Linear vs. Quadratic Optimization Algorithms for Bias Correction of Left Ventricle Chamber Boundaries in Low Contrast Projection Ventriculograms Produced from Xray Cardiac Catheterization Procedure

Abstract

Cardiac catheterization procedure produces ventriculograms which have very low contrast in the apical, anterior and inferior zones of the left ventricle (LV). Pixel-based classifiers operating on these images produce boundaries which have systematic positional and orientation bias and have a mean error of about 10.5 mm. Using the LV convex information, comprising of the apex and the aortic valve plane, this paper presents a comparison of the linear and quadratic optimization algorithms to remove these biases. These algorithms are named after the way the coefficients are computed: the identical coefficient and the independent coefficient. Using the polyline metric, we show that the quadratic optimization is better than the linear optimization. We also show that the independent coefficient method performs better than the identical coeffcient when the training data is large. The overall mean system error was 2.49 mm while the goal set by the cardiologist was 2.5 mm.