Siriapisith Thanongchai, Kusakunniran Worapan, Haddawy Peter
Department Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand.
Faculty of Information and Communication Technology, Mahidol University, Nakhonpathom, 73170, Thailand.
Comput Biol Med. 2020 Nov;126:103997. doi: 10.1016/j.compbiomed.2020.103997. Epub 2020 Sep 19.
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±5.23%, 88.86±11.77%, 90.68±2.45%, respectively.
由于像素强度的相似性以及相邻区域之间较差的梯度强度，灰度医学图像的分割具有挑战性。现有的仅基于强度或梯度信息的图像分割方法往往无法产生准确的分割结果。文献中以前的方法通过嵌入或顺序整合不同类型的信息来解决该问题，以提高特定任务的图像分割性能。然而，这种信息的有效组合或整合难以实现，并且对于密切相关的任务不够通用。在单个图结构中整合这两种信息源是解决该问题的一种潜在更有效的方法。在本文中，我们介绍了一种用于灰度医学图像分割的新技术，称为金字塔图割，它使用单个源节点和多个汇节点在金字塔形图结构中组合强度和梯度信息源。源节点位于金字塔图的顶部，将强度信息嵌入到其相连的边中。汇节点位于金字塔图的底部，将梯度信息嵌入到其相连的边中。最小割根据在每次迭代的每个切割位置中哪个更有用或具有更高影响来使用强度信息和梯度信息。实验结果表明，在灰度医学图像数据集（包括公共的3DIRCADb - 01数据集）上，所提出的方法比单独基于强度的分割（即高斯混合模型）和单独基于梯度的分割（即距离正则化水平集演化）更有效。所提出的方法在腹主动脉瘤的样本CT、肝肿瘤的MRI和肝肿瘤的US上取得了优异的分割结果，骰子系数分别为90.49±5.23%、88.86±11.77%、90.68±2.45%。
