Accurate nuclear and cell segmentations plays an important role in improving the accuracy of target recognition in microscopic cell images. As the traditional SLIC (Simple Linear Iterative Clustering) algorithm cannot segment microscopic cell images well, an improved SLIC superpixel segmentation algorithm based on gray scale enhancement and regional equalization is proposed. According to the characteristics of microscopic cell images, selecting different transformation parameters with the conditional iterative algorithm, the best classification multi-threshold method based on maximum entropy criterion is used to nonlinearly enhance the gray scale of the original images, while enhancing the contrast of the image, it also greatly improves the balance of each classification region. Then the gray distance and spatial distance are calculated respectively in the circle neighborhood of the cluster center to realize the superpixel segmentation of the image. Finally, the improved SLIC algorithm and the comparison algorithm are tested and evaluated. The experimental results show that our improved SLIC algorithm model has higher segmentation accuracy and is more suitable for cell segmentation in microscopic cell images than original SLIC algorithm.