Sperm Motility Analysis by using Recursive Kalman Filters with the smartphone based data acquisition and reporting approach

Abstract Semen analysis is currently performed by using two techniques. Visual assessment technique is manual observation based technique and strongly depends on the experiences of the observer. Therefore, the reliability of the results is skeptical. On the other hand, computer based expert systems are more consistent and reliable. However, they are very expensive systems, therefore, cannot be utilized in many laboratories. In this study, we proposed a hybrid expert system utilizing visual assessment environment with the computerized analyzing part to eliminate the disadvantages of each technique. In the proposed system, smartphone based data acquisition approach is used to provide more modular and practical expert system for the sperm analysis. The records are, then, transferred to the server to analyze by developed software. In this analyzing software, we proposed multi-stage hybrid analyzing approach in terms of video stabilization, sperm concentration and motility analysis. Each video was initially fixed by the Speed Up Robust Features based matching technique. Then, Kalman Filter was employed for sperm tracking. After tracking step, trajectories have been divided into 3 s length to prevent possible incorrect assignments due to sudden changes in sperm motions. In the experimental tests, we combined all trajectories obtained from a total of 18 videos of 6 different subjects. We clustered a total of 89438 trajectories into 4 cluster as fast progressive, progressive, non-progressive and immotile according to extracted seven features. In order to compare the results, we also analyzed the same semen sample in another expert system, SQA-Vision. The difference was measured 3.4% and 4.8% in the determination of total and motile sperm concentration, and 2.1%, 7.4%, 5.3% for progressive, non-progressive and immotile movement type analysis respectively. The significance and impact of the proposed system are capability of reporting more detailed results in a variety of situations and having more advantages than any expert systems utilized for sperm analysis in terms of portability, cost and modularity. Additionally, to the best of our knowledge, this is the first study reporting use of the smartphone in an expert system for the sperm analysis in terms of data acquisition and result reporting.

[1]  Clement Leung,et al.  Quantitative Analysis of Locomotive Behavior of Human Sperm Head and Tail , 2013, IEEE Transactions on Biomedical Engineering.

[2]  Moshe Kam,et al.  Automatic Tracking and Motility Analysis of Human Sperm in Time-Lapse Images , 2017, IEEE Transactions on Medical Imaging.

[3]  Stephen T. C. Wong,et al.  Microscopic Image Analysis for Life Science Applications , 2008 .

[4]  Laura Fernández-Robles,et al.  Acrosome integrity assessment of boar spermatozoa images using an early fusion of texture and contour descriptors , 2015, Comput. Methods Programs Biomed..

[5]  Moshe Kam,et al.  Tracking of Human Sperm in Time-Lapse Images , 2018, 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP).

[6]  S. C. Johnson Hierarchical clustering schemes , 1967, Psychometrika.

[7]  Seyed Abolghasem Mirroshandel,et al.  An efficient method for automatic morphological abnormality detection from human sperm images , 2015, Comput. Methods Programs Biomed..

[8]  A Makler,et al.  The improved ten-micrometer chamber for rapid sperm count and motility evaluation. , 1980, Fertility and sterility.

[9]  Iwan Awaludin,et al.  Automatic sperm motility measurement , 2015, 2015 International Conference on Information Technology Systems and Innovation (ICITSI).

[10]  Lin Ma,et al.  Hybrid generative-discriminative learning for online tracking of sperm cell , 2016, Neurocomputing.

[11]  Rupert P Amann,et al.  Computer-assisted sperm analysis (CASA): capabilities and potential developments. , 2014, Theriogenology.

[12]  Y. Bar-Shalom,et al.  The probabilistic data association filter , 2009, IEEE Control Systems.

[13]  Hamza Osman Ilhan,et al.  Smartphone based sperm counting - an alternative way to the visual assessment technique in sperm concentration analysis , 2019, Multimedia Tools and Applications.

[14]  Reza Fazel-Rezai,et al.  Occlusion Robust Low-Contrast Sperm Tracking Using Switchable Weight Particle Filtering , 2014 .

[15]  Nizamettin Aydin,et al.  A novel data acquisition and analyzing approach to spermiogram tests , 2018, Biomed. Signal Process. Control..

[16]  Masatoshi Ishikawa,et al.  How to track spermatozoa using high-speed visual feedback , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Fethullah Karabiber,et al.  Automatic Detection of Regions of Interest in Makler Images by Combinational Approach and Sperms Analysis by Fuzzy C-Means , 2017, Current Medical Imaging Reviews.

[18]  Łukasz Witkowski,et al.  A computer system for a human semen quality assessment , 2013 .

[19]  Leonardo F. Urbano Robust Automatic Multi-Sperm Tracking in Time-Lapse Images , 2014 .

[20]  Nicolai Petkov,et al.  Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ , 2013, Comput. Methods Programs Biomed..

[21]  Michael Möller,et al.  A framework for automated cell tracking in phase contrast microscopic videos based on normal velocities , 2012, J. Vis. Commun. Image Represent..

[22]  Rafael Grompone von Gioi,et al.  LSD: A Fast Line Segment Detector with a False Detection Control , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Yuanqing Xia,et al.  Kalman Filter With Recursive Covariance Estimation—Sequentially Estimating Process Noise Covariance , 2014, IEEE Transactions on Industrial Electronics.

[24]  O. L. Savkay,et al.  Analysis of sperm motility with CNN architecture , 2012, 2012 13th International Workshop on Cellular Nanoscale Networks and their Applications.

[25]  Víctor González-Castro,et al.  Texture and moments-based classification of the acrosome integrity of boar spermatozoa images , 2012, Comput. Methods Programs Biomed..

[26]  Xi Chen,et al.  Automatic human spermatozoa detection in microscopic video streams based on OpenCV , 2012, 2012 5th International Conference on BioMedical Engineering and Informatics.

[27]  Aniati Murni Arymurthy,et al.  Visual Tracking for Abrupt Motions of Human Sperm Using Smoothing Stochastic Approximate Monte Carlo , 2015 .

[28]  Xavier Descombes,et al.  Head tracking and flagellum tracing for sperm motility analysis , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[29]  V. Shanthi,et al.  Spermatozoa detection, counting and tracking in video streams to detect asthenozoospermia , 2010, 2010 International Conference on Signal and Image Processing.

[30]  J. Smith,et al.  Evaluation of sperm concentration by the hemacytometer method; comparison of four counting fluids. , 1955, Fertility and sterility.

[31]  Nancy Hitschfeld-Kahler,et al.  Gold-standard and improved framework for sperm head segmentation , 2014, Comput. Methods Programs Biomed..