Share Email Print

Proceedings Paper

Self-learning contour finding algorithm for echocardiac analysis
Author(s): Ding-Horng Chen; Yung-Nien Sun
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 24 June 1998
PDF: 11 pages
Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); doi: 10.1117/12.310975
Show Author Affiliations
Ding-Horng Chen, National Cheng-Kung Univ. (Taiwan)
Yung-Nien Sun, National Cheng-Kung Univ. (Taiwan)

Published in SPIE Proceedings Vol. 3338:
Medical Imaging 1998: Image Processing
Kenneth M. Hanson, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?