Lin Xiang, Cowan Brett, Young Alistair
Bioengineering Institute, University of Auckland.
Conf Proc IEEE Eng Med Biol Soc. 2005;2005:3059-62. doi: 10.1109/IEMBS.2005.1617120.
Model-based medical image analysis allows high level information to guide image segmentation. However, most model-based methods rely on evolution methods which may become trapped in local minima. Graph cuts have been proposed for image segmentation problems where the cost of the cut corresponds to an energy function which is then globally minimized. However, it has been difficult to include high level information in the formulation of the graph cut. We have developed a method for integrating model-based a priori information into the graph cut formulation. A 4D model prior of the left ventricle is calculated from an average of historically analyzed cases. This is scaled and rotated to the given case and a 2D spatial prior is calculated for each image. The spatial prior is then combined with pixel intensity data and edge information in the graph cut optimization. Both epicardial and endocardial contours can be found using variations of this procedure. We report results on 11 normal volunteers and 6 patients with heart disease, compared with the results from two experienced observers. A modified Hausdorff distance measure showed good agreement between the model-based graph cut and the expert observers.
基于模型的医学图像分析允许高级信息指导图像分割。然而，大多数基于模型的方法依赖于进化方法，而这些方法可能会陷入局部最小值。针对图像分割问题提出了图割方法，其中割的代价对应于一个能量函数，然后该能量函数被全局最小化。然而，在图割的公式中纳入高级信息一直很困难。我们开发了一种将基于模型的先验信息集成到图割公式中的方法。左心室的四维模型先验是根据历史分析病例的平均值计算得出的。将其缩放并旋转到给定病例，然后为每个图像计算二维空间先验。然后在图割优化中将空间先验与像素强度数据和边缘信息相结合。使用此过程的变体可以找到心外膜和心内膜轮廓。我们报告了11名正常志愿者和6名心脏病患者的结果，并与两名经验丰富的观察者的结果进行了比较。一种改进的豪斯多夫距离度量显示基于模型的图割与专家观察者之间具有良好的一致性。
