In order to solve the uneven illumination of phase contrast microscopic cell images, achieving the goals of accurate and fast cell image segmentation and automatic counting, this paper proposes a method of image illumination equalization combined with double threshold segmentation and counting. The method chunks the image, adjusts the brightness based on the ratio of sub-blocks to the average gray level of the whole image, and optimizes the ratio by using Gaussian function weights to eliminate the “square effect” caused by image chunks, and determines the optimal standard deviation by the relationship curve between image entropy and Gaussian function standard deviation. The images were segmented using a double-threshold method to optimize the morphology of the restored cells by combining cavity filling and area constraints. The algorithm was tested using the C2C12 phase-difference microscopic cell image dataset, in which the segmentation accuracy, recall and F-value of the high cell density images were 0. 966 2, 0. 967 8 and 0. 967 0, respectively, which were significantly better than other comparative methods. The results showed that the method could achieve light equalization when processing the light inhomogeneous phase contrast cell images of different cell densities, with less image information loss, high-accuracy cell-segmentation and counting results.