A novel automated cell counting technique for cell sample images used to study the side-effects of lens cleaning solutions on human corneal epithelial cells is developed. The proposed multi-step approach integrates non-maximum suppression, seeded region growing, connected component analysis, and adaptive thresholding to produce segmentation and classification results that are robust to background illumination variation and clustering of cells. The proposed algorithm is computationally efficient, and experimental results show that the average detection rate of nucleated cells is greater than 90% with the proposed technique as opposed to the state-of-the-art level set method which gives an accuracy of less than 65%.