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Conference Paper/Proceeding/Abstract 408 views

Segmentation and Tracking of Coronary Arterial Wall / Ehab Essa; Xianghua Xie

Computational and Mathematical Biomedical Engineering

Swansea University Author: Xianghua, Xie

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Abstract

In this paper, we present a segmentation and tracking method based on hidden Markov model (HMM) to detect the outer coronary arterial wall in intravascular images. The proposed method tracks a set of hidden states representing the border location on a set of normal lines obtained from the previ- ous...

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Published in: Computational and Mathematical Biomedical Engineering
ISSN: 2227-3085 2227-9385
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa32107
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first_indexed 2017-02-25T05:04:38Z
last_indexed 2018-02-09T05:19:41Z
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spelling 2017-05-19T19:14:29.4563598 v2 32107 2017-02-24 Segmentation and Tracking of Coronary Arterial Wall b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2017-02-24 SCS In this paper, we present a segmentation and tracking method based on hidden Markov model (HMM) to detect the outer coronary arterial wall in intravascular images. The proposed method tracks a set of hidden states representing the border location on a set of normal lines obtained from the previ- ous frame. The border observation is derived from a classification-based cost function and a shape prior model. The emission probability is defined based on two Gaussian probability distributions for the vessel border and background. The transition probability is learned by using the Baum-Welch algorithm. The optimal sequence of the hidden states is obtained by using Viterbi algorithm. The proposed method shows promising results on tracking and segmenting the arterial wall. Conference Paper/Proceeding/Abstract Computational and Mathematical Biomedical Engineering 2227-3085 2227-9385 Image analysis, machine learning 30 4 2017 2017-04-30 http://www.compbiomed.net/getfile.php?type=13/site_documents&amp;id=CMBE17Vol1prepress_2227-9385.pdf The paper is available freely online as part of CMBE 2017 Proceedings Vol. 1: http://www.compbiomed.net/2017/cmbe-proceedings.htm COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2017-05-19T19:14:29.4563598 2017-02-24T23:42:23.2106693 College of Science Computer Science Ehab Essa 1 Xianghua Xie 0000-0002-2701-8660 2
title Segmentation and Tracking of Coronary Arterial Wall
spellingShingle Segmentation and Tracking of Coronary Arterial Wall
Xianghua, Xie
title_short Segmentation and Tracking of Coronary Arterial Wall
title_full Segmentation and Tracking of Coronary Arterial Wall
title_fullStr Segmentation and Tracking of Coronary Arterial Wall
title_full_unstemmed Segmentation and Tracking of Coronary Arterial Wall
title_sort Segmentation and Tracking of Coronary Arterial Wall
author_id_str_mv b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xianghua, Xie
author Xianghua, Xie
author2 Ehab Essa
Xianghua Xie
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publishDate 2017
institution Swansea University
issn 2227-3085
2227-9385
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hierarchy_top_title College of Science
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url http://www.compbiomed.net/getfile.php?type=13/site_documents&amp;id=CMBE17Vol1prepress_2227-9385.pdf
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description In this paper, we present a segmentation and tracking method based on hidden Markov model (HMM) to detect the outer coronary arterial wall in intravascular images. The proposed method tracks a set of hidden states representing the border location on a set of normal lines obtained from the previ- ous frame. The border observation is derived from a classification-based cost function and a shape prior model. The emission probability is defined based on two Gaussian probability distributions for the vessel border and background. The transition probability is learned by using the Baum-Welch algorithm. The optimal sequence of the hidden states is obtained by using Viterbi algorithm. The proposed method shows promising results on tracking and segmenting the arterial wall.
published_date 2017-04-30T03:47:45Z
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