ABSTRACT
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 one-dimensional, "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.
- Grabe, N., Pommerencke, T., Steinberg, T., Dickhaus, H., Tomakidi, P. (2007). "Reconstructing protein networks of epithelial differentiation from histological sections." Bioinformatics. 23(23): 3200--3208. Google ScholarDigital Library
- Colquhoun, P., Nogueras, J.J., Dipasquale, B., Petras, R., Wexner, S.D., Woodhouse, S. (2003). "Interobserver and intraobserver bias exists in the interpretation of anal dysplasia." Dis Colon Rectum. 46(10): 1332--1338.Google ScholarCross Ref
- Plummer, M., Buiatti, E., Lopez, G., Peraza, S., Vivas, J., Oliver, W., Munoz, N. (1997). "Histological diagnosis of precancerous lesions of the stomach: a reliability study." Int J Epidemiol. 26(4): 716--720.Google ScholarCross Ref
- Bradbury, J. (2004). "Small fish, big science." PLoS Biology. 2(5): E148.Google ScholarCross Ref
- Nusslein-Volhard, C., Dahm, R. (eds.) Zebrafish: A Practical Approach. Oxford: Oxford University Press; 2002.Google Scholar
- Tsao-Wu, G.S., Weber, C.H., Budgeon, L.R., Cheng, K.C. (1998). "Agarose embedded tissue arrays for histologic and genetic analysis." Biotechniques. 25(4): 614--618.Google ScholarCross Ref
- Sabaliauskas, N.A., Foutz, C.A., Mest, J.R., Budgeon, L.R., Sidor, A.T., Gershenson, J.A., Joshi, S.B., Cheng, K.C. (2006). "High-throughput zebrafish histology." Methods. 39(3): 246--254.Google ScholarCross Ref
- Tsao-Wu, G.S., Weber, C.H., Budgeon, L.R., Cheng, K.C. (1999). "Agarose embedded tissue arrays for histologic and genetic analysis." in Expression Genetics: High-Throughput Methods, Chapter 4, M. McClelland and A. Pardee (eds.). Natick, MA: Eaton Publishing; 1999.Google Scholar
- Moore, J.L., Aros, M., Steudel, K.G., Cheng, K.C. (2002). "Fixation and decalcification of adult zebrafish for histological, immunocytochemical, and genotypic analysis." Biotechniques. 32: 296--298.Google ScholarCross Ref
- Canada, B.A., Thomas, G.K., Cheng, K.C., Wang, J.Z. (2007). "Automated segmentation and classification of zebrafish histology images for high-throughput phenotyping." Proc. of the 3rd IEEE-NIH Life Science Systems and Applications Workshop, pp. 245--248.Google Scholar
- Mohideen, M.A., Beckwith, L.G., Tsao-Wu, G.S., Moore, J.L., Wong, A.C.C., Chinoy, M.R., Cheng, K.C. (2003). "Histology-based screen for zebrafish mutants with abnormal cell differentiation." Dev. Dynamics. 228: 414--423.Google ScholarCross Ref
- Canada, B.A., Liu, Y. (2008). "Application of Computational Symmetry to Histology Images." Penn State Dept. of Computer Science and Engineering Technical Report 08-001.Google Scholar
- Lee, S., Collins, R.T., Liu, Y. (2008). "Rotation symmetry group detection via frequency analysis of frieze-expansions." Proc. of the 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008).Google Scholar
- Grünbaum, B., Shephard, G.C. Tilings and Patterns. New York: W. H. Freeman and Company; 1987.Google Scholar
- Loy, G., Eklundh, J. (2006). "Detecting symmetry and symmetric constellations of features." Proc. of the 9th European Conference on Computer Vision, 2:508--521. Google ScholarDigital Library
- Prasad, V.S.N., Davis, L.S. (2005). "Detecting rotational symmetries." Proc. of the Tenth IEEE International Conference on Computer Vision, 2:954--961. Google ScholarDigital Library
- Haralick, M., Shanmugam, K., Dinstein, I. (1973). "Texture features for image classification." IEEE Transactions on Systems, Man, and Cybernetics. 3(6): 610.Google ScholarCross Ref
- Viola, P., Wells, W.M. (1997). "Alignment by maximization of mutual information." Int. J. Comput. Vis. 24(2), 137--154. Google ScholarDigital Library
- Turner, M.R. (1986). "Texture discrimination by Gabor functions." Biol Cybern. 55(2-3):71--82. Google ScholarDigital Library
- Doyle, S, Hwang, M, Shah, K, Madabhushi, A, Tomasezweski, J, Feldman, M. (2007). "Automated grading of prostate cancer using architectural and textural image features." Proc. of the International Symposium on Biomedical Imaging (ISBI), pp. 1284--87.Google Scholar
- Field, D.J. (1987) "Relations between the statistics of natural images and the response properties of cortical cells." J. Opt. Soc. Am. A. 4(12):2379.Google ScholarCross Ref
- Kovesi., P. (1995). "Image features from phase congruency." Technical Report 95/4, University of Western Australia, Robotics and Vision Group.Google Scholar
- Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A. Classification and Regression Trees. Belmont: Wadsworth International Group; 1984.Google Scholar
Index Terms
- Towards efficient automated characterization of irregular histology images via transformation to frieze-like patterns
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