Automated segmentation and classification of zebrafish histology images
for high-throughput phenotyping
Brian A. Canada, Georgia Thomas,
Keith C. Cheng, and James Z. Wang
The Pennsylvania State University, University Park, PA 16802
Because of its small size and rapid development the larval zevrafish
is an ideal model organism for studying mutant phenotypes using
high-thoughput histological analysis. Although the preparation and
subsequent digitization of zebrafish larval histology specimens can be
conducted in parallel, the scoring and annotation of the resulting
virtual slides is largely manual and therefore rate limiting, which
motivates the development of systems for automated characterization
of histology images. We present a prototype for automated segmentation
and classification of histology images in animal models, with a pilot
study focusing on larval zebrafish eye and gut images. We show that
the segmentation of the images into regions of individual cell layers
can be conducted with good precision using combinations of widely-used
image processing operations, and that the resulting classification
system, based on a decision tree algorithm, exhibits promising
Full Paper in Color
Brian A. Canada, Georgia Thomas, Keith C. Cheng, and James Z. Wang,
`` Automated Segmentation and Classification of Zebrafish Histology Images for High-throughput Phenotyping,''
Proceedings of the IEEE/NIH Life Science Systems and Applications
Workshop, pp. 245-248, Bethesda, MD, November 8-9, 2007.
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September 11, 2006