Maximum likelihood and James-Stein edge estimators for left ventricle tracking in 3D echocardiography

Authors:
Engin Dikici;Fredrik Orderud
Affiliations:
Norwegian University of Science and Technology, Trondheim, Norway;GE Vingmed Ultrasound, Oslo, Norway
Venue:
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Year:
2011

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Abstract

Accurate and consistent detection of endocardial borders in 3D echocardiography is a challenging task. Part of the reason for this is that the trabeculated structure of the endocardial boundary leads to alternating edge characteristics that varies over a cardiac cycle. The maximum gradient (MG), step criterion (STEP) and max flow/min cut (MFMC) edge detectors have been previously applied for the detection of endocardial edges. In this paper, we combine the responses of these edge detectors based on their confidences using maximum likelihood (MLE) and James-Stein (JS) estimators. We also present a method for utilizing the confidence-based estimates as measurements in a Kalman filter based left ventricle (LV) tracking framework. The effectiveness of the introduced methods are validated via comparative analyses among the MG, STEP, MFMC, MLE and JS.