Segmentation of grayscale medical images is challenging because of the similarity of pixel intensities and poor gradient strength between adjacent regions. The existing image segmentation approaches based on either intensity or gradient information alone often fail to produce accurate segmentation results. Previous approaches in the literature have approached the problem by embedded or sequential integration of different information types to improve the performance of the image segmentation on specific tasks. However, an effective combination or integration of such information is difficult to implement and not sufficiently generic for closely related tasks. Integration of the two information sources in a single graph structure is a potentially more effective way to solve the problem. In this paper we introduce a novel technique for grayscale medical image segmentation called pyramid graph cut, which combines intensity and gradient sources of information in a pyramid-shaped graph structure using a single source node and multiple sink nodes. The source node, which is the top of the pyramid graph, embeds intensity information into its linked edges. The sink nodes, which are the base of the pyramid graph, embed gradient information into their linked edges. The min-cut uses intensity information and gradient information, depending on which one is more useful or has a higher influence in each cutting location of each iteration. The experimental results demonstrate the effectiveness of the proposed method over intensity-based segmentation alone (i.e. Gaussian mixture model) and gradient-based segmentation alone (i.e. distance regularized level set evolution) on grayscale medical image datasets, including the public 3DIRCADb-01 dataset. The proposed method archives excellent segmentation results on the sample CT of abdominal aortic aneurysm, MRI of liver tumor and US of liver tumor, with dice scores of 90.49$\pm$5.23%, 88.86$\pm$11.77%, 90.68$\pm$2.45%, respectively.

由于像素强度的相似性和相邻区域之间较差的渐变强度，灰度医学图像的分割具有挑战性。仅基于强度或梯度信息的现有图像分割方法常常不能产生准确的分割结果。文献中的先前方法通过嵌入或顺序集成不同的信息类型来解决该问题，以提高特定任务上图像分割的性能。但是，此类信息的有效组合或集成很难实现，并且对于紧密相关的任务还不够通用。将两个信息源集成在单个图形结构中是解决该问题的一种可能更有效的方法。在本文中，我们介绍了一种称为金字塔图切割的灰度医学图像分割新技术，该技术使用单个源节点和多个接收器节点将信息的强度和梯度源组合成金字塔形的图结构。源节点位于金字塔图的顶部，它将强度信息嵌入到其链接的边缘中。作为金字塔图基础的汇点节点将渐变信息嵌入到其链接的边缘中。最小切割使用强度信息和梯度信息，这取决于在每次迭代的每个切割位置中哪个更有用或影响更大。实验结果证明了该方法相对于仅基于强度的分割（即高斯混合模型）和仅基于梯度的分割（即 距离正则化水平集演化）（包括公共3DIRCADb-01数据集）。该方法在腹部主动脉瘤CT，肝肿瘤MRI和肝肿瘤超声检查中均获得了优异的分割结果，骰子得分为90.49。$\pm$5.23％，88.86$\pm$11.77％，90.68$\pm$分别为2.45％。