Endocardial boundary detection using a neural network

Ching Tsorng Tsai, Yung Nien Sun, Pau Choo Chung, Jiann Shu Lee

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23 引文 斯高帕斯(Scopus)


Echocardiography has been widely used as a real-time non-invasive clinical tool to diagnose cardiac functions. Due to the poor quality and inherent ambiguity in echocardiograms, it is difficult to detect the myocardial boundaries of the left ventricle. Many existing methods are semi-automatic and detect cardial boundaries by serial computation which is too slow to be practical in real applications. In this paper, a new method for detecting the endocardial boundary by using a Hopfield neural network is proposed. Taking advantage of parallel computation and energy convergence capability in the Hopfield network, this method is faster and more stable for the detection of the endocardial border. Moreover, neither manual operations nor a priori assumptions are needed in this method. Experiments on several LV echocardiograms and clinical validation have shown the effectiveness of our method in these patient studies.

頁(從 - 到)1057-1068
期刊Pattern Recognition
出版狀態Published - 1993 七月

All Science Journal Classification (ASJC) codes

  • 軟體
  • 訊號處理
  • 電腦視覺和模式識別
  • 人工智慧


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