Self-learning contour finding algorithm for echocardiac analysis

Ding Horng Chen, Yung-Nien Sun

研究成果: Conference article同行評審

2 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)971-981
期刊Proceedings of SPIE - The International Society for Optical Engineering
出版狀態Published - 1998 十二月 1
事件Medical Imaging 1998: Image Processing - San Diego, CA, United States
持續時間: 1998 二月 231998 二月 23

All Science Journal Classification (ASJC) codes

  • 電腦科學應用
  • 電氣與電子工程
  • 電子、光磁材料
  • 應用數學
  • 凝聚態物理學


深入研究「Self-learning contour finding algorithm for echocardiac analysis」主題。共同形成了獨特的指紋。