Mathematical classification of tight junction protein images

K. H. Ogawa, C. M. Troyer, R. G. Doss, F. Aminian, E. C. Balreira, J. M. King

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Summary: We present the rationale for the development of mathematical features used for classification of images stained for selected tight junction proteins. The project examined localization of zonula occludens-1, claudin-1 and F-actin in a model epithelium, Madin-Darby canine kidney II cells. Cytochalasin D exposure was used to perturb junctional localization by actin cytoskeleton disruption. Mathematical features were extracted from images to reliably reveal characteristic information of the pattern of protein localization. Features, such as neighbourhood standard deviation, gradient of pixel intensity measurement and conditional probability, provided meaningful information to classify complex image sets. The newly developed mathematical features were used as input to train a neural network that provided a robust method of individual image classification. The ability for researchers to make determinations concerning image classification while minimizing human bias is an important advancement for the field of tight junction cellular biology.

Original languageEnglish (US)
Pages (from-to)100-110
Number of pages11
JournalJournal of Microscopy
Volume252
Issue number2
DOIs
StatePublished - Nov 2013
Externally publishedYes

Keywords

  • Claudin
  • Cytochalasin D
  • Cytoskeleton
  • Edge detection
  • Image classification
  • Modeling
  • Neural networks
  • Tight junction
  • ZO-1modeling

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Histology

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