Department of Computer Engineering, Bilkent University, Ankara TR-06800, Turkey.
IEEE Trans Biomed Eng. 2012 Jun;59(6):1681-90. doi: 10.1109/TBME.2012.2191784. Epub 2012 Mar 23.
This paper presents a new approach for unsupervised segmentation of histopathological tissue images. This approach has two main contributions. First, it introduces a new set of high-level texture features to represent the prior knowledge of spatial organization of the tissue components. These texture features are defined on the tissue components, which are approximately represented by tissue objects, and quantify the frequency of two component types being cooccurred in a particular spatial relationship. As they are defined on components, rather than on image pixels, these object cooccurrence features are expected to be less vulnerable to noise and variations that are typically observed at the pixel level of tissue images. Second, it proposes to obtain multiple segmentations by multilevel partitioning of a graph constructed on the tissue objects and combine them by an ensemble function. This multilevel graph partitioning algorithm introduces randomization in graph construction and refinements in its multilevel scheme to increase diversity of individual segmentations, and thus, improve the final result. The experiments on 200 colon tissue images reveal that the proposed approach--the object cooccurrence features together with the multilevel segmentation algorithm--is effective to obtain high-quality results. The experiments also show that it improves the segmentation results compared to the previous approaches.
本文提出了一种新的用于组织病理学图像无监督分割的方法。该方法主要有两个贡献。首先，它引入了一组新的高级纹理特征来表示组织成分空间组织的先验知识。这些纹理特征是在组织成分上定义的，这些成分由组织对象近似表示，并量化了两种成分类型在特定空间关系中同时出现的频率。由于它们是在成分上定义的，而不是在图像像素上定义的，因此这些对象共现特征预计比在组织图像的像素级别上观察到的噪声和变化更不容易受到影响。其次，它建议通过在组织对象上构建的图的多级分区来获得多个分割，并通过集合函数对它们进行组合。这种多级图划分算法在图的构建和其多级方案的细化中引入了随机性，以增加各个分割的多样性，从而提高最终结果。在 200 张结肠组织图像上的实验表明，所提出的方法——对象共现特征结合多级分割算法——对于获得高质量的结果是有效的。实验还表明，与以前的方法相比，它提高了分割结果。
