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A fully automated segmentation of the endocardial surface was developed by integrating spatio-temporal information of 3D ultrasound image sequences. 2D and 3D (adaptive) filtering was used to reduce speckle noise and optimize the distinction between blood and myocardium, while preserving sharpness of edges between various structures. Four different filters (2D Adaptive Mean, 2D and 3D Adaptive Mean Squares Filter and 2D Local Entropy) were tested. Filter quality was measured by comparing overlap percentages of histograms of manually segmented blood and myocardial regions. ROC curves of manually segmented blood regions were determined to compare effects of the different filters. A deformable contour algorithm was used, after automatic thresholding, to yield a closed contour of the endocardial border in each elevational plane. Each contour was optimized using contours of surrounding spatio-temporal planes as limiting condition to ensure spatio-temporal continuity.