Poultry Skin Tumor Detection in Hyperspectral Images Using Radial Basis Probabilistic Neural Network

  • Intaek Kim
  • Chengzhe Xu
  • Moon S. Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


This paper presents a method for detecting poultry skin tumors using hyperspectral fluorescence image. New feature space is generated by the ratio of intensities of two bands, the combination of images such that their intensity ratios yield the least false detection rate is selected by minimizing overlap area of normal and tumor’s PDFs. Four feature images are chosen and presented as an input to a classifier based on the radial basis probability neural network. The classifier categorizes the input with three classes, where one is for tumor and two for normal skin pixels. The classification result based on this method shows the improved performance in that the number of false classification is reduced.


Hide Layer Normal Skin Hide Neuron Hyperspectral Image Radial Basis Function Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Calnek, B.W., John Barnes, H., Beard, C.W., Reid, W.M., Yoder, H.W.: Diseases of Poultry. Iowa State University Press, Ames, Iowa (1991)Google Scholar
  2. 2.
    Chao, K., Chen, Y.R., Hruschka, W.R., Gwozdz, F.B.: On-line Inspection of Poultry Carcasses by a Dual-camera System. J. Food. Eng. 51(3), 185–192 (2002)CrossRefGoogle Scholar
  3. 3.
    Chen, Y.R., Park, B., Huffman, R.W., Nguyen, M.: Classification of On-line Poultry Carcasses with Backpropagation Neural Networks. J. Food. Eng. 21(1), 33–48 (1998)CrossRefGoogle Scholar
  4. 4.
    Kong, S.G., Chen, Y.R., Kim, I., Kim, M.S.: Analysis of Hyperspectral Fluorescence Images for Poultry Skin Tumor Inspection. Applied Optics 43(4), 824–833 (2004)CrossRefGoogle Scholar
  5. 5.
    Kim, I., Kim, M.S., Chen, Y.R., Kong, S.G.: Detection of Skin Tumors on Chicken Carcasses using Hyperspectral Fluorescence Imaging. Transactions of ASAE 47(5), 1785–1792 (2004)Google Scholar
  6. 6.
    Huang, D.S.: Radial Basis Probabilistic Neural Networks: Model and Application. International Journal of Pattern Recognition and Artificial Intelligence 13(7), 1083–1101 (1999)CrossRefGoogle Scholar
  7. 7.
    Haykin, S.: Adaptive Filter Theory, 3rd edn. Prentice-Hall, Upper Saddle River (1996)Google Scholar
  8. 8.
    Fletcher, J.T., Kong, S.G.: Principal Component analysis for Poultry Tumor Inspection using Hyperspectral Fluorescence Imaging. In: Proceedings of the International Joint Conference on Neural Networks 2003, vol. 1, pp. 149–153. Doubletree Hotel-Jantzen Beach, Portland, Oregon (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Intaek Kim
    • 1
  • Chengzhe Xu
    • 1
  • Moon S. Kim
    • 2
  1. 1.Department of Communication EngineeringMyongji UniversityKyonggidoSouth Korea
  2. 2.USDA ARS, BA, ANRI, ISLBeltsvilleU.S.A.

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