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 journalArticle

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.

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Harmonic generation
Textures
Image texture
Pregnancy
Infection
Lipopolysaccharides
Image processing
Personnel
Premature Obstetric Labor
Premature Birth
Cervix Uteri

ASJC Scopus subject areas

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

Cite this

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title = "Separation of preterm infection model from normal pregnancy in mice using texture analysis of second harmonic generation images.",
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.",
author = "S. Yousefi and N. Kehtarnavaz and M. Akins and K. Luby-Phelps and M. Mahendroo",
year = "2010",
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AU - Akins, M.

AU - Luby-Phelps, K.

AU - Mahendroo, M.

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AB - 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.

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