Texture measures combination for improved meningioma classification of histopathological images
Texture measures combination for improved meningioma classification of histopathological images
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DOI：
10.1016/j.patcog.2010.01.005
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发表时间：
2010-06-01
影响因子：
8
通讯作者：
Al-Kadi, Omar S.
中科院分区：
文献类型：
--
作者：
Al-Kadi, Omar S.
提供一种改进的技术，可以帮助病理学家正确分类脑膜瘤肿瘤具有显著的准确性是我们的主要目标。该方法基于最优纹理测度组合，检测RGB颜色通道的可分离性，选择最适合分割组织病理图像细胞核的通道。形态学梯度用于提取每个亚型的感兴趣区域，并消除活检准备过程中可能发生的噪声（例如裂纹）。采用4种不同的纹理测度（2种基于模型的纹理测度和2种基于统计的纹理测度）提取脑膜瘤纹理特征，剔除高相关特征后将相应特征以不同组合方式融合在一起，并采用贝叶斯分类器进行脑膜瘤亚型判别。结合高斯马尔可夫随机场和运行长度矩阵纹理测量在定量表征脑膜瘤组织方面优于所有其他组合，实现了92.50%的总体分类准确率，而如果单独使用纹理测量，则达到了83.75%的最佳准确率。(C) 2010 Elsevier Ltd.版权所有。
Providing an improved technique which can assist pathologists in correctly classifying meningioma tumours with a significant accuracy is our main objective. The proposed technique, which is based on optimum texture measure combination, inspects the separability of the RGB colour channels and selects the channel which best segments the cell nuclei of the histopathological images. The morphological gradient was applied to extract the region of interest for each subtype and for elimination of possible noise (e.g. cracks) which might occur during biopsy preparation. Meningioma texture features are extracted by four different texture measures (two model-based and two statistical-based) and then corresponding features are fused together in different combinations after excluding highly correlated features, and a Bayesian classifier was used for meningioma subtype discrimination. The combined Gaussian Markov random field and run-length matrix texture measures outperformed all other combinations in terms of quantitatively characterising the meningioma tissue, achieving an overall classification accuracy of 92.50%, improving from 83.75% which is the best accuracy achieved if the texture measures are used individually. (C) 2010 Elsevier Ltd. All rights reserved.