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 ?igh-throughput?methods have been developed for preparing digital ?irtual 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 ?igh-throughput.

Text Reference

Brian Canada and Yanxi Liu, "Application of Computational Symmetry to Histology Images," tech. report CMU-RI-TR-08-08 , Carnegie Mellon University, January, 2008

BibTeX Reference

@techreport{Canada_Tech_2008,
    author = "Brian Canada and Yanxi Liu",
    title = "Application of Computational Symmetry to Histology Images",
    institution = "Robotics Institute",
    month = "January",
    year = "2008",
    number= "CMU-RI-TR-08-08",
    address= "Pittsburgh, PA",
}