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11 April 2002 Unsupervised estimation of the left ventricular boundary in echocardiographic image sequences using edge probabilities
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As a tool for cardiac assessment, 3D echocardiography (3DE) has largely been limited to use by experts capable of qualitative determination of left ventricular (LV) function. The usefulness of 3DE can be extended to physicians in critical care settings who have minimal training in echocardiography if it delivers quantitative parameters of LV function without expert supervision. As a critical step to generate the quantitative measures, we develop an algorithm that automatically locates and tracks the LV boundary through a sequence of 2D frames, such as those constituting a 3DE data set. A novel approach of the algorithm in this paper is the computation of an edge probability field for each frame. For unsupervised processing, the algorithm incorporates a template-based search in the first frame using prior knowledge about the LV shape. Then, by active contour (or snake) matching, the LV boundary is obtained as a MAP estimate. The internal energy constraints of the snake serve as the prior probability. Unlike the conventional snakes that depend on hard edge information as external energy, the snake in our approach uses the soft information in the edge probability field. We also can obtain a measure of confidence in the boundary estimate from the value of the energy function at the estimated contour. The estimate from one frame is used to initialize active contour matching in the next frame for LV tracking.
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Il-Seop Shin, Patrick A. Kelly, Haluk Derin, K. Francis Lee, and Dennis Tighe "Unsupervised estimation of the left ventricular boundary in echocardiographic image sequences using edge probabilities", Proc. SPIE 4687, Medical Imaging 2002: Ultrasonic Imaging and Signal Processing, (11 April 2002);

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