Classification and clustering of human sperm swimming patterns

Ji Won Choi, Chizhong Wang, Leonardo F. Urbano, Puneet Masson, Matthew Vermilyea, Moshe Kam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The principal observed progressive swim types of sperm cells are linear mean and circular swim. Using motility characteristic parameters produced by CASA systems, we perform a parameter subset search to produce distinct clusters of the different swim types. For this task, the artificial bee colony algorithm (an iterative search algorithm modeled after the collective behavior of bees) and the well-studied k-means clustering algorithm were used on simulated and human sperm swim data. The result is distinct clusters with features of each types of swim. The clustering approach displays potential as a tool for automated sperm swim subpopulation analysis.

Original languageEnglish (US)
Title of host publication2018 IEEE 3rd International Conference on Signal and Image Processing, ICSIP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages192-196
Number of pages5
ISBN (Electronic)9781538663943
DOIs
StatePublished - Jan 2 2019
Event2018 IEEE 3rd International Conference on Signal and Image Processing, ICSIP 2018 - Shenzhen, China
Duration: Jul 13 2018Jul 15 2018

Publication series

Name2018 IEEE 3rd International Conference on Signal and Image Processing, ICSIP 2018

Conference

Conference2018 IEEE 3rd International Conference on Signal and Image Processing, ICSIP 2018
CountryChina
CityShenzhen
Period7/13/187/15/18

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing

Keywords

  • Artificial bee colony algorithm
  • Clustering
  • Particle tracking
  • Sperm imaging
  • Sperm motility

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