Active shape models—their training and application
Computer Vision and Image Understanding
3D Cardiac Deformation from Ultrasound Images
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Motion Analysis: Model Selection and Motion Segmentation
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation
International Journal of Computer Vision
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Generalized L2-Divergence and Its Application to Shape Alignment
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Bilinear Models for Spatio-Temporal Point Distribution Analysis
International Journal of Computer Vision
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Large-scale multimodal mining for healthcare with mapreduce
Proceedings of the 1st ACM International Health Informatics Symposium
A spatio-temporal framework for related topic search in micro-blogging
AMT'10 Proceedings of the 6th international conference on Active media technology
The synergy of 3d SIFT and sparse codes for classification of viewpoints from echocardiogram videos
MCBR-CDS'12 Proceedings of the Third MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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Disease-specific understanding of echocardiographic sequences requires accurate characterization of spatio-temporal motion patterns. In this paper we present a method of automatic extraction and matching of spatio-temporal patterns from cardiac echo videos. Specifically, we extract cardiac regions (chambers and walls) using a variation of multiscale normalized cuts that combines motion estimates from deformable models with image intensity. We then derive spatio-temporal trajectories of region measurements such as wall motion, volume and thickness. The region trajectories are then matched to infer the similarities in disease labels of patients. Validation results on patient data sets collected from many hospitals are presented.