Best linear unbiased estimator for Kalman filter based left ventricle tracking in 3D+T echocardiography

In this paper, we introduce the best linear unbiased estimator (BLUE) for the detection of endocardial edges in 3D+T echocardiography recordings. The maximum gradient (MG), step criterion (STEP) and max flow/min cut (MFMC) edge detectors have been previously applied for the detection of the endocardial edges. BLUE combines the responses of these 3 base estimators using statistical inferences. First, the base estimator bias and covariance properties are learned for each endocardial surface point at each cardiac cycle position. Then, these statistical properties are utilized to compute an optimal linear combination of the base detectors by BLUE. For the validation, MG, STEP, MFMC and BLUE were each employed in connection to a Kalman tracking frame- work. Comparative analyses showed that BLUE outper- forms the other estimators in surface and volumetric measurement accuracy.