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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Polynomial Regression Based Edge Filtering for Left Ventricle Tracking in 3D Echocardiography
verfasst von : Engin Dikici, Fredrik Orderud
Erschienen in: Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges
Verlag: Springer Berlin Heidelberg
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Automated detection of endocardial borders in 3D echocardiography is a challenging task. Part of the reason for this is the endocardial boundary leads to alternating edge characteristics that vary 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 endocardial edge detection problem. In this paper, a local polynomial regression based method (LPR) is introduced for filtering the STEP results. For each endocardial model point, (1) the surface is parametrized locally around the point, (2) a polynomial regression is applied on the STEP edges in the parametric domain, and (3) the fitted polynomial is evaluated at the origin of the parametric domain to determine the endocardial edge position. The effectiveness of the introduced method is validated via comparative analyses among the MFMC, STEP, and first & second degree LPR methods.