Biomedical Imaging GroupSTI
English only   BIG > Publications > Wavelet Footprints

 Home Page
 News & Events
 Tutorials and Reviews
 Download Algorithms

 All BibTeX References

Multidimensional, Multistage Wavelet Footprints: A New Tool for Image Segmentation and Feature Extraction in Medical Ultrasound

C.H.P. Jansen, M. Arigovindan, M. Sühling, S. Marsch, M. Unser, P. Hunziker

Proceedings of the SPIE International Symposium on Medical Imaging: Image Processing (MI'03), San Diego CA, USA, February 17-20, 2003, vol. 5032, part II, pp. 762-767.

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.

AUTHOR="Jansen, C.P.H. and Arigovindan, M. and S{\"{u}}hling, M. and
        Marsch, S. and Unser, M. and Hunziker, P.",
TITLE="Multidimensional, Multistage Wavelet Footprints: {A} New Tool
        for Image Segmentation and Feature Extraction in Medical
BOOKTITLE="Progress in Biomedical Optics and Imaging, vol. 4, no.
editor="Sonka, M. and Fitzpatrick, J.M.",
series="Proceedings of the {SPIE} International Symposium on Medical
        Imaging: {I}mage Processing ({MI'03})",
address="San Diego CA, USA",
month="February 17-20,",
note="{Part {II}}")

© 2003 SPIE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from SPIE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.