We present a new wavelet-based strategy for autonomous feature extraction and segmentation of cardiac structures in dynamic ultrasound images. Image sequences subjected to a multidimensional (2D plus time) wavelet transform yield a large number of individual subbands, each coding for partial structural and motion information of the ultrasound sequence. We exploited this fact to create an analysis strategy for autonomous analysis of cardiac ultrasound that builds on shape- and motion specific wavelet subband flters. Subband selection was in an automatic manner based on subband statistics. Such a collection of predefined subbands corresponds to the so-called footprint of the target structure and can be used as a multidimensional multiscale filter to detect and localize the target structure in the original ultrasound sequence. Autonomous, unequivocal localization by the autonomous algorithm is then done using a peak finding algorithm, allowing to compare the findings with a reference standard. Image segmentation is then possible using standard region growing operations. To test the feasibility of this multiscale footprint algorithm, we tried to localize, enhance and segment the mitral valve autonomously in 182 non-selected clinical cardiac ultrasound sequences. Correct autonomous localization by the algorithm was feasible in 165 of 182 reconstructed ultrasound sequences, using the experienced echocardiographer as reference. This corresponds to a 91% accuracy of the proposed method in unselected clinical data. Thus, multidimensional multiscale wavelet footprints allow successful autonomous detection and segmentation of the mitral valve with good accuracy in dynamic cardiac ultrasound sequences which are otherwise difficult to analyse due to their high noise level.