Semi-automatic boundary detection for identification of cells in DIC microscope images

The work described is motivated by the need to research high rate algal ponds, an environmentally important development in applied microbiology. These are energy-efficient, low-technology waste treatment systems. Achieving the optimum efficiency of such systems relies on a knowledge of the biomass of algae and bacteria in the mixed microbial population of the pond. This is determined by viewing pond samples under a microscope, counting the number and measuring the size of cells, then using standard formulae to estimate the biomass from these measurements. Algal cells are typically clustered and/or overlapping, and a method is needed for accurately separating, identifying and counting individual cells in a sample, while ignoring the noise. The diversity encountered makes it unreasonable to expect that a single automatic algorithm will accurately extract the cell contours in all cases. Semi-automatic procedures are investigated as a means of identifying and sizing cells in differential interference contrast (DIC) microscope images