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Applied Optics

Applied Optics


  • Vol. 43, Iss. 4 — Feb. 1, 2004
  • pp: 824–833

Analysis of hyperspectral fluorescence images for poultry skin tumor inspection

Seong G. Kong, Yud-Ren Chen, Intaek Kim, and Moon S. Kim  »View Author Affiliations

Applied Optics, Vol. 43, Issue 4, pp. 824-833 (2004)

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We present a hyperspectral fluorescence imaging system with a fuzzy inference scheme for detecting skin tumors on poultry carcasses. Hyperspectral images reveal spatial and spectral information useful for finding pathological lesions or contaminants on agricultural products. Skin tumors are not obvious because the visual signature appears as a shape distortion rather than a discoloration. Fluorescence imaging allows the visualization of poultry skin tumors more easily than reflectance. The hyperspectral image samples obtained for this poultry tumor inspection contain 65 spectral bands of fluorescence in the visible region of the spectrum at wavelengths ranging from 425 to 711 nm. The large amount of hyperspectral image data is compressed by use of a discrete wavelet transform in the spatial domain. Principal-component analysis provides an effective compressed representation of the spectral signal of each pixel in the spectral domain. A small number of significant features are extracted from two major spectral peaks of relative fluorescence intensity that have been identified as meaningful spectral bands for detecting tumors. A fuzzy inference scheme that uses a small number of fuzzy rules and Gaussian membership functions successfully detects skin tumors on poultry carcasses. Spatial-filtering techniques are used to significantly reduce false positives.

© 2004 Optical Society of America

OCIS Codes
(170.6510) Medical optics and biotechnology : Spectroscopy, tissue diagnostics
(330.6180) Vision, color, and visual optics : Spectral discrimination

Original Manuscript: April 25, 2003
Revised Manuscript: September 16, 2003
Published: February 1, 2004

Seong G. Kong, Yud-Ren Chen, Intaek Kim, and Moon S. Kim, "Analysis of hyperspectral fluorescence images for poultry skin tumor inspection," Appl. Opt. 43, 824-833 (2004)

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  1. D. A. Landgrebe, “Hyperspectral image data analysis as a high dimensional signal processing problem,” IEEE Signal Process. Mag. 19, 17–28 (2002). [CrossRef]
  2. H. Holden, E. LeDrew, “Spectral discrimination of healthy and non-healthy corals based on cluster analysis, principal components analysis, and derivative spectroscopy,” Remote Sens. Environ. 65, 217–224 (1998). [CrossRef]
  3. D. W. Lamb, R. B. Brown, “Remote-sensing and mapping of weeds in crops,” J. Agric. Eng. Res. 78, 117–125 (2001). [CrossRef]
  4. A. Rosenfeld, “Computer vision: basic principles,” Proc. IEEE 76, 863–868 (1988). [CrossRef]
  5. B. W. Calnek, H. J. Barnes, C. W. Beard, W. M. Reid, H. W. Yoder, Diseases of Poultry (Iowa State University, Ames, Iowa, 1991), Chap. 16, pp. 386–484.
  6. K. Chao, Y. R. Chen, W. R. Hruschka, F. B. Gwozdz, “On-line inspection of poultry carcasses by a dual-camera system,” J. Food Eng. 51, 185–192 (2002). [CrossRef]
  7. Y. R. Chen, B. Park, R. W. Huffman, M. Nguyen, “Classification of on-line poultry carcasses with backpropagation neural networks,” J. Food Process. Eng. 21, 33–48 (1998). [CrossRef]
  8. B. Park, Y. R. Chen, M. Nguyen, H. Hwang, “Characterizing multispectral images of tumorous, bruised, skin-torn, and wholesome poultry carcasses,” Trans. ASAE 39, 1933–1941 (1996).
  9. Z. Wen, Y. Tao, “Fuzzy-based determination of model and parameters of dual-wavelength vision system for on-line apple sorting,” Opt. Eng. 37, 293–299 (1998). [CrossRef]
  10. E. W. Chappelle, J. E. McMurtrey, M. S. Kim, “Identification of the pigment responsible for the blue fluorescence band in laser induced fluorescence spectra of green plants, and potential use of this band in remotely estimating rates of photosynthesis,” Remote Sens. Environ. 36, 213–218 (1991). [CrossRef]
  11. M. S. Kim, Y. R. Chen, P. M. Mehl, “Hyperspectral reflectance and fluorescence imaging system for food quality and safety,” Trans. ASAE 44, 721–729 (2001).
  12. S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989). [CrossRef]
  13. S. Burrus, R. Gopinath, H. Guo, Introduction to Wavelets and Wavelet Transforms: A Primer, 1st ed. (Prentice-Hall, Englewood Cliffs, N.J., 1998).
  14. P. Luigi-Dragotti, G. Poggi, A. R. P. Ragozini, “Compression of multispectral images by three-dimensional SPIHT algorithm,” IEEE Trans. Geosci. Remote Sens. 38, 416–428 (2000). [CrossRef]
  15. I. Daubechies, Ten Lectures on Wavelets, Vol. 61 of Conference Board of the Mathematical Sciences-National Science Foundation Regional Conference Series in Applied Mathematics, (Society for Industrial and Applied Mathematics, Philadelphia, Pa., 1992). [CrossRef]
  16. C. M. Bishop, Neural Networks for Pattern Recognition (Oxford U. Press, New York, N.Y., 1995).
  17. S. M. Schweizer, J. M. F. Moura, “Efficient detection in hyperspectral imagery,” IEEE Trans. Image Process. 10, 584–597 (2001). [CrossRef]
  18. D. S. Jayas, J. Paliwal, N. S. Visen, “Multi-layer neural network for image analysis of agricultural products,” J. Agric. Eng. Res. 77, 119–128 (2000). [CrossRef]
  19. B. Park, Y. R. Chen, M. Nguyen, “Multi-spectral image analysis using neural network algorithm for inspection of poultry carcasses,” J. Agric. Eng. Res. 69, 351–363 (1998). [CrossRef]
  20. L. Brancaleon, A. J. Durkin, J. H. Tu, G. Menaker, J. D. Fallon, N. Kollias, “In vivo fluorescence spectroscopy of nonmelanoma skin cancer,” Photochem. Photobiol. 73, 178–183 (2001). [CrossRef] [PubMed]
  21. S. G. Kong, B. Kosko, “Adaptive fuzzy system for backing up a truck-and-trailer,” IEEE Trans. Neural Netw. 3, 211–223 (1992). [CrossRef]

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