Open AccessSemi‐automatic detection of coronary artery stenosis in 3D CTA
Author(s) -
Khedmati Abolfazl,
Nikravanshalmani Alireza,
Salajegheh Afshin
Publication year - 2016
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2015.0687
Subject(s) - coronary arteries , stenosis , segmentation , medicine , artery , computed tomography angiography , radiology , angiography , cardiology , artificial intelligence , computer science
Detection of coronary artery stenosis, from 3D computed tomography angiography (CTA), is applicable for inspecting heart diseases. In this study, a semi‐automatic method is proposed which its stages include 3D CTA pre‐processing, vessel enhancement, coronary artery segmentation, centreline extraction, arteries cross‐section diameter estimation, and stenosis detection. In contrast to conventional methods, this study is proposed in which the slices are rescaled from original size to smaller size and then returned to the original size, in order to reduce processing time in the centreline extracting step. To optimise final results, a semi‐automatic method is proposed to adjust seed points for coronary arteries segmentation using 3D region growing method for reducing human interventions. The authors consider two types of evaluations for stenosis detecting, more than 50%, on 18 real data. The first type is patient‐based analysis and the second type is segment‐based analysis. In the first type, a sensitivity of 88.89% and a positive predictive value (PPV) of 88.89% are obtained, and in the second type, a sensitivity of 44.2%, and a PPV of 34.27% is achieved. Moreover, the average execution time for stenosis detecting in a 3D CTA is approximately 8.5 min.