Improving performance of automated coronary arterial tree center-line extraction, stent localization and tracking

Stent placement is a common procedure in Percutaneous Transluminal Coronary Angioplasty which helps many patients to avoid emergency heart bypass surgery or heart attack. In this procedure, a stent is implanted at the narrowing part of an artery to keep its lumen open allowing blood to flow normally...

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Main Author:	Boroujeni, Farsad Zamani
Format:	Thesis
Language:	English
Published:	2012
Subjects:	Stents (Surgery) - Automation Coronary arteries - Data processing Computer algorithms
Online Access:	http://psasir.upm.edu.my/id/eprint/31411/1/FSKTM%202012%2028R.pdf
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id	my-upm-ir.31411
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spelling	my-upm-ir.314112015-02-12T01:07:53Z Improving performance of automated coronary arterial tree center-line extraction, stent localization and tracking 2012-12 Boroujeni, Farsad Zamani Stent placement is a common procedure in Percutaneous Transluminal Coronary Angioplasty which helps many patients to avoid emergency heart bypass surgery or heart attack. In this procedure, a stent is implanted at the narrowing part of an artery to keep its lumen open allowing blood to flow normally through the artery. A potential risk of this treatment is the inaccurate placement of the stent (geographic miss), which could result in serious complications for the patient such as development of new stenotic lesions, increasing the likelihood of blood clot formation and the need for revascularization. Over the last decade, many algorithms have been developed to address this problem. However, most of them either fail to meet the demanding requirements of real-time assistant systems or to provide quantitative analysis for erification of stent placement task. In this research work, we report on new contributions to the major parts of a computer assisted stent positioning system. The first contribution is automatic detection of seed points which serve as a prerequisite step for centerline extraction algorithm. The solution consists of an algorithm for automatic collection of candidate seed points using efficient grid line searching mechanism and a validation method which uses local geometric and intensity based features as effective validation rules to discriminate between the actual seed point and false alarms. The experimental results show that combining the advantages of the geometric based validation and contrast based filtering as well as avoiding large quantization errors, lead to significant enhancement in the performance of the seed point detection algorithm in terms of balancing between the precision and recall. The second contribution is related to the robust and accurate extraction of centerlines for all vessel segments of the arterial tree in the angiogram images. This problem is addressed by proposing an accurate and robust centerline extraction method. Starting at each detected seed point, the centerline extraction method utilizes eigenvalues and eigenvectors of Hessian matrix for the pixels located on a semi-circular scanning profile for robust estimation of the next centerline point. The experimental validations show that the use of Hessian matrix results in significant improvement in the robustness of the tracing algorithm. The stent localization and tracking in angiogram image sequence is the topic of the third contribution. The proposed method combines special fast filtering, region of interest processing and graph based trajectory analysis approach to localize and track the radioopaque markers of the stent in fluoroscopic frame sequences. The most interesting finding was that the validation of the potential markers prior to building tracks of marker pairs, causes the landmark detection process to avoid dealing with a large number of outliers and misdetections. In total, the current study found that the proposed algorithms outperform their well-established existing counterparts indicating their suitability to be adopted in practical computer assisted stent positioning systems. Stents (Surgery) - Automation Coronary arteries - Data processing Computer algorithms 2012-12 Thesis http://psasir.upm.edu.my/id/eprint/31411/ http://psasir.upm.edu.my/id/eprint/31411/1/FSKTM%202012%2028R.pdf application/pdf en public phd doctoral Universiti Putra Malaysia Stents (Surgery) - Automation Coronary arteries - Data processing Computer algorithms Faculty of Computer Science and Information Technology
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Stents (Surgery) - Automation Coronary arteries - Data processing Computer algorithms
spellingShingle	Stents (Surgery) - Automation Coronary arteries - Data processing Computer algorithms Boroujeni, Farsad Zamani Improving performance of automated coronary arterial tree center-line extraction, stent localization and tracking
description	Stent placement is a common procedure in Percutaneous Transluminal Coronary Angioplasty which helps many patients to avoid emergency heart bypass surgery or heart attack. In this procedure, a stent is implanted at the narrowing part of an artery to keep its lumen open allowing blood to flow normally through the artery. A potential risk of this treatment is the inaccurate placement of the stent (geographic miss), which could result in serious complications for the patient such as development of new stenotic lesions, increasing the likelihood of blood clot formation and the need for revascularization. Over the last decade, many algorithms have been developed to address this problem. However, most of them either fail to meet the demanding requirements of real-time assistant systems or to provide quantitative analysis for erification of stent placement task. In this research work, we report on new contributions to the major parts of a computer assisted stent positioning system. The first contribution is automatic detection of seed points which serve as a prerequisite step for centerline extraction algorithm. The solution consists of an algorithm for automatic collection of candidate seed points using efficient grid line searching mechanism and a validation method which uses local geometric and intensity based features as effective validation rules to discriminate between the actual seed point and false alarms. The experimental results show that combining the advantages of the geometric based validation and contrast based filtering as well as avoiding large quantization errors, lead to significant enhancement in the performance of the seed point detection algorithm in terms of balancing between the precision and recall. The second contribution is related to the robust and accurate extraction of centerlines for all vessel segments of the arterial tree in the angiogram images. This problem is addressed by proposing an accurate and robust centerline extraction method. Starting at each detected seed point, the centerline extraction method utilizes eigenvalues and eigenvectors of Hessian matrix for the pixels located on a semi-circular scanning profile for robust estimation of the next centerline point. The experimental validations show that the use of Hessian matrix results in significant improvement in the robustness of the tracing algorithm. The stent localization and tracking in angiogram image sequence is the topic of the third contribution. The proposed method combines special fast filtering, region of interest processing and graph based trajectory analysis approach to localize and track the radioopaque markers of the stent in fluoroscopic frame sequences. The most interesting finding was that the validation of the potential markers prior to building tracks of marker pairs, causes the landmark detection process to avoid dealing with a large number of outliers and misdetections. In total, the current study found that the proposed algorithms outperform their well-established existing counterparts indicating their suitability to be adopted in practical computer assisted stent positioning systems.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Boroujeni, Farsad Zamani
author_facet	Boroujeni, Farsad Zamani
author_sort	Boroujeni, Farsad Zamani
title	Improving performance of automated coronary arterial tree center-line extraction, stent localization and tracking
title_short	Improving performance of automated coronary arterial tree center-line extraction, stent localization and tracking
title_full	Improving performance of automated coronary arterial tree center-line extraction, stent localization and tracking
title_fullStr	Improving performance of automated coronary arterial tree center-line extraction, stent localization and tracking
title_full_unstemmed	Improving performance of automated coronary arterial tree center-line extraction, stent localization and tracking
title_sort	improving performance of automated coronary arterial tree center-line extraction, stent localization and tracking
granting_institution	Universiti Putra Malaysia
granting_department	Faculty of Computer Science and Information Technology
publishDate	2012
url	http://psasir.upm.edu.my/id/eprint/31411/1/FSKTM%202012%2028R.pdf
_version_	1747811611278573568

Improving performance of automated coronary arterial tree center-line extraction, stent localization and tracking

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