Self-learning contour finding algorithm for echocardiac analysis

Ding-Horng Chen; Yung-Nien Sun

doi:10.1117/12.310975

24 June 1998 Self-learning contour finding algorithm for echocardiac analysis

Ding-Horng Chen, Yung-Nien Sun

Proceedings Volume 3338, Medical Imaging 1998: Image Processing; (1998) https://doi.org/10.1117/12.310975
Event: Medical Imaging '98, 1998, San Diego, CA, United States

Abstract

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.

Citation Download Citation

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); https://doi.org/10.1117/12.310975

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