On the classification of image features
Pattern Recognition Letters
Active Contours: The Application of Techniques from Graphics,Vision,Control Theory and Statistics to Visual Tracking of Shapes in Motion
Prediction of multivariate chaotic time series with local polynomial fitting
Computers & Mathematics with Applications
Kernel Regression for Image Processing and Reconstruction
IEEE Transactions on Image Processing
Isoparametric finite element analysis for Doo-Sabin subdivision models
Proceedings of Graphics Interface 2012
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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.