Automatic Lattice Detection in
Near-Regular Histology Array Images
Brian A. Canada, Georgia Thomas, Keith C. Cheng, James Z. Wang and Yanxi Liu
The Pennsylvania State University
Near-regular texture (NRT), denoting deviations from otherwise
symmetric wallpaper patterns, is commonly observable in the real
world. Existing lattice detection algorithms capture the underlying
lattice of an NRT pattern and all of its individual texels, facilitating
an automated analysis of NRT. Many real world images, as in
those of zebrafish larval histology arrays, depart significantly from
regularity and challenge the current state of the art wallpaper group
theory-based lattice detection methods. We propose an alternative
2D lattice detection algorithm that exploits translation and reflection
symmetries and specific imaging cues. By outperforming
existing methods on histology array images, our algorithm leads us
towards complete automation of high-throughput histological image
processing while broadening the spectrum of NRT computation.
Full Paper in Color
Brian A. Canada, Georgia Thomas, Keith C. Cheng, James Z. Wang and
Yanxi Liu, ``Automatic Lattice Detection in
Near-Regular Histology Array Images,''
Proceedings of the IEEE International Conference on Image Processing,
pp. 1452-1455, San Diego, CA, October 2008.
Copyright 2008 IEEE.
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June 21, 2008