DocumentCode :
385352
Title :
An integrated approach to surface modeling in freehand three-dimensional echocardiography
Author :
Song, Mingzhou ; Haralick, Robert M. ; Sheehan, Florence H. ; Johnson, Richard K.
Author_Institution :
Dept. of Comput. Sci., Queens Coll., Flushing, NY, USA
Volume :
2
fYear :
2002
fDate :
2002
Firstpage :
1082
Abstract :
We describe an integrated Bayesian solution to find a left ventricle model, including both epicardium and endocardium surfaces, from freehand 3-D echocardiographic images. The observed images and prior shape knowledge are combined to make the most consistent inference about unknown surface models using the maximum a posteriori rule. Typical model-based computer vision techniques divide the overall problem into two separate low and high-level subproblems. Unlike previous approaches, our approach unifies these two levels through a pixel class prediction mechanism. A putative surface model is generated from a catalog of 86 representative surface models. For each observed pixel, its appearance probability profile from different classes is first computed. Then the class predication probability profile is also computed, based only on the putative surface model. An optimal surface model has the best overall match between these two profiles for all the pixels. The probability models are obtained off-line by the expectation maximization algorithm from 20 training studies. Quantitative experimental results on 25 test studies show the advantage of the integrated approach.
Keywords :
Bayes methods; computer vision; echocardiography; edge detection; image segmentation; medical image processing; physiological models; appearance probability profile; class predication probability profile; endocardium surfaces; epicardium surfaces; expectation maximization algorithm; freehand three-dimensional echocardiography; high-level subproblems; integrated Bayesian solution; left ventricle model; low-level subproblems; maximum a posteriori rule; model-based computer vision techniques; optimal surface model; pixel class prediction mechanism; prior shape knowledge; probability models; putative surface model; representative surface models; surface modeling; training studies; Biomedical imaging; Computer science; Computer vision; Echocardiography; Educational institutions; Image quality; Image segmentation; Predictive models; Probability; Shape;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint
ISSN :
1094-687X
Print_ISBN :
0-7803-7612-9
Type :
conf
DOI :
10.1109/IEMBS.2002.1106288
Filename :
1106288
Link To Document :
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