Abstract

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

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References

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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]

2002 (2)

D. A. Landgrebe, “Hyperspectral image data analysis as a high dimensional signal processing problem,” IEEE Signal Process. Mag. 19, 17–28 (2002).
[CrossRef]

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]

2001 (4)

D. W. Lamb, R. B. Brown, “Remote-sensing and mapping of weeds in crops,” J. Agric. Eng. Res. 78, 117–125 (2001).
[CrossRef]

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).

S. M. Schweizer, J. M. F. Moura, “Efficient detection in hyperspectral imagery,” IEEE Trans. Image Process. 10, 584–597 (2001).
[CrossRef]

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]

2000 (2)

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]

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]

1998 (4)

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]

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]

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]

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]

1996 (1)

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).

1992 (1)

S. G. Kong, B. Kosko, “Adaptive fuzzy system for backing up a truck-and-trailer,” IEEE Trans. Neural Netw. 3, 211–223 (1992).
[CrossRef]

1991 (1)

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]

1989 (1)

S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989).
[CrossRef]

1988 (1)

A. Rosenfeld, “Computer vision: basic principles,” Proc. IEEE 76, 863–868 (1988).
[CrossRef]

Barnes, H. J.

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.

Beard, C. W.

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.

Bishop, C. M.

C. M. Bishop, Neural Networks for Pattern Recognition (Oxford U. Press, New York, N.Y., 1995).

Brancaleon, L.

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]

Brown, R. B.

D. W. Lamb, R. B. Brown, “Remote-sensing and mapping of weeds in crops,” J. Agric. Eng. Res. 78, 117–125 (2001).
[CrossRef]

Burrus, S.

S. Burrus, R. Gopinath, H. Guo, Introduction to Wavelets and Wavelet Transforms: A Primer, 1st ed. (Prentice-Hall, Englewood Cliffs, N.J., 1998).

Calnek, B. W.

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.

Chao, K.

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]

Chappelle, E. W.

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]

Chen, Y. R.

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]

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).

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]

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]

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).

Daubechies, I.

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]

Durkin, A. J.

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]

Fallon, J. D.

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]

Gopinath, R.

S. Burrus, R. Gopinath, H. Guo, Introduction to Wavelets and Wavelet Transforms: A Primer, 1st ed. (Prentice-Hall, Englewood Cliffs, N.J., 1998).

Guo, H.

S. Burrus, R. Gopinath, H. Guo, Introduction to Wavelets and Wavelet Transforms: A Primer, 1st ed. (Prentice-Hall, Englewood Cliffs, N.J., 1998).

Gwozdz, F. B.

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]

Holden, H.

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]

Hruschka, W. R.

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]

Huffman, R. W.

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]

Hwang, H.

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).

Jayas, D. S.

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]

Kim, M. S.

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).

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]

Kollias, N.

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]

Kong, S. G.

S. G. Kong, B. Kosko, “Adaptive fuzzy system for backing up a truck-and-trailer,” IEEE Trans. Neural Netw. 3, 211–223 (1992).
[CrossRef]

Kosko, B.

S. G. Kong, B. Kosko, “Adaptive fuzzy system for backing up a truck-and-trailer,” IEEE Trans. Neural Netw. 3, 211–223 (1992).
[CrossRef]

Lamb, D. W.

D. W. Lamb, R. B. Brown, “Remote-sensing and mapping of weeds in crops,” J. Agric. Eng. Res. 78, 117–125 (2001).
[CrossRef]

Landgrebe, D. A.

D. A. Landgrebe, “Hyperspectral image data analysis as a high dimensional signal processing problem,” IEEE Signal Process. Mag. 19, 17–28 (2002).
[CrossRef]

LeDrew, E.

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]

Luigi-Dragotti, P.

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]

Mallat, S. G.

S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989).
[CrossRef]

McMurtrey, J. E.

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]

Mehl, P. M.

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).

Menaker, G.

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]

Moura, J. M. F.

S. M. Schweizer, J. M. F. Moura, “Efficient detection in hyperspectral imagery,” IEEE Trans. Image Process. 10, 584–597 (2001).
[CrossRef]

Nguyen, M.

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]

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]

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).

Paliwal, J.

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]

Park, B.

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]

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]

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).

Poggi, G.

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]

Ragozini, A. R. P.

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]

Reid, W. M.

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.

Rosenfeld, A.

A. Rosenfeld, “Computer vision: basic principles,” Proc. IEEE 76, 863–868 (1988).
[CrossRef]

Schweizer, S. M.

S. M. Schweizer, J. M. F. Moura, “Efficient detection in hyperspectral imagery,” IEEE Trans. Image Process. 10, 584–597 (2001).
[CrossRef]

Tao, Y.

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]

Tu, J. H.

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]

Visen, N. S.

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]

Wen, Z.

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]

Yoder, H. W.

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.

IEEE Signal Process. Mag. (1)

D. A. Landgrebe, “Hyperspectral image data analysis as a high dimensional signal processing problem,” IEEE Signal Process. Mag. 19, 17–28 (2002).
[CrossRef]

IEEE Trans. Geosci. Remote Sens. (1)

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]

IEEE Trans. Image Process. (1)

S. M. Schweizer, J. M. F. Moura, “Efficient detection in hyperspectral imagery,” IEEE Trans. Image Process. 10, 584–597 (2001).
[CrossRef]

IEEE Trans. Neural Netw. (1)

S. G. Kong, B. Kosko, “Adaptive fuzzy system for backing up a truck-and-trailer,” IEEE Trans. Neural Netw. 3, 211–223 (1992).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989).
[CrossRef]

J. Agric. Eng. Res. (3)

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]

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]

D. W. Lamb, R. B. Brown, “Remote-sensing and mapping of weeds in crops,” J. Agric. Eng. Res. 78, 117–125 (2001).
[CrossRef]

J. Food Eng. (1)

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]

J. Food Process. Eng. (1)

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]

Opt. Eng. (1)

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]

Photochem. Photobiol. (1)

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]

Proc. IEEE (1)

A. Rosenfeld, “Computer vision: basic principles,” Proc. IEEE 76, 863–868 (1988).
[CrossRef]

Remote Sens. Environ. (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]

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]

Trans. ASAE (2)

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).

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).

Other (4)

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.

S. Burrus, R. Gopinath, H. Guo, Introduction to Wavelets and Wavelet Transforms: A Primer, 1st ed. (Prentice-Hall, Englewood Cliffs, N.J., 1998).

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]

C. M. Bishop, Neural Networks for Pattern Recognition (Oxford U. Press, New York, N.Y., 1995).

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Figures (11)

Fig. 1
Fig. 1

Procedure of hyperspectral image analysis for poultry skin tumor inspection.

Fig. 2
Fig. 2

Hardware components of the ISL hyperspectral imaging system.11 VIS, visible; NIR, near-infrared.

Fig. 3
Fig. 3

Hyperspectral fluorescence images of a poultry carcass sample (bands 5, 10, through 60, for a total of 12 bands).

Fig. 4
Fig. 4

Two-dimensional discrete wavelet decomposition. (a) Recursive filter tree implementation of the DWT. Filter banks for DWT. (b) Level-1 wavelet decomposition of a single-band image. Detail components are shown in reverse gray levels. LPF, low-pass filter; HPF, high-pass filter.

Fig. 5
Fig. 5

PCA of spectral signatures. (a) The first three eigenvectors and (b) energy content of the principal components.

Fig. 6
Fig. 6

Representation of spectral signals with a small number of principal components. (a) Normal tissue and (b) tumor.

Fig. 7
Fig. 7

Relative fluorescence intensity of normal tissue and tumor as a function of spectral bands.

Fig. 8
Fig. 8

Scatter plot of the spectral features in the feature space.

Fig. 9
Fig. 9

Membership functions for the fuzzy variables x 1 and x 2. (a) Fuzzy variable x 1 (centers: 0, 0.2, 1; widths: 0.05, 0.12, 0.4) and (b) fuzzy variable x 2 (centers: 0, 1; widths: 0.12, 0.3).

Fig. 10
Fig. 10

Detection of tumors with the fuzzy classifier for the training sample of band 20 (λ20). (a) Original image, (b) fuzzy inference system output, and (c) fuzzy inference system output with morphological filtering.

Fig. 11
Fig. 11

Detection of tumors with the fuzzy inference system for testing sample of band 20 (λ20). (a) Original image, (b) fuzzy system output, and (c) fuzzy system output with median filtering.

Tables (2)

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Table 1 Wavelength Values of Each Spectral Band

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Table 2 Classification Performance

Equations (15)

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Wfj, k=n=0N-1 fnϕj,kn,
ϕj,kn=12j ϕn-2jk2j
Aj+1i=l=0L-1 HlAj2i+l,
Dj+1i=l=0L-1 GlAj2i+l.
y=i=1L aiei,
x1=15i=2024 Iu, v, λi,
x2=i=2024 Iu, v, λi-i=4448 Iu, v, λii=2024 Iu, v, λi.
mAx=exp-x-c22w2.
mTUMORy=minmMEDx1, mSMLx2,
mNORMALy=minmHGHx1, mBIGx2,
mBGy=mLOWx1.
m*y=maxmNORMALy, mTUMORy, mBGy.
DIu, v=1if m*y=mTUMORy0otherwise.
y*=y1mNORMALy+y2mTUMORy+y3mBGymNORMALy+mTUMORy+mBGy.
DIu, v=1if y*Jy20otherwise,

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