High-Content Screening (HCS) is a powerful technology for biological research, which relies heavily on the
capabilities for processing and analysis of cell biology images. The quality of the quantification results, obtained
by analysis of hundreds and thousands of images, is crucial for analysis of biological phenomena under study.
Traditionally, a quality control in the HCS refers to the preparation of biological assay, setting up instrumentation,
and analysis of the obtained quantification results, thus skipping an important step of assessment of the image
quality. So far, only few papers have been addressing this issue, but no standard methodology yet exists, that
would allow pointing out images, potentially producing outliers when processed. In this research the importance
of the image quality control for the HCS is emphasized, with the following possible advantages: (a) validation
of the visual quality of the screening; (b) detection of the potentially problem images; (c) more accurate setting
of the processing parameters. For the detection of outlier images the Power Log-Log Slope (PLLS) is applied,
as it is known to be sensitive to the focusing errors, and validated using open data sets. The results show that
PLLS correlates with the cell counting error and, when taken it into account, allows reducing the variance of
measurements. Possible extensions and problems of the approach are discussed.