Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection
Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection
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DOI：
10.1016/j.patcog.2008.07.007
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发表时间：
2009-06-01
影响因子：
8
通讯作者：
Gunduz-Demir, Cigdem
中科院分区：
文献类型：
--
作者：
Tosun, Akif Burak;Kandemir, Melih;Gunduz-Demir, Cigdem
病理学中常规使用的染色方法导致组织病理学图像的生物学不同区域中的相似颜色分布。这在用于癌症的定量分析和检测的图像分割中引起问题。为了克服这个问题，与以前的方法，使用像素分布，我们提出了一个新的均匀性措施的基础上，我们定义为代表组织成分的对象的分布。使用这种措施，我们展示了一个新的面向对象的分割算法。与结肠活检图像的工作，我们表明，该算法分割癌和正常区域的平均准确率为94.89%，并显着提高了分割精度相比，其基于像素的对应。(C)2008爱思唯尔有限公司保留所有权利。
Staining methods routinely used in pathology lead to similar color distributions in the biologically different regions of histopathological images. This causes problems in image segmentation for the quantitative analysis and detection of cancer. To overcome this problem, unlike previous methods that use pixel distributions, we propose a new homogeneity measure based on the distribution of the objects that we define to represent tissue components. Using this measure, we demonstrate a new object-oriented segmentation algorithm. Working with colon biopsy images, we show that this algorithm segments the cancerous and normal regions with 94.89 percent accuracy on the average and significantly improves the segmentation accuracy compared to its pixel-based counterpart. (C) 2008 Elsevier Ltd. All rights reserved.