Wu Chunyan, Zhao Weizhao, Lin Baowan, Ginsberg Myron D
Department of Biomedical Engineering, University of Miami, Coral Gables, FL 33124-0621, USA.
Comput Methods Programs Biomed. 2005 Apr;78(1):75-86. doi: 10.1016/j.cmpb.2004.12.005.
Immunochemical staining techniques are commonly used to assess neuronal, astrocytic and microglial alterations in experimental neuroscience research, and in particular, are applied to tissues from animals subjected to ischemic stroke. Immunoreactivity of brain sections can be measured from digitized immunohistology slides so that quantitative assessment can be carried out by computer-assisted analysis. Conventional methods of analyzing immunohistology are based on image classification techniques applied to a specific anatomic location at high magnification. Such micro-scale localized image analysis limits one for further correlative studies with other imaging modalities on whole brain sections, which are of particular interest in experimental stroke research. This report presents a semi-automated image analysis method that performs convolution-based image classification on micro-scale images, extracts numerical data representing positive immunoreactivity from the processed micro-scale images and creates a corresponding quantitative macro-scale image. The present method utilizes several image-processing techniques to cope with variances in intensity distribution, as well as artifacts caused by light scattering or heterogeneity of antigen expression, which are commonly encountered in immunohistology. Micro-scale images are composed by a tiling function in a mosaic manner. Image classification is accomplished by the K-means clustering method at the relatively low-magnification micro-scale level in order to increase computation efficiency. The quantitative macro-scale image is suitable for correlative analysis with other imaging modalities. This method was applied to different immunostaining antibodies, such as endothelial barrier antigen (EBA), lectin, and glial fibrillary acidic protein (GFAP), on histology slides from animals subjected to middle cerebral artery occlusion by the intraluminal suture method. Reliability tests show that the results obtained from immunostained images at high magnification and relatively low magnification are virtually the same.
免疫化学染色技术在实验神经科学研究中常用于评估神经元、星形胶质细胞和小胶质细胞的变化，特别是应用于遭受缺血性中风的动物组织。脑切片的免疫反应性可从数字化免疫组织学载玻片上进行测量，以便通过计算机辅助分析进行定量评估。传统的免疫组织学分析方法基于在高倍放大下应用于特定解剖位置的图像分类技术。这种微观尺度的局部图像分析限制了其与全脑切片上的其他成像方式进行进一步相关性研究，而这在实验性中风研究中尤为重要。本报告提出了一种半自动图像分析方法，该方法对微观尺度图像进行基于卷积的图像分类，从处理后的微观尺度图像中提取代表阳性免疫反应性的数值数据，并创建相应的定量宏观尺度图像。本方法利用多种图像处理技术来应对强度分布的差异，以及免疫组织学中常见的由光散射或抗原表达异质性引起的伪影。微观尺度图像通过平铺功能以镶嵌方式组成。图像分类通过K均值聚类方法在相对低倍放大的微观尺度水平上完成，以提高计算效率。定量宏观尺度图像适用于与其他成像方式进行相关性分析。该方法应用于不同的免疫染色抗体，如内皮屏障抗原（EBA）、凝集素和胶质纤维酸性蛋白（GFAP），用于通过腔内缝合方法遭受大脑中动脉闭塞的动物的组织学载玻片。可靠性测试表明，在高倍放大和相对低倍放大下从免疫染色图像获得的结果几乎相同。
