2-D maximum entropy spermatozoa image segmentation based on Canny operator

Aiming at the problems of sperm microscopic imaging that is unclear, low-contrast and involves many impurities, a two-dimensional maximum entropy segmentation based on canny operator is presented. First, the initial sperm image segmentation is made according to its features. Next, the real spermatozoa edge is detected by using Canny operator. Finally, the 2-D maximum entropy segmentation is used for the segmentation of sperm image targets and their edges according to different thresholds, thus solving the imprecise segmentation problems because of the local blurred and overlapping spermatozoa image. The experiments show that this spermatozoa image segmentation is of good robustness and high segmentation accuracy.