Automated selection of metaphase cells by quality.

The performance of metaphase-finding systems could be improved if they were able to determine the quality of the cells detected. This paper discusses the extent to which this may be realized by the introduction of a metaphase-quality parameter. Data obtained from 300 cells were statistically analyzed. Seventeen features were measured and nine visual properties were determined for each cell. Discriminant analysis and regression analysis were used to extract those features and visual properties which contribute to assessment of metaphase quality. Rather low correlations were found between the selected measured features and visual properties. A quality-parameter based on a linear combination of cluster projections, areas and perimeters was found to account for 64% of the variation between visual and measured quality indicators. In addition, an increase in the predictive value for finding usable metaphases from 28-68% was achieved.