Abstract
Nematodes are primitive creatures that are endangering and devouring many of the essential resources beneficial to human beings. For effective inspection and quarantine, we have devised an image based system for quantitatively characterizing and identifying nematodes, and achieved average successful identification rate of 71.2% for the Uncoordinated (Unc) mutant types and 91.2% for other types. To enhance system performance, here we introduce the worm-body Trunk Coordinate System for defining and characterizing the locomotion patterns of representative mutants. At least 60,000 frames for each species, totally 480,000 frames, representing wild type and 7 other mutant types, were analyzed. The average correct classification rate, measured by Classification and Regression Tree (CART) algorithm, was 79.3% for Unc types. The scheme devised and the features extracted are good supplements for the previous automated nematode identification system.
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References
Baek, J., Cosman, P., Feng, Z., Silver, J., Schafer, W.R.: Using machine vision to analyze and classify C. elegans behavioral phenotypes quantitatively. J. Neurosci. Methods 118, 9–21 (2002)
Breiman, L., Fried, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Belmont, CA, Wadsworth (1984)
Geng, W., Cosman, P., Berry, C.C., Feng, Z., Schafer, W.R.: Automatic Tracking, Feature Extraction and Classification of C. elegans Phenotypes. IEEE Trans. Biomedical Engineering 51, 1181–1820 (2004)
Gonzalez, R.C., Woods, R.E.: Digital image processing, 2nd edn. Prentice Hall, Englewood Cliffs (2002)
Jain, R., Rangachar, R., Schunck, B.: Machine Vision. McGraw-Hill, New York (1995)
Leonard, P., Gianessi, C.S.: Silvers, Sujatha Sankula: Current and Potential Impact For Improving Pest Managemen. In U.S. Agriculture An Analysis of 40 Case Study. National Center for Food & Agricultural Policy (2002)
Silva, C.A., Magalhaes, K.M.C., Doria Neto, A.D.: An intelligent system for detection of nematodes in digital images. In: Proceedings of the International Joint Conference on Neural Networks, vol. 1, pp. 20–24 (2003)
Zhang, T.Y., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Comm. ACM 27(3), 236–239 (1984)
Zhou, B., Nah, W., Lee, K., Baek, J.: A General Image Based Nematode Identification System Design. In: Hao, Y., Liu, J., Wang, Y.-P., Cheung, Y.-m., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005. LNCS (LNAI), vol. 3802, pp. 899–904. Springer, Heidelberg (2005)
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Zhou, BT., Baek, JH. (2006). An Automatic Nematode Identification Method Based on Locomotion Patterns. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_41
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DOI: https://doi.org/10.1007/11816102_41
Publisher Name: Springer, Berlin, Heidelberg
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