Automatic delineation of the myocardium in real-time 3D

echocardiography may be used to aid the diagnosis of heart problems

such as ischaemia, by enabling quantification of wall thickening and

wall motion abnormalities. Distinguishing between myocardial and nonmyocardial

tissue is, however, difficult due to low signal-to-noise ratio as

well as the efficiency constraints imposed on any algorithmic solution by

the large size of the data under consideration. In this paper, we take a

machine learning approach treating this problem as a two-class 3D patch

classification task. We demonstrate that solving such task using random

forests, which are the discriminative classifiers developed recently in the

machine learning community, allows to obtain accurate delineations in a

matter of seconds (on a CPU) or even in real-time (on a GPU) for the

entire 3D volume.

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In  FIMH 2009 [best paper award]

Publisher  Springer Verlag
All copyrights reserved by Springer 2007.

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