Automatic Sperm Analysis in Microscopic Images of Human Semen: Segmentation Using Minimization of Information Distance

• Main Authors: Seyed Vahab Shojaedini, Masoud Heydari
• Format: Article
• Language: English
• Published: Mashhad University of Medical Sciences 2014-08-01
• Series: Iranian Journal of Medical Physics
• Subjects: Entropy; Infertility; Microscopy; Semen; Spermatozoa
• Online Access: http://ijmp.mums.ac.ir/article_3104_bd81571759cc3f88f247343dc317be78.pdf

Introduction The morphologic features of human sperms are key indicators for monitoring fertility problems in men. Therefore, automated analyzing methods via microscopic videos have become the most favorite policy in infertility treatment during the last decades. Materials and Methods In the proposed method, firstly a hypothesis testing framework was defined to distinguish sperms from background. Then, some regions were selected as candidates by minimization of the information distance between the original and processed images. Finally, the correct sperms were extracted from candidates using a watershed-based algorithm. Results The proposed, Watershed Segmentation Algorithm (WSA), Multi Structure Element Segmentation (MSES) and Dynamic Threshold Segmentation (DTS) algorithms achieve true positive rates of 96%, 84%, 81%, and 70%, respectively, versus typically 3% of false positive rate in semen specimens with high density of sperms. The true positive rates of 87%, 69%, 66%, and 52%, respectively, at the same false positive rate were obtained for the semen specimens with high density of sperms. Conclusion Results show that false positive rates of the proposed algorithm were at least 8% (in the first scenario) and 32% (in the second scenario) better than other methods considering the minimum acceptable true positive rate of 90%. Furthermore, it has been shown that the proposed algorithm extracted sperms at least 12% (in the first scenario) and 18% (in the second scenario) better than other methods in presence of a typically low false positive rate equal to 3%.