Computers in Biology and Medicine
Edge detection in ventriculograms using support vector machine classifiers and deformable models
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
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Automated left-ventricle (LV) boundary delineation from contrast ventriculograms has been studied for decades. Unfortunately, no accurate methods have ever been reported. A new knowledge based multistage method to automatically delineate the LV boundary at end diastole (ED) and end systole (ES) is discussed in this paper. It has a mean absolute boundary error of about 2 mm and an associated ejection fraction error of about 6%. The method makes extensive use of knowledge about LV shape and movement. The processing includes a multi-image pixel region classification, shape regression, and rejection classification. The method was trained and cross-validated tested on a database of 375 studies whose ED and ES boundary had been manually traced as the ground truth. The cross-validated results presented in this paper show that the accuracy is close to and slightly above the interobserver variability.