The aim of this study is to develop and implement a straightforward algorithm for identifying cell boundaries in microscopic images, particularly focusing on the unique intensity patterns often present at cell membranes. This algorithm is designed for deployment on a hardware chip to evaluate its performance in terms of both segmentation accuracy and hardware efficiency. Methods: This research addresses the challenge of cell segmentation in microscopic images by leveraging a simple, yet effective algorithm tailored to detect cell boundaries. Unlike conventional approaches that predominantly utilize the gradient field of the image, our method capitalizes on distinct intensity patterns at cell membranes. The algorithm is integrated into a hardware chip to experimentally test its efficacy. To optimize the use of hardware resources, a resource-sharing technique is applied, aiming to minimize the required area while maintaining accurate boundary identification. Results: The proposed algorithm effectively isolates cell boundaries within microscopic images. When implemented on the hardware chip, it not only maintains high segmentation accuracy but also achieves a significant reduction in the area occupied by the hardware. Through the resource-sharing strategy, we successfully decrease the hardware area requirement by 60%, ensuring that the algorithm remains efficient and accurate in identifying cell boundaries