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Automatic segmentation of cross-sectional coronary arterial images

Ehab Essa, Xianghua Xie Orcid Logo

Computer Vision and Image Understanding, Volume: 165, Pages: 97 - 110

Swansea University Author: Xianghua Xie Orcid Logo

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Abstract

We present a novel approach to segment coronary cross-sectional images acquired using catheterization imaging techniques, i.e. intra-vascular ultrasound (IVUS) and optical coherence tomography (OCT). The proposed approach combines cross-sectional segmentation with longitudinal tracking in order to t...

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Published in: Computer Vision and Image Understanding
ISSN: 10773142
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa36719
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first_indexed 2017-11-12T14:08:51Z
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spelling 2021-01-28T16:09:27.9152689 v2 36719 2017-11-12 Automatic segmentation of cross-sectional coronary arterial images b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2017-11-12 SCS We present a novel approach to segment coronary cross-sectional images acquired using catheterization imaging techniques, i.e. intra-vascular ultrasound (IVUS) and optical coherence tomography (OCT). The proposed approach combines cross-sectional segmentation with longitudinal tracking in order to tackle various forms of imaging artifacts and to achieve consistent segmentation. A node-weighted directed graph is constructed on two consecutive cross-sectional frames with embedded shape constraints within individual cross-sections or frames and between consecutive frames. The intra-frame constraints are derived from a set of training samples and are embedded in both graph construction and its cost function. The inter-frame constraints are imposed by tracking the borders of interest across multiple frames. The coronary images are transformed from Cartesian coordinates to polar coordinates. Graph partition can then be formulated as searching an optimal interface in the node-weighted directed graph without user initialization. It also allows efficient parametrization of the border using radial basis function (RBF) and thus reduces the tracking of a large number of border points to a very few RBF centers. Moreover, we carry out supervised column-wise tissue classification in order to automatically optimize the feature selection. Instead of empirically assigning weights to different feature detectors, we dynamically and automatically adapt those weighting depending on the tissue compositions in each individual column of pixels. Journal Article Computer Vision and Image Understanding 165 97 110 10773142 Medical Image Analysis, IVUS, OCT, Graph cut, Combinatorial Optimisation, Image Segmentation. 31 12 2017 2017-12-31 10.1016/j.cviu.2017.11.004 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2021-01-28T16:09:27.9152689 2017-11-12T11:51:51.3450360 Ehab Essa 1 Xianghua Xie 0000-0002-2701-8660 2 0036719-12112017115858.pdf eexx_cviu_v2v2.pdf 2017-11-12T11:58:58.8530000 Output 8675349 application/pdf Accepted Manuscript true 2018-11-15T00:00:00.0000000 Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND). true eng
title Automatic segmentation of cross-sectional coronary arterial images
spellingShingle Automatic segmentation of cross-sectional coronary arterial images
Xianghua Xie
title_short Automatic segmentation of cross-sectional coronary arterial images
title_full Automatic segmentation of cross-sectional coronary arterial images
title_fullStr Automatic segmentation of cross-sectional coronary arterial images
title_full_unstemmed Automatic segmentation of cross-sectional coronary arterial images
title_sort Automatic segmentation of cross-sectional coronary arterial images
author_id_str_mv b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Xianghua Xie
author2 Ehab Essa
Xianghua Xie
format Journal article
container_title Computer Vision and Image Understanding
container_volume 165
container_start_page 97
publishDate 2017
institution Swansea University
issn 10773142
doi_str_mv 10.1016/j.cviu.2017.11.004
document_store_str 1
active_str 0
description We present a novel approach to segment coronary cross-sectional images acquired using catheterization imaging techniques, i.e. intra-vascular ultrasound (IVUS) and optical coherence tomography (OCT). The proposed approach combines cross-sectional segmentation with longitudinal tracking in order to tackle various forms of imaging artifacts and to achieve consistent segmentation. A node-weighted directed graph is constructed on two consecutive cross-sectional frames with embedded shape constraints within individual cross-sections or frames and between consecutive frames. The intra-frame constraints are derived from a set of training samples and are embedded in both graph construction and its cost function. The inter-frame constraints are imposed by tracking the borders of interest across multiple frames. The coronary images are transformed from Cartesian coordinates to polar coordinates. Graph partition can then be formulated as searching an optimal interface in the node-weighted directed graph without user initialization. It also allows efficient parametrization of the border using radial basis function (RBF) and thus reduces the tracking of a large number of border points to a very few RBF centers. Moreover, we carry out supervised column-wise tissue classification in order to automatically optimize the feature selection. Instead of empirically assigning weights to different feature detectors, we dynamically and automatically adapt those weighting depending on the tissue compositions in each individual column of pixels.
published_date 2017-12-31T03:49:57Z
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score 10.898149