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 Abstract:

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.


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Citation: 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.

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Last Modified: June 21, 2008
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