Separation of preterm infection model from normal pregnancy in mice using texture analysis of Second Harmonic Generation images

S. Yousefi, N. Kehtarnavaz, M. Akins, K. Luby-Phelps, M. Mahendroo

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

6 Scopus citations

Abstract

This paper presents an image processing system to distinguish a lipopolysaccharide (LPS) infection model of preterm labor from normal mouse pregnancy using Second Harmonic Generation (SHG) images of mouse cervix. Two classes of SHG images are considered: images from mice in which premature birth was caused by intrauterine LPS administration and images from normal pregnant mice. A wide collection of image texture features consisting of co-occurrence matrix-based, granulometry-based and wavelet-based are examined. The results obtained indicate that the combination of co-occurrence-based and granulometry-based textures features provides the most effective texture set for separating these two classes of images.

Original languageEnglish (US)
Title of host publication2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Pages5314-5317
Number of pages4
DOIs
StatePublished - Dec 1 2010
Event2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina
Duration: Aug 31 2010Sep 4 2010

Publication series

Name2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

Other

Other2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Country/TerritoryArgentina
CityBuenos Aires
Period8/31/109/4/10

Keywords

  • Bioimaging application
  • Medical image texture feature analysis
  • Preterm labor modeling
  • Second harmonic generation imaging

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Health Informatics

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