Towards Efficient Automated Characterization of Irregular
Histology Images via Transformation to Frieze-Like Patterns
Brian A. Canada, Georgia Thomas, Keith C. Cheng, James Z. Wang and Yanxi Liu
The Pennsylvania State University
Histology is used in both clinical and research contexts as a highly
sensitive method for detecting morphological abnormalities in
organ tissues. Although modern scanning equipment has enabled
high-throughput digitization of high-resolution histology slides,
the manual scoring and annotation of these images is a tedious,
subjective, and sometimes error-prone process. A number of
methods have been proposed for the automated characterization of
histology images, most of which rely on the extraction of texture
features used for classifier training. The irregular, nonlinear
shapes of certain types of tissues can obscure the implicit
symmetries observed within them, making it difficult or
cumbersome for automated methods to extract texture features
quickly and reliably. Using larval zebrafish eye and gut tissues as
a pilot model, we present a prototype method for transforming the
appearance of these irregularly-shaped tissues into onedimensional,
frieze-like patterns. We show that the reduced
dimensionality of the patterns may allow them to be characterized
with greater efficiency and accuracy than by previous methods of
image analysis, which in turn enables potentially greater accuracy
in the retrieval of histology images exhibiting abnormalities of
interest to pathologists and researchers.
Full Paper in Color
Brian A. Canada, Georgia Thomas, Keith C. Cheng, James Z. Wang and
Yanxi Liu, ``Towards Efficient Automated Characterization of Irregular
Histology Images via Transformation to Frieze-Like Patterns,''
Proceedings of the ACM International Conference on Image and Video
pp. 581-590, Niagara Falls, Canada, July 2008.
Copyright 2008 ACM.
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May 6, 2008