Department of Mathematics and Computer Science, Fort Valley State University, Fort Valley, GA, USA.
College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
J Healthc Eng. 2017;2017:6506049. doi: 10.1155/2017/6506049. Epub 2017 Oct 23.
Random walk (RW) method has been widely used to segment the organ in the volumetric medical image. However, it leads to a very large-scale graph due to a number of nodes equal to a voxel number and inaccurate segmentation because of the unavailability of appropriate initial seed point setting. In addition, the classical RW algorithm was designed for a user to mark a few pixels with an arbitrary number of labels, regardless of the intensity and shape information of the organ. Hence, we propose a prior knowledge-based Bayes random walk framework to segment the volumetric medical image in a slice-by-slice manner. Our strategy is to employ the previous segmented slice to obtain the shape and intensity knowledge of the target organ for the adjacent slice. According to the prior knowledge, the object/background seed points can be dynamically updated for the adjacent slice by combining the narrow band threshold (NBT) method and the organ model with a Gaussian process. Finally, a high-quality image segmentation result can be automatically achieved using Bayes RW algorithm. Comparing our method with conventional RW and state-of-the-art interactive segmentation methods, our results show an improvement in the accuracy for liver segmentation ( < 0.001).
随机游走 (RW) 方法已被广泛应用于分割体绘制医学图像中的器官。然而，由于节点数量等于体素数量，并且由于缺乏适当的初始种子点设置，导致分割不准确，因此会产生非常大规模的图。此外，经典的 RW 算法是为用户标记任意数量的标签而设计的，而不考虑器官的强度和形状信息。因此，我们提出了一种基于先验知识的贝叶斯随机游走框架，以逐片方式分割体绘制医学图像。我们的策略是利用之前分割的切片，为相邻切片获取目标器官的形状和强度知识。根据先验知识，通过结合窄带阈值 (NBT) 方法和具有高斯过程的器官模型，可以为相邻切片动态更新目标/背景种子点。最后，使用贝叶斯 RW 算法可以自动获得高质量的图像分割结果。将我们的方法与传统的 RW 和最先进的交互式分割方法进行比较，我们的结果表明肝脏分割的准确性有所提高（<0.001）。
