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Microscopic tissue image processing for pathological evaluation.

机译：显微组织图像处理，用于病理评估。

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摘要

Image processing techniques were developed for pathological tissue analysis. The work included three main parts: analysis of tissue images stained with Perl's Prussian blue, development of object density-based segmentation algorithms for tissue image segmentation, and analysis of tissue images stained with hematoxylin and eosin.; For Perl's Prussian blue-stained tissue images, the blue areas were segmented based on color attributes. A series of image features were extracted to describe the subjective concept of “blueness”, an important attribute for pathological evaluation of blue-stained tissues. The features were selected through statistical analysis. Both statistical and neural network models were developed to predict expert pathological scores from the image features. The neural network model predicted pathological scores to an R2-value of 0.86.; Three object density-based algorithms were developed to segment images according to the spatial density of objects such as nuclei. The three algorithms were respectively based on primitive growing, wavelet transform, and influence zones. The algorithms were tested with both synthesized and real images. The test results showed the algorithms were effective, with the primitive-grow-based method performing better than the other two.; For hematoxylin and eosin-stained spleen sections, red pulps were segmented from white pulps based on differences in color and the lymphocyte density between red pulps and white pulps. Image features corresponding to a number of pathological attributes were extracted. Statistical and neural network methods were used to develop models for prediction of a comprehensive pathological score. The R2 value of prediction was 0.64 for a regression model and 0.75 for a neural network model. The neural network model showed advantage in comprehensive pathological score prediction.
机译：图像处理技术被开发用于病理组织分析。这项工作包括三个主要部分：分析用Perl普鲁士蓝染色的组织图像，开发基于对象密度的用于组织图像分割的分割算法，以及分析用苏木精和曙红染色的组织图像。对于Perl的普鲁士蓝染色的组织图像，根据颜色属性对蓝色区域进行了分割。提取了一系列图像特征来描述“蓝色”的主观概念，“蓝色”是蓝色染色组织病理评估的重要属性。通过统计分析选择特征。统计和神经网络模型均已开发，可根据图像特征预测专家病理评分。神经网络模型预测病理评分为R 2 值0.86。研发了三种基于物体密度的算法，可根据物体（如核）的空间密度对图像进行分割。这三种算法分别基于原始增长，小波变换和影响区。使用合成图像和真实图像对算法进行了测试。测试结果表明，该算法是有效的，基于原始增长的方法性能优于其他两种。对于苏木精和曙红染色的脾脏切片，根据颜色和红浆与白浆之间的淋巴细胞密度差异，将白浆与红浆分开。提取对应于许多病理属性的图像特征。使用统计和神经网络方法来开发用于预测综合病理评分的模型。回归模型的预测R 2 值为0.64，而神经网络模型的预测值为0.75。神经网络模型在综合病理评分预测中显示出优势。

著录项

  • 作者

    Liu, Xiaoqiu.;

  • 作者单位

    University of Missouri - Columbia.;

  • 授予单位 University of Missouri - Columbia.;
  • 学科 Engineering Agricultural.
  • 学位 Ph.D.
  • 年度 2000
  • 页码 p.6580
  • 总页数 143
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 农业工程;
  • 关键词

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