Greedy Algorithm for Error Correction in Automatically Produced Boundaries from Low Contrast Ventriculograms

Abstract

Non-homogeneous mixing of the dye with the blood in the left ventricle chamber of the heart causes poor contrast in the ventriculograms. The pixel-based classifiers [1] operating on these ventriculograms yield boundaries which are not close to ground truth boundaries as delineated by the cardiologist. They have a mean boundary error of 6.4 mm and an error of 12.5 mm in the apex zone. These errors have a systematic positional and orientational bias, the boundary being under-estimated in the apex zone. This paper discusses two calibration methods: the identical coefficient and the independent coefficient to remove these systematic biases. From these methods, we constitute a fused algorithm which reduces the boundary error compared to either of the calibration methods. The algorithm, in a greedy way, computes which and how many vertices of the left ventricle boundary can be taken from the computed boundary of each method in order to best improve the performance. The corrected boundaries have a mean error of less than 3.5 mm with a standard deviation of 3.4 mm over the approximately 6 × 10 4 vertices in the data set of 291 studies. Our method reduces the mean boundary error by 2.9 mm over the boundary produced by the classifier. We also show that the calibration algorithm performs better in the apex zone where the dye is unable to propagate. For end diastole, the algorithm reduces the error in the apex zone by 8.5 mm over the pixel-based classifier boundaries.