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Automatic contour detection by encoding knowledge into active contour models

机译:通过将知识编码到活动轮廓模型中来自动轮廓检测

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An original method for an automatic detection of contours in difficult images is proposed. This method is based on a tight cooperation between a multi-resolution neural network and a hidden Markov model-enhanced dynamic programming procedure. This new method is able to overcome the three major drawbacks of the "standard" active contours, initialization dependency, exclusive use of local information and occlusion sensitivity. The driving idea is to introduce high-order a priori information in each step of the system. An application to the automatic detection of the left ventricle in digital X-ray images is proposed.
机译:提出了一种自动检测困难图像轮廓的原始方法。该方法基于多分辨率神经网络与隐马尔可夫模型增强的动态规划程序之间的紧密协作。这种新方法能够克服“标准”活动轮廓,初始化依赖性,局部信息的独占使用和遮挡敏感度这三个主要缺点。驱动思想是在系统的每个步骤中引入高阶先验信息。提出了一种在数字X射线图像中自动检测左心室的方法。

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