Identification and sizing of cells in microscope images by template matching and edge detection

The work described here results from a need in research into high rate algal ponds, an environmentally important development in applied microbiology. These are simple, energy efficient, low-technology waste treatment systems. Achieving optimum efficiency of such systems relies on knowledge of the biomass of algae and bacteria in the mixed microbial populations of the pond. This is determined by viewing pond samples under a microscope, counting the number and measuring the size of cells and using standard formulae to estimate biomass from these measurements. The current methods for identifying, counting and measuring cells are at best semi-automatic and slow. No fully automatic method has so far provided successful due to the complex nature of the microscope images, for example presence of different types and shapes of cells, blurring from out-of-focus cells, presence of detritus material etc. Also, algal cells are typically clustered and/or overlapping. Methods are needed for accurately separating, identifying and counting individual cells in a sample, while ignoring noise, as a first step towards estimation of biomass directly from the microscope image. Ideally such methods would be applicable to all microscope modalities eg. brightfield, differential interference contrast (DIC), phase contrast and epifluorescence microscopy