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24 June 1998 Self-learning contour finding algorithm for echocardiac analysis
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The detection of left ventricular boundary is an interesting and challenging task in the cardiac analysis. In this paper, a self-learning contour finding model derived based on the snake model is designed to detect the echocardiac boundaries. The proposed model utilizes the genetic algorithms as a training kernel to acquire the weights for the driving forces in the snake deformation. Thus, the weights can be treated as a priori knowledge of contour definition before the contour finding process is proceeded. Both the synthetic and real image experiments are carried out to verify the performance of the proposed method.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ding-Horng Chen and Yung-Nien Sun "Self-learning contour finding algorithm for echocardiac analysis", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998);

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