. A connectivity component analysis is carried out as post processing to remove isolated, small regions to produce the segmented coronary arterial vessels. The proposed method requires limited user interference and achieves robust segmentation results. Conference Paper/Proceeding/Abstract 2014 IEEE International Conference on Image Processing (ICIP) 942 946 Paris, France 978-1-4799-5751-4 1522-4880 2381-8549 Coronary artery, interactive segmentation, random forests, Markov random field, primal dual algorithm 31 12 2015 2015-12-31 10.1109/ICIP.2014.7025189 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2019-04-08T10:35:57.0913595 2019-03-20T20:51:24.7444285 College of Science Computer Science Jingjing Deng 0000-0001-9274-651X 1 Xianghua Xie 0000-0002-2701-8660 2 Rob Alcock 3 Carl Roobottom 4 0049670-01042019172102.pdf icip_v3.0_JD.pdf 2019-04-01T17:21:02.2500000 Output 1706074 application/pdf Accepted Manuscript true 2019-04-01T00:00:00.0000000 true eng
title 3D interactive coronary artery segmentation using random forests and Markov random field optimization
spellingShingle 3D interactive coronary artery segmentation using random forests and Markov random field optimization
Xianghua, Xie
Jingjing, Deng
title_short 3D interactive coronary artery segmentation using random forests and Markov random field optimization
title_full 3D interactive coronary artery segmentation using random forests and Markov random field optimization
title_fullStr 3D interactive coronary artery segmentation using random forests and Markov random field optimization
title_full_unstemmed 3D interactive coronary artery segmentation using random forests and Markov random field optimization
title_sort 3D interactive coronary artery segmentation using random forests and Markov random field optimization
author_id_str_mv b334d40963c7a2f435f06d2c26c74e11
6f6d01d585363d6dc1622640bb4fcb3f
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xianghua, Xie
6f6d01d585363d6dc1622640bb4fcb3f_***_Jingjing, Deng
author Xianghua, Xie
Jingjing, Deng
format Conference Paper/Proceeding/Abstract
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description Coronary artery segmentation plays a vital important role in coronary disease diagnosis and treatment. In this paper, we present a machine learning based interactive coronary artery segmentation method for 3D computed tomography angiography images. We first apply vessel diffusion to reduce noise interference and enhance the tubular structures in the images. A few user strokes are required to specify region of interest and background. Various image features for detecting the coronary arteries are then extracted in a multi-scale fashion, and are fed into a random forests classifier, which assigns each voxel with probability values of being coronary artery and background. The final segmentation is carried through an MRF based optimization using primal dual algorithm. A connectivity component analysis is carried out as post processing to remove isolated, small regions to produce the segmented coronary arterial vessels. The proposed method requires limited user interference and achieves robust segmentation results.
published_date 2015-12-31T05:45:28Z
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