Computer aided detection and measurement of coronary artery disease from computed tomography angiography images

Mazinani, Mahdi (2012) Computer aided detection and measurement of coronary artery disease from computed tomography angiography images. (PhD thesis), Kingston University, uk.bl.ethos.592759.

Full text not available from this archive.

Abstract

Coronary artery disease is one of the most pernicious diseases around the world and early identification of vascular disease can help to reduce morbidity and mortality. Assessment of the degree of vascular obstruction, or stenosis, is critical for classifying the risks of the future vascular events. Automatic detection and quantification of stenosis are important in assessing coronary artery disease from medical imagery, especially for disease progression. Important factors affecting the reproducability and robustness of accuarate quantification arise from the partial volume effect and other noise sources. The main goal of this study is to present a fully automatic approach for detection and quantification of the stenosis in the coronary arteries. The proposed approach begins by building a 3D reconstruction of the coronary arterial system and then making accurate measurement of the vessel diameter from a robust estimate of the vessel cross-section. The proposed algorithm models the partial volume effect using a Markovian fuzzy clustering method in the process of accurate quantification of the degree of stenosis. To evaluate the accuracy and reproducibility of the measurement, the method was applied to a vascular phantom that was scanned using different protocols. The algorithm was applied to 20 CTA patient datasets containing a total of 85 stenoses, which were all successfully detected, with an average false positive rate of 0.7 per scan.

Item Type: Thesis (PhD)
Physical Location: This item is held in stock at Kingston University library.
Research Area: Allied health professions and studies
Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing (until 2017) > School of Computing and Information Systems
Depositing User: Katrina Clifford
Date Deposited: 13 Mar 2014 12:30
Last Modified: 06 Nov 2018 11:43
DOI: uk.bl.ethos.592759
URI: http://eprints.kingston.ac.uk/id/eprint/24527

Actions (Repository Editors)

Item Control Page Item Control Page