Automated segmentation of X-ray Left Ventricular angiograms using multi-view Active Appearance Models and dynamic programming

Abstract

A novel approach to automated segmentation of X-ray Left Ventricular (LV) angiograms is proposed, based on Active Appearance Models (AAMs) and dynamic programming (DP). Due to combined modeling of the end-diastolic (ED) and end-systolic (ES) phase, existing correlations in shape and texture representation are exploited, resulting in a better segmentation in the ES phase. The intrinsic over-constraining by the model is compensated by a DP algorithm, in which also cardiac contraction motion features are incorporated. An elaborate evaluation of the algorithm, based on 70 paired ED-ES images, shows success rates of 100% for ED and 99% for ES, with average border positioning errors of 0.68 mm and 1.45 mm respectively. Calculated volumes were accurate and unbiased, proving the high clinical potential of our method. © Springer-Verlag Berlin Heidelberg 2005.