Department of Computer Engineering, Bilkent University, Ankara TR-06800, Turkey.
IEEE Trans Med Imaging. 2011 Mar;30(3):721-32. doi: 10.1109/TMI.2010.2094200. Epub 2010 Nov 22.
The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. In this paper, we introduce an effective and robust algorithm for the segmentation of histopathological tissue images. This algorithm incorporates the background knowledge of the tissue organization into segmentation. For this purpose, it quantifies spatial relations of cytological tissue components by constructing a graph and uses this graph to define new texture features for image segmentation. This new texture definition makes use of the idea of gray-level run-length matrices. However, it considers the runs of cytological components on a graph to form a matrix, instead of considering the runs of pixel intensities. Working with colon tissue images, our experiments demonstrate that the texture features extracted from "graph run-length matrices" lead to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with four other segmentation algorithms, the results show that the proposed algorithm is more effective in histopathological image segmentation.
组织标本的组织病理学检查对于癌症的诊断和分级至关重要。然而，由于该检查主要依赖病理学家的视觉解释，因此存在很大的观察者变异性。为了解决这个问题，开发计算定量工具非常重要，而图像分割则是核心步骤。在本文中，我们引入了一种用于组织病理学图像分割的有效且鲁棒的算法。该算法将组织的背景知识纳入到分割中。为此，它通过构建图来量化细胞学组织成分的空间关系，并使用该图为图像分割定义新的纹理特征。这种新的纹理定义利用了灰度游程矩阵的思想。但是，它考虑的是细胞学成分在图上的游程来形成矩阵，而不是考虑像素强度的游程。通过对结肠组织图像进行实验，我们的实验表明，从“图游程矩阵”中提取的纹理特征可实现较高的分割精度，同时还可提供数量合理的分割区域。与其他四种分割算法相比，结果表明，所提出的算法在组织病理学图像分割中更有效。
