A software package to improve image quality and isolation of objects of interest for quantitative stereology studies of rat hepatocarcinogenesis

Abstract

In the studies of quantitative stereology of rat hepatocarcinogenesis, we have used image analysis technology (automatic particle analysis) to obtain data such as liver tissue area, size and location of altered hepatic focal lesions (AHF), and nuclei counts. These data are then used for three-dimensional estimation of AHF occurrence and nuclear labeling index analysis. These are important parameters for quantitative studies of carcinogenesis, for screening and classifying carcinogens, and for risk estimation. To take such measurements, structures or cells of interest should be separated from the other components based on the difference of color and density. Common background problems seen on the captured sample image such as uneven light illumination or color shading can cause severe problems in the measurement. Two application programs (BK_Correction and Pixel_Separator) have been developed to solve these problems. With BK_Correction, common background problems such as incorrect color temperature setting, color shading, and uneven light illumination background, can be corrected. With Pixel_Separator different types of objects can be separated from each other in relation to their color, such as seen with different colors in immunohistochemically stained slides. The resultant images of such objects separated from other components are then ready for particle analysis. Objects that have the same darkness but different colors can be accurately differentiated in a grayscale image analysis system after application of these programs.