The anatomical and functional cardiac cavities information obtained by Ultrasound images allows a qualitative and quantitative analysis to determine patient's health and detect possible pathologies. Several approaches have been proposed for semiautomatic or fully automatic segmentation. Texture based presegmentation combined with an active contour model have proven to be a promising way to extract cardiac structures from echographic images. In this work a novel procedure for 3D cardiac image segmentation is introduced. A robust pre-processing step that reduces noise and extracts an initial frontier of cardiac structures is combined with an Active Surface Model to obtain final 3D segmentation. Preprocessing is performed by the Mean Shift algorithm that integrates 3D edge confidence map and includes entropy, echoes intensity and spatial information as input features. This procedure locates adequately homogeneous regions in 3D echocardiographic images. The external energy terms included in the Active Surface Model are the 3D edge confidence map and the entropy component obtained by the Mean Shift pre-segmentation. The results demonstrate that the pre-processing provides homogeneous regions and a good initial frontier between blood and myocardium. The Active Surface Model adjusts the initial surface computed by the mean-shift algorithm to the cardiac border. Finally, the obtained results are compared with the experts' manual segmentation and the Tanimoto index between these segmentations is calculated.