Title
Mathematical Classification of Tight Junction Protein Images
Document Type
Post-Print
Publication Date
11-2013
Abstract
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 neighborhood 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.
DOI
10.1111/jmi.12074
Publisher
Wiley-Blackwell
Repository Citation
Ogawa, K. H., Troyer, C. M., Doss, R. G., Aminian, F., Balreira, E. C., & King, J. M. (2013). Mathematical classification of tight junction protein images. Journal of Microscopy, 252(2), 100-110. http://doi.org/10.1111/jmi.12074
Publication Information
Journal of Microscopy