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Segmentation of 3D RF echocardiography using a joint spatio-temporal predictor and signal intensity model

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5 Author(s)
Pearlman, P.C. ; Dept. of Electr. Eng., Yale Univ., New Haven, CT, USA ; Tagare, H.D. ; Lin, B.A. ; Sinusas, A.J.
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We present an approach for left ventricular segmentation of radio-frequency (RF) ultrasound sequences. Our method employs an independent identically distributed (iid) spatial model for RF voxel intensity and a conditional model relating a subsequent frame in the image sequence to the frame being segmented by means of a linear predictor that exploits spatio-temporal coherence in the data. The conditional model overcomes segmentation problems due to image inhomogeneity issues, while the spatial model overcomes a tendency of the conditional model to underestimate the blood pool. The method is validated by comparison with manual tracings, segmentations performed using Chan-Vese level sets, and by segmentations leveraging only the linear predictor.

Published in:

Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on

Date of Conference:

March 30 2011-April 2 2011