Automated Confocal Microscope Bias Correction

Illumination artifacts systematically occur in 2D cross‐section confocal microscopy imaging . These bias can strongly corrupt an higher level image processing such as a segmentation, a fluorescence evaluation or even a pattern extraction/recognition. This paper presents a new fully automated bias correction methodology based on large image database preprocessing. This method is very appropriate to the High Content Screening (HCS), method dedicated to drugs discovery. Our method assumes that the amount of pictures available is large enough to allow a reliable statistical computation of an average bias image. A relevant segmentation evaluation protocol and experimental results validate our correction algorithm by outperforming object extraction on non corrupted images.