Improved (STEM) cell segmentation with histogram matching image contrast enhancement

The tracking of moving biological cells in time-lapse video sequences is fundamental to further understanding biological processes. Automatic cell tracking techniques require accurate cell image segmentation; however, current segmentation techniques are susceptible to errors due to non-ideal but realistic cell image conditions, including low contrast typical of cell microscopic images. This paper proposes a novel image pre-processing technique to enhance the low grayscale image contrast for improved cell image segmentation accuracy. A shifted bi-Gaussian model is matched to the original cell image intensity histogram for greater differentiation between the cell foreground and image background, whilst maintaining the original intensity histogram shape. Experiments conducted on a stem cell time-lapse image database show up to 33% improved segmentation accuracy, in some frames (partially or completely) detecting cells that manual ground-truth and/or existing segmentation approaches fail to identify.