Shape-Based Similarity Retrieval of Doppler Images for Clinical Decision Support
Abstract
Flow Doppler imaging has become an integral part of an echocardiographic exam. Automated interpretation of flow doppler imaging has so far been restricted to obtaining hemodynamic information from velocity-time profiles depicted in these images. In this paper we exploit the shape patterns in Doppler images to infer the similarity in valvular disease labels for purposes of automated clinical decision support. Specifically, we model the similarity in appearance of Doppler images from the same disease class as a constrained non-rigid translation transform of the velocity envelopes embedded in these images. The shape similarity between two Doppler images is then judged by recovering the alignment transform using a variant of dynamic shape warping. Results of similarity retrieval of doppler images for cardiac decision support on a large database of images are presented.
Cite
Text
Syeda-Mahmood et al. "Shape-Based Similarity Retrieval of Doppler Images for Clinical Decision Support." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010. doi:10.1109/CVPR.2010.5540126Markdown
[Syeda-Mahmood et al. "Shape-Based Similarity Retrieval of Doppler Images for Clinical Decision Support." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010.](https://mlanthology.org/cvpr/2010/syedamahmood2010cvpr-shape/) doi:10.1109/CVPR.2010.5540126BibTeX
@inproceedings{syedamahmood2010cvpr-shape,
title = {{Shape-Based Similarity Retrieval of Doppler Images for Clinical Decision Support}},
author = {Syeda-Mahmood, Tanveer Fathima and Turaga, Pavan K. and Beymer, David and Wang, Fei and Amir, Arnon and Greenspan, Hayit and Pohl, Kilian M.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2010},
pages = {855-862},
doi = {10.1109/CVPR.2010.5540126},
url = {https://mlanthology.org/cvpr/2010/syedamahmood2010cvpr-shape/}
}