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首页> 美国卫生研究院文献>Analytical Cellular Pathology : the Journal of the European Society for Analytical Cellular Pathology >Image Analysis Based Grading of Bladder Carcinoma. Comparison of Object, Texture and Graph Based Methods and Their Reproducibility
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Image Analysis Based Grading of Bladder Carcinoma. Comparison of Object, Texture and Graph Based Methods and Their Reproducibility

机译：基于图像分析的膀胱癌分级。基于对象，纹理和图形的方法及其可重复性的比较

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摘要

The possibility that computerized image analysis could increase the reproducibility of grading of bladder carcinoma as compared to conventional subjective grading made by pathologists was investigated. Object, texture and graph based analysis were carried out from Feulgen stained histological tissue sections. The object based features were extracted from gray scale images, binary images obtained by thresholding the nuclei and several other images derived through image processing operations. The textural features were based on the spatial gray‐tone co‐occurrence probability matrices and the graph based features were extracted from the minimum spanning trees connecting all nuclei. The large numbers of extracted features were evaluated in relation to subjective grading and to factors related to prognosis using multivariate statistical methods and multilayer backpropagation neural networks. All the methods were originally developed and tested on material from one patient and then tested for reproducibility on entirely different patient material. The results indicate reasonably good reproducibility for the best sets of features. In addition, image analysis based grading showed almost identical correlation to mitotic density and expression of p53 protein as subjective grading. It should thus be possible to use this kind of image analysis as a prognostic tool for bladder carcinoma.
机译：与病理学家进行的常规主观分级相比，计算机图像分析可以提高膀胱癌分级的可重复性。从Feulgen染色的组织学切片进行基于对象，纹理和图形的分析。从灰度图像，通过阈值化核获得的二进制图像以及通过图像处理操作得出的其他一些图像中提取了基于对象的特征。纹理特征是基于空间灰度共现概率矩阵，而基于图的特征是从连接所有原子核的最小生成树中提取的。使用多变量统计方法和多层反向传播神经网络，对与主观等级和与预后相关的因素进行了评估的大量提取特征。所有方法最初都是在一名患者的材料上开发和测试的，然后在完全不同的患者材料上进行了重现性测试。结果表明，最佳功能集具有相当好的再现性。另外，基于图像分析的分级显示与有丝分裂密度和p53蛋白表达的主观分级几乎相同的相关性。因此，应该有可能将这种图像分析用作膀胱癌的预后工具。

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