Texture-based echocardiographic segmentation using a non-parametric estimator and an active contour model

An accurate segmentation of cardiac cavities is necessary to assess cardiac function and to determine quantitative parameters. Several semi-automatic techniques have been tested to achieve this goal. In this work we propose an algorithm to segment cardiac structures, based on a robust pre-processing step that eliminates noise and extracts an initial frontier, together with a refined deformable model, that integrates edge confidence and texture information. Results show that a combination of a mean-shift filter with an active contour model is adequate for echographic images, especially when texture information is included.