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: Contribution to journalArticlepeer-review

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.

ASJC Scopus subject areas

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

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