In this paper a novel parameter optimization approach for cell detection tool and counting cells procedure in phase contrast images are presented. Manual counting of the attached cells in phase contrast images is time-consuming and subjective. For evaluation of electroporation efficiency of attached cells, we often perform manual counting of the cells which is needed to determine the percentage of electroporated cells under different experimental conditions. Here we present an automated cell counting procedure based on novel artificial neural network optimizationof Image-based Tool forCounting Nuclei algorithm parameters to fit the training image set based on counts from an expert. Comparing the results of automated cell counting to user manual counting a 90,31% average agreement was achieved which is reasonably good especially taking intoaccountinter-person errorwhichcanbeupto10%. Evenmore, our procedure can also be used for fluorescent cell imageswith similar counting accuracy (>90%) enabling us to determine electroporation efficiency. In our experiments, the electroporation efficiency determined by manual cell counting was virtually the same as the one obtained by the automated procedure.