title: Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector.
abstract: We propose a method for extracting the left ventricular (LV) contours from left ventriculograms by means of a neural edge detector (NED) in order to extract the contours which accord with those traced by a cardiologist. The NED is a supervised edge detector based on a modified multilayer neural network, and is trained by use of a modified back-propagation algorithm. The NED can acquire the function of a desired edge detector through training with a set of input images and the desired edges obtained from the contours traced by a cardiologist. The proposed contour-extraction method consists of 1) detection of "subjective edges" by use of the NED; 2) extraction of rough contours by use of low-pass filtering and edge enhancement; and 3) a contour-tracing method based on the contour candidates synthesized from the edges detected by the NED and the rough contours. Through experiments, it was shown that the proposed method was able to extract the contours in agreement with those traced by an experienced cardiologist, i.e., we achieved an average contour error of 6.2% for left ventriculograms at end-diastole and an average difference between the ejection fractions obtained from the manually traced contours and those obtained from the computer-extracted contours of 4.1%.
authors.affiliation: Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, The University of Chicago, 5841 S. Maryland Ave., Chicago, IL 60637, USA. suzuki@uchicago.edu
authors.forename: Kenji
authors.initials: K
authors.lastname: Suzuki
authors.forename: Isao
authors.initials: I
authors.lastname: Horiba
authors.forename: Noboru
authors.initials: N
authors.lastname: Sugie
authors.forename: Michio
authors.initials: M
authors.lastname: Nanki
mesh_terms: D000465:Algorithms; D015901:Angiography, Digital Subtraction / Q000379:methods*; D001185:Artificial Intelligence; D006352:Heart Ventricles / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D015635:Radionuclide Ventriculography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity