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Conference Paper/Proceeding/Abstract 958 views 226 downloads

3D interactive coronary artery segmentation using random forests and Markov random field optimization

Jingjing Deng, Xianghua Xie Orcid Logo, Rob Alcock, Carl Roobottom

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

Swansea University Authors: Jingjing Deng, Xianghua Xie Orcid Logo

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DOI (Published version): 10.1109/ICIP.2014.7025189

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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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 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.
Keywords: Coronary artery, interactive segmentation, random forests, Markov random field, primal dual algorithm
College: Faculty of Science and Engineering
Start Page: 942
End Page: 946

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