Cell density estimation from a still image for in-situ microscopy

The paper investigates an algorithm to estimate the cell density (cell count) from a still intensity image captured by an in-situ microscope directly from inside a bioreactor. In comparison with other algorithms, ours has the advantage that it allows a reliable cell density estimation, even though the cells build clusters in the scene. First, image regions containing at least one cell are segmented by applying a maximum-likelihood thresholding technique. Then, the cell density inside each segmented region is estimated by maximizing the variance of the circular Hough transform of the edges inside it. The edges are extracted by applying the smallest univalue segment assimilating nucleus algorithm (SUSAN). The total cell density is the sum of the cell densities estimated inside the segmented regions. The proposed algorithm has been implemented and applied to thousands of real images of cultures of mammalian baby hamster kidney cells (BHK cells) captured by an in-situ microscope. The average of the percentage of the absolute cell density estimation error was 6.27%. The estimates are similar to those obtained with current off-the-shelf cell density monitoring instruments for cultures up to cell densities of 5/spl times/10/sup 6/ cells/ml.