Development of a multistage classifier for a monitoring system of cell activity based on imaging of chromosomal...

Cell-based assays utilizing digital image cytometry yield multivariate sets of information measuring the efficacy of medicines/chemicals. The use of a HeLa cell line that expresses a GFP-Histone-H1 fusion protein further enhances the performance of these systems, avoiding the use of dyes that may have detrimental influence on cells. Aside from the mitotic index, the distribution of the cell-cycle phases during mitosis can be used as measures of drug/treatment efficacy. Quantification of these parameters, however, requires skill and is time consuming. The purpose of this research was therefore to create a classifier to be incorporated into a system that can automatically identify the cell-cycle phases in a given image.

Features based on the shape and texture of the chromosomal regions in images of live HeLa cells were measured and analyzed. Linear discriminant functions were calculated for the eight cell-cycle phases: interphase, prophase, prometaphase, metaphase, early anaphase, anaphase, telophase and cytokinesis.

The multistage linear discriminant classifier developed had an average classification efficiency of 87.30%.

We demonstrated the possibility of creating a classifier to discriminate between cell-cycle phases using shape and texture features of chromosomal regions. The classifier can be fused to an algorithm for image segmentation, forming a system to automatically and rapidly measure the aforementioned parameters. The results can then be collated to constitute an assay assessing the effects of a drug or treatment on mammalian cells.