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RI | Publications | Application of Computational Symmetry to Histology Images
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Text only version of this site
Application of Computational Symmetry to Histology Images
B. Canada and Y. Liu
tech. report CMU-RI-TR-08-08, Robotics Institute, Carnegie Mellon University, January, 2008.
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| Abstract |
The larval zebrafish is an ideal organism for studying mutant phenotypes (observable traits) because of its small size and rapid, ex vivo development. Histology is one highly sensitive means for detecting and scoring zebrafish mutants, and while “high-throughput” methods have been developed for preparing digital “virtual slides” of zebrafish larval histology, problems of subjectivity and labor bottlenecks associated with scoring these virtual slides impede large-scale histological analysis from being widely adopted in zebrafish laboratories. Here, we demonstrate that novel computer vision techniques derived from and inspired by the current state-of-the-art algorithms for computational symmetry detection have the potential to improve the efficiency and accuracy of the histology image preparation and classification workflow, thereby bringing the overall process closer to being more fully automated and truly “high-throughput.”
| Notes |
Associated center: VASC
Associated lab/group: Computational Symmetry
Number of pages: 47
| Text Reference |
B. Canada and Y. Liu, Application of Computational Symmetry to Histology Images, tech. report CMU-RI-TR-08-08, Robotics Institute, Carnegie Mellon University, January, 2008.
| BibTeX Reference |
@techreport{Canada_2008_5982,
author = "Brian Canada and Yanxi Liu",
title = "Application of Computational Symmetry to Histology Images",
institution = "Robotics Institute, Carnegie Mellon University",
month = "January",
year = "2008",
number = "CMU-RI-TR-08-08",
address = "Pittsburgh, PA"
}