Cell Segmentation Based on FOPSO Combined With Shape Information Improved Intuitionistic FCM

Fuzzy c-means (FCM) clustering algorithms have been proved to be effective image segmentation techniques. However, FCM clustering algorithms are sensitive to noises and initialization. They cannot effectively segment cell images with inhomogeneous gray value distributions and complex touching cells. Aiming to overcome these disadvantages, this paper proposes a cell image segmentation algorithm using fractional-order velocity based particle swarm optimization (FOPSO) combined with shape information improved intuitionistic FCM (SI-IFCM) clustering. Iterations are carried out between FOPSO and SI-IFCM to achieve final cell segmentation. Experimental results demonstrate that the proposed algorithm has advantages on cell image segmentation, with the highest recall (90.25%) and lowest false discovery rate (0.28%) compared with the state-of-the-art algorithms.