One treatment for limbal stem cell deficiency (LSCD) involves culturing limbal stem cells (LSCs) ex vivo and transplanting the cultivated LSCs into the patient’s eye. The transplant’s success depends on how many LSCs are present in the graft. LSCs are smaller than differentiated corneal epithelial cells, therefore, cell size is one way to characterize the phenotype of cells in culture. Currently, a program exists that measures cell size; however, the program is not automated and thus subject to error. The goal of this project is to code a program that can accurately measure the size of cultivated LSCs, reducing the need for human input in evaluating the LSC population in cultivated cells. This program will use the packages matplotlib, Numpy, skimage, OpenCV, and pandas in the Anaconda Python3 distribution, brightfield images of dissociated LSCs undergo pre-processing through noise reduction and black balance to accurately identify the cell border. Then, through additional processing, the images give the circularity and diameter of the LSCs. Data analysis excludes cells with low circularity and outputs the percentage of cells <12 µm in diameter. The initial results suggest that our program is more efficient at segmentation and improves the results of noise reduction. This program is potentially very beneficial to the field of LSCD treatment. The fully automatic program will accurately measure the size of cultivated LSCs, which would characterize the population of LSCs in culture. In the future, this program could be extended to measure other types of cells.