Facial image classification of mouse embryos for the animal model study of fetal alcohol syndrome

Shiaofen Fang, Ying Liu, Jeffrey Huang, Sophia Vinci-Booher, Bruce Anthony, Feng Zhou

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

3 Scopus citations

Abstract

Fetal Alcohol Syndrome (FAS) is a developmental disorder caused by maternal drinking during pregnancy. Computerize imaging techniques have been applied to study human facial dysmorphology associated with FAS. This paper describes a new facial image analysis method based on a multi-angle image classification technique using micro-video images of mouse embryo. Images taken from several different angles are analyzed separately, and the results are combined for classifications that separate embryos with and without alcohol exposures. Analysis results from animal models provide critical references for the understanding of FAS and potential therapy solutions for human patients.

Original languageEnglish (US)
Title of host publication24th Annual ACM Symposium on Applied Computing, SAC 2009
Pages852-856
Number of pages5
DOIs
StatePublished - Dec 1 2009
Event24th Annual ACM Symposium on Applied Computing, SAC 2009 - Honolulu, HI, United States
Duration: Mar 8 2009Mar 12 2009

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Other

Other24th Annual ACM Symposium on Applied Computing, SAC 2009
CountryUnited States
CityHonolulu, HI
Period3/8/093/12/09

Keywords

  • Algorithms
  • Experimentation
  • Feature extraction
  • Fetal alcohol syndrome
  • Image analysis
  • Machine learning
  • Measurement
  • Medical diagnosis
  • Pattern classification

ASJC Scopus subject areas

  • Software

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  • Cite this

    Fang, S., Liu, Y., Huang, J., Vinci-Booher, S., Anthony, B., & Zhou, F. (2009). Facial image classification of mouse embryos for the animal model study of fetal alcohol syndrome. In 24th Annual ACM Symposium on Applied Computing, SAC 2009 (pp. 852-856). (Proceedings of the ACM Symposium on Applied Computing). https://doi.org/10.1145/1529282.1529463