Automated image-based cytometry with fluorescence-stained specimens

Abstract

The combination of digitized microscopy, algorithms for object recognition and fluorescent labeling is a promising approach for reliable, quick, automated and cost-effective screening of clinical specimens. We describe two conceptually different algorithms for detecting objects in fluorescence microscopic images. One, which is partially automated, compares a mask that represents a typical object with every position in the image; the other, which is fully automated, calculates threshold intensities to segment the image into regions of objects and background. Applications of the algorithms in conjunction with a prototype image-based cytometer are demonstrated for determining the DNA ploidy distribution of cultured human endometrial cells and determining the DNA ploidy distribution and the fraction of cells expressing the E6 antigen of human papilloma virus serotypes 16 and 18 in a PAP smear. The encouraging results from this study suggest that automated image-based cytometry utilizing fluorescent stains will be a valuable asset for clinical screening.