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Automated myocardial edge detection on MR images: Accuracy in consecutive subjects
Author(s) -
Fleagle Steven R.,
Thedens Daniel R.,
Stanford William,
Thompson Brad H.,
Weston John M.,
Patel Pranav P.,
Skorton David J.
Publication year - 1993
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.1880030508
Subject(s) - artificial intelligence , computer science , radiology , edge detection , computer vision , medicine , nuclear medicine , pattern recognition (psychology) , image processing , image (mathematics)
Abstract The authors previously demonstrated the feasibility of graph‐searching‐based automated edge detection in cardiac magnetic resonance (MR) imaging. To further assess the clinical utility of this method, unselected images from 11 consecutive subjects undergoing clinically indicated (except for one healthy volunteer) short‐axis spin‐echo MR imaging were analyzed. A total of 142 images from the 11 subjects, encompassing the left ventricle from apex to outflow tract, were analyzed. The computer algorithm correctly identified complete endocardial and epicardial contours in 121 of 142 images (85%). Correlations between observerbraced and computer‐derived epicardial areas for all images were good ( r = .71 for epicardium, r = .83 for endocardium); they improved for a subset of higherquality images ( r = .82 for epicardium, r = .92 for endocardium). The authors conclude that the current data further support the usefulness of computer digital image processing in geometric analysis of cardiac MR image data.