Bacterial foraging based edge detection for cell image segmentation

Edge detection is the most popular and common choices for cell image segmentation, in which local searching strategies are commonly used. In spite of their computational efficiency, traditional edge detectors, however, may either produce discontinued edges or rely heavily on initializations. In this paper, we propose a bacterial foraging based edge detection (BFED) algorithm for cell image segmentation. We model the gradients of intensities as the nutrient concentration and propel bacteria to forage along nutrient-rich locations via mimicking the behavior of Escherichia coli, including the chemotaxis, swarming, reproduction, elimination and dispersal. As a nature-inspired evolutionary technique, this algorithm can identify the desired edges and mark them as the tracks of bacteria. We have evaluated the proposed algorithm against the Canny, SUSAN, Verma´s and an active contour model (ACM) based edge detectors on both synthetic and real cell images. Our results suggest that the BFED algorithm can identify boundaries more effectively and provide more accurate cell image segmentation.