Department of Biological Engineering, University of Missouri, Columbia, Missouri, USA.
Toxicol Mech Methods. 2004;14(5):301-7. doi: 10.1080/15376520490434638.
Color and geometric characteristics of stained areas in histochemical slides are among the features pathologists assess to evaluate the severity of lesions. In this research, image processing techniques were used to perform objective quantification of these characteristics in images of H&E-stained spleen tissues. A segmentation algorithm was developed to isolate the areas of interest in microscopic tissue images. Image features important to pathological evaluation were then extracted. These features were used to build statistical and neural network models to predict pathologist scores. A linear regression model predicted the scores to an R(2)-value of 0.6, and a neural network model classified samples to an accuracy of 75%. The results show the usefulness of image processing as a tool for pathological evaluation.
染色组织切片中染色区域的颜色和几何特征是病理学家评估病变严重程度时评估的特征之一。在这项研究中，使用图像处理技术对 H&E 染色的脾脏组织图像中的这些特征进行客观量化。开发了一种分割算法来分离显微镜组织图像中的感兴趣区域。然后提取对病理评估很重要的图像特征。这些特征被用于构建统计和神经网络模型来预测病理学家的评分。线性回归模型预测评分的 R(2)-值为 0.6，神经网络模型分类样本的准确率为 75%。结果表明，图像处理作为病理评估工具的有用性。
