A Hybrid Classification Model for Digital Pathology Using Structural and Statistical Pattern Recognition
A Hybrid Classification Model for Digital Pathology Using Structural and Statistical Pattern Recognition
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DOI：
10.1109/tmi.2012.2230186
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发表时间：
2013-02-01
影响因子：
10.6
通讯作者：
Gunduz-Demir, Cigdem
中科院分区：
文献类型：
--
作者：
Ozdemir, Erdem;Gunduz-Demir, Cigdem
癌症导致细胞分布的偏差，导致它们形成的生物结构发生变化。这些结构的正确定位和特征对于准确的癌症诊断和分级至关重要。在本文中，我们介绍了一种有效的混合模型，它结合了结构和统计模式识别技术来定位和表征组织图像中的生物结构，用于组织量化。为此，该混合模型定义了组织图像的属性图和一组查询图作为正常生物结构的参考。然后，它通过在整个组织图上搜索查询图来定位与正常生物结构最相似的关键区域。与传统方法不同，该混合模型通过使用结构和统计技术提取两种不同类型的特征来量化所定位的关键区域。第一种类型包括将图形编辑距离嵌入到查询图中，而第二种类型包括关键区域的纹理特征。使用结肠组织图像，我们的实验表明，与仅使用统计技术进行组织量化的传统方法相比，所提出的混合模型具有更高的分类精度。
Cancer causes deviations in the distribution of cells, leading to changes in biological structures that they form. Correct localization and characterization of these structures are crucial for accurate cancer diagnosis and grading. In this paper, we introduce an effective hybrid model that employs both structural and statistical pattern recognition techniques to locate and characterize the biological structures in a tissue image for tissue quantification. To this end, this hybrid model defines an attributed graph for a tissue image and a set of query graphs as a reference to the normal biological structure. It then locates key regions that are most similar to a normal biological structure by searching the query graphs over the entire tissue graph. Unlike conventional approaches, this hybrid model quantifies the located key regions with two different types of features extracted using structural and statistical techniques. The first type includes embedding of graph edit distances to the query graphs whereas the second one comprises textural features of the key regions. Working with colon tissue images, our experiments demonstrate that the proposed hybrid model leads to higher classification accuracies, compared against the conventional approaches that use only statistical techniques for tissue quantification.