Abstract

Near-infrared hyperspectral imaging is becoming a popular tool in various fields. In all imaging systems, proper illumination is crucial for attaining optimal image quality that is needed for the best performance of image analysis algorithms. In hyperspectral imaging, the acquired spectral signature has to be representative in all parts of the imaged object. Therefore, the whole object must be equally well illuminated–without shadows or specular reflections. As there are no restrictions imposed on the material and geometry of the object, the desired illumination of the object can only be achieved with completely diffuse illumination. In order to minimize shadows and specular reflections, the light illuminating the object must be spatially, angularly and spectrally uniform. The quality of illumination systems for hyperspectral imaging can therefore be assessed using spatial-intensity, spatial-spectral, angular-intensity and angular-spectral non-uniformity measures that are presented in this paper. Emphasis is given to the angular-intensity and angular-spectral non-uniformity measures, which are the most important contributions of this paper. The measures were defined on images of two reference targets—a flat, white diffuse reflectance target and a sphere grid target—acquired with an acousto-optic tunable filter (AOTF) based hyperspectral imaging system. The proposed measures were tested on a ring light and on a diffuse dome illumination system.

© 2013 OSA

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  1. H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE T. Bio.-Med. Eng.57, 2011–2017 (2010).
    [CrossRef]
  2. L. L. Randeberg, E. L. P. Larsen, and L. O. Svaasand, “Characterization of vascular structures and skin bruises using hyperspectral imaging, image analysis and diffusion theory,” J. Biophotonics3, 53–65 (2010).
    [CrossRef]
  3. G. N. Stamatas and N. Kollias, “In vivo documentation of cutaneous inflammation using spectral imaging,” J. Biomed. Opt.12, 051603 (2007).
    [CrossRef] [PubMed]
  4. H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci.102, 852–857 (2011).
    [CrossRef] [PubMed]
  5. S. V. Panasyuk, S. Yang, D. V. Faller, D. Ngo, R. A. Lew, J. E. Freeman, and A. E. Rogers, “Medical hyperspectral imaging to facilitate residual tumor identification during surgery,” Cancer Biol. Ther.6, 439–446 (2007).
    [CrossRef] [PubMed]
  6. C. Zakian, I. Pretty, and R. Ellwood, “Near-infared hyperspectral imaging of teeth for dental caries detection,” J. Biomed. Opt.14, 064047 (2009).
    [CrossRef]
  7. C. Lee, D. Lee, C. L. Darling, and D. Fried, “Nondestructive assessment of the severity of occlusal caries lesions with near-infrared imaging at 1310 nm,” J. Biomed. Opt.15, 047011 (2010).
    [CrossRef] [PubMed]
  8. G. Reich, “Near-infrared spectroscopy and imaging: basic principles and pharmaceutical applications,” Adv. Drug Deliver. Rev.57, 1109–1143 (2005).
    [CrossRef]
  9. C. Gendrin, Y. Roggo, and C. Collet, “Pharmaceutical applications of vibrational chemical imaging and chemometrics: A review,” J. Pharmaceut. Biomed.48, 533–553 (2008).
    [CrossRef]
  10. D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment,” Food Bioprocess Tech.5, 1121–1142 (2011).
    [CrossRef]
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    [CrossRef]
  12. G. K. Naganathan, L. M. Grimes, J. Subbiah, C. R. Calkins, A. Samal, and G. E. Meyer, “Visible/near-infrared hyperspectral imaging for beef tenderness prediction,” Comput. Electron. Agr.64, 225–233 (2008).
    [CrossRef]
  13. P. Geladi, J. Burger, and T. Lestander, “Hyperspectral imaging: calibration problems and solutions,” Chemometr. Intell. Lab.72, 209–217 (2004).
    [CrossRef]
  14. G. Polder, G. V. D. Heijden, and I. Young, “Spectral image analysis for measuring ripeness of tomatoes,” T. ASAE45, 1155–1161 (2002).
  15. Y. Chen, K. Chao, and M. Kim, “Machine vision technology for agricultural applications,” Comput. Electron. Agr.36, 173–191 (2002).
    [CrossRef]
  16. B. Bennedsen, D. Peterson, and A. Tabb, “Identifying defects in images of rotating apples,” Comput. Electron. Agr.48, 92–102 (2005).
    [CrossRef]
  17. J. Gómez-Sanchis, E. Moltó, G. Camps-Valls, L. Gómez-Chova, N. Aleixos, and J. Blasco, “Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits,” J. Food Eng.85, 191–200 (2008).
    [CrossRef]
  18. J. Katrašnik, F. Pernuš, and B. Likar, “Illumination system characterization for hyperspectral imaging,” Proc. SPIE7891, 78910T (2011).
    [CrossRef]
  19. J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging systems,” Chemometr. Intell. Lab.101, 23–29 (2010).
    [CrossRef]
  20. J. Katrašnik, F. Pernuš, and B. Likar, “Radiometric calibration and noise estimation of hyperspectral imaging systems,” Submitted for publication (2012).
  21. M. Pharr and G. Humphreys, Physically Based Rendering: From Theory To Implementation, The Morgan Kaufmann Series in Interactive 3D Technology (Elsevier/Morgan Kaufmann, 2004).
  22. K. M. Górski, E. Hivon, A. J. Banday, B. D. Wandelt, F. K. Hansen, M. Reinecke, and M. Bartelmann, “HEALPix: A framework for high-resolution discretization and fast analysis of data distributed on the sphere,” Astrophys. J.622, 759–771 (2005).
    [CrossRef]

2011

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci.102, 852–857 (2011).
[CrossRef] [PubMed]

D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment,” Food Bioprocess Tech.5, 1121–1142 (2011).
[CrossRef]

J. Katrašnik, F. Pernuš, and B. Likar, “Illumination system characterization for hyperspectral imaging,” Proc. SPIE7891, 78910T (2011).
[CrossRef]

2010

J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging systems,” Chemometr. Intell. Lab.101, 23–29 (2010).
[CrossRef]

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE T. Bio.-Med. Eng.57, 2011–2017 (2010).
[CrossRef]

L. L. Randeberg, E. L. P. Larsen, and L. O. Svaasand, “Characterization of vascular structures and skin bruises using hyperspectral imaging, image analysis and diffusion theory,” J. Biophotonics3, 53–65 (2010).
[CrossRef]

C. Lee, D. Lee, C. L. Darling, and D. Fried, “Nondestructive assessment of the severity of occlusal caries lesions with near-infrared imaging at 1310 nm,” J. Biomed. Opt.15, 047011 (2010).
[CrossRef] [PubMed]

2009

C. Zakian, I. Pretty, and R. Ellwood, “Near-infared hyperspectral imaging of teeth for dental caries detection,” J. Biomed. Opt.14, 064047 (2009).
[CrossRef]

2008

C. Gendrin, Y. Roggo, and C. Collet, “Pharmaceutical applications of vibrational chemical imaging and chemometrics: A review,” J. Pharmaceut. Biomed.48, 533–553 (2008).
[CrossRef]

J. Gómez-Sanchis, E. Moltó, G. Camps-Valls, L. Gómez-Chova, N. Aleixos, and J. Blasco, “Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits,” J. Food Eng.85, 191–200 (2008).
[CrossRef]

G. K. Naganathan, L. M. Grimes, J. Subbiah, C. R. Calkins, A. Samal, and G. E. Meyer, “Visible/near-infrared hyperspectral imaging for beef tenderness prediction,” Comput. Electron. Agr.64, 225–233 (2008).
[CrossRef]

2007

B. M. Nicolaï, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K. I. Theron, and J. Lammertyn, “Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review,” Postharvest Biol. Tec.46, 99–118 (2007).
[CrossRef]

G. N. Stamatas and N. Kollias, “In vivo documentation of cutaneous inflammation using spectral imaging,” J. Biomed. Opt.12, 051603 (2007).
[CrossRef] [PubMed]

S. V. Panasyuk, S. Yang, D. V. Faller, D. Ngo, R. A. Lew, J. E. Freeman, and A. E. Rogers, “Medical hyperspectral imaging to facilitate residual tumor identification during surgery,” Cancer Biol. Ther.6, 439–446 (2007).
[CrossRef] [PubMed]

2005

G. Reich, “Near-infrared spectroscopy and imaging: basic principles and pharmaceutical applications,” Adv. Drug Deliver. Rev.57, 1109–1143 (2005).
[CrossRef]

B. Bennedsen, D. Peterson, and A. Tabb, “Identifying defects in images of rotating apples,” Comput. Electron. Agr.48, 92–102 (2005).
[CrossRef]

K. M. Górski, E. Hivon, A. J. Banday, B. D. Wandelt, F. K. Hansen, M. Reinecke, and M. Bartelmann, “HEALPix: A framework for high-resolution discretization and fast analysis of data distributed on the sphere,” Astrophys. J.622, 759–771 (2005).
[CrossRef]

2004

P. Geladi, J. Burger, and T. Lestander, “Hyperspectral imaging: calibration problems and solutions,” Chemometr. Intell. Lab.72, 209–217 (2004).
[CrossRef]

2002

G. Polder, G. V. D. Heijden, and I. Young, “Spectral image analysis for measuring ripeness of tomatoes,” T. ASAE45, 1155–1161 (2002).

Y. Chen, K. Chao, and M. Kim, “Machine vision technology for agricultural applications,” Comput. Electron. Agr.36, 173–191 (2002).
[CrossRef]

Akbari, H.

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci.102, 852–857 (2011).
[CrossRef] [PubMed]

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE T. Bio.-Med. Eng.57, 2011–2017 (2010).
[CrossRef]

Aleixos, N.

D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment,” Food Bioprocess Tech.5, 1121–1142 (2011).
[CrossRef]

J. Gómez-Sanchis, E. Moltó, G. Camps-Valls, L. Gómez-Chova, N. Aleixos, and J. Blasco, “Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits,” J. Food Eng.85, 191–200 (2008).
[CrossRef]

Banday, A. J.

K. M. Górski, E. Hivon, A. J. Banday, B. D. Wandelt, F. K. Hansen, M. Reinecke, and M. Bartelmann, “HEALPix: A framework for high-resolution discretization and fast analysis of data distributed on the sphere,” Astrophys. J.622, 759–771 (2005).
[CrossRef]

Bartelmann, M.

K. M. Górski, E. Hivon, A. J. Banday, B. D. Wandelt, F. K. Hansen, M. Reinecke, and M. Bartelmann, “HEALPix: A framework for high-resolution discretization and fast analysis of data distributed on the sphere,” Astrophys. J.622, 759–771 (2005).
[CrossRef]

Bennedsen, B.

B. Bennedsen, D. Peterson, and A. Tabb, “Identifying defects in images of rotating apples,” Comput. Electron. Agr.48, 92–102 (2005).
[CrossRef]

Beullens, K.

B. M. Nicolaï, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K. I. Theron, and J. Lammertyn, “Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review,” Postharvest Biol. Tec.46, 99–118 (2007).
[CrossRef]

Blasco, J.

D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment,” Food Bioprocess Tech.5, 1121–1142 (2011).
[CrossRef]

J. Gómez-Sanchis, E. Moltó, G. Camps-Valls, L. Gómez-Chova, N. Aleixos, and J. Blasco, “Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits,” J. Food Eng.85, 191–200 (2008).
[CrossRef]

Bobelyn, E.

B. M. Nicolaï, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K. I. Theron, and J. Lammertyn, “Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review,” Postharvest Biol. Tec.46, 99–118 (2007).
[CrossRef]

Burger, J.

P. Geladi, J. Burger, and T. Lestander, “Hyperspectral imaging: calibration problems and solutions,” Chemometr. Intell. Lab.72, 209–217 (2004).
[CrossRef]

Bürmen, M.

J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging systems,” Chemometr. Intell. Lab.101, 23–29 (2010).
[CrossRef]

Calkins, C. R.

G. K. Naganathan, L. M. Grimes, J. Subbiah, C. R. Calkins, A. Samal, and G. E. Meyer, “Visible/near-infrared hyperspectral imaging for beef tenderness prediction,” Comput. Electron. Agr.64, 225–233 (2008).
[CrossRef]

Camps-Valls, G.

J. Gómez-Sanchis, E. Moltó, G. Camps-Valls, L. Gómez-Chova, N. Aleixos, and J. Blasco, “Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits,” J. Food Eng.85, 191–200 (2008).
[CrossRef]

Chao, K.

Y. Chen, K. Chao, and M. Kim, “Machine vision technology for agricultural applications,” Comput. Electron. Agr.36, 173–191 (2002).
[CrossRef]

Chen, Y.

Y. Chen, K. Chao, and M. Kim, “Machine vision technology for agricultural applications,” Comput. Electron. Agr.36, 173–191 (2002).
[CrossRef]

Collet, C.

C. Gendrin, Y. Roggo, and C. Collet, “Pharmaceutical applications of vibrational chemical imaging and chemometrics: A review,” J. Pharmaceut. Biomed.48, 533–553 (2008).
[CrossRef]

Cubero, S.

D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment,” Food Bioprocess Tech.5, 1121–1142 (2011).
[CrossRef]

Darling, C. L.

C. Lee, D. Lee, C. L. Darling, and D. Fried, “Nondestructive assessment of the severity of occlusal caries lesions with near-infrared imaging at 1310 nm,” J. Biomed. Opt.15, 047011 (2010).
[CrossRef] [PubMed]

Ellwood, R.

C. Zakian, I. Pretty, and R. Ellwood, “Near-infared hyperspectral imaging of teeth for dental caries detection,” J. Biomed. Opt.14, 064047 (2009).
[CrossRef]

Faller, D. V.

S. V. Panasyuk, S. Yang, D. V. Faller, D. Ngo, R. A. Lew, J. E. Freeman, and A. E. Rogers, “Medical hyperspectral imaging to facilitate residual tumor identification during surgery,” Cancer Biol. Ther.6, 439–446 (2007).
[CrossRef] [PubMed]

Freeman, J. E.

S. V. Panasyuk, S. Yang, D. V. Faller, D. Ngo, R. A. Lew, J. E. Freeman, and A. E. Rogers, “Medical hyperspectral imaging to facilitate residual tumor identification during surgery,” Cancer Biol. Ther.6, 439–446 (2007).
[CrossRef] [PubMed]

Fried, D.

C. Lee, D. Lee, C. L. Darling, and D. Fried, “Nondestructive assessment of the severity of occlusal caries lesions with near-infrared imaging at 1310 nm,” J. Biomed. Opt.15, 047011 (2010).
[CrossRef] [PubMed]

García-Navarrete, O. L.

D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment,” Food Bioprocess Tech.5, 1121–1142 (2011).
[CrossRef]

Geladi, P.

P. Geladi, J. Burger, and T. Lestander, “Hyperspectral imaging: calibration problems and solutions,” Chemometr. Intell. Lab.72, 209–217 (2004).
[CrossRef]

Gendrin, C.

C. Gendrin, Y. Roggo, and C. Collet, “Pharmaceutical applications of vibrational chemical imaging and chemometrics: A review,” J. Pharmaceut. Biomed.48, 533–553 (2008).
[CrossRef]

Gómez-Chova, L.

J. Gómez-Sanchis, E. Moltó, G. Camps-Valls, L. Gómez-Chova, N. Aleixos, and J. Blasco, “Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits,” J. Food Eng.85, 191–200 (2008).
[CrossRef]

Gómez-Sanchis, J.

D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment,” Food Bioprocess Tech.5, 1121–1142 (2011).
[CrossRef]

J. Gómez-Sanchis, E. Moltó, G. Camps-Valls, L. Gómez-Chova, N. Aleixos, and J. Blasco, “Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits,” J. Food Eng.85, 191–200 (2008).
[CrossRef]

Górski, K. M.

K. M. Górski, E. Hivon, A. J. Banday, B. D. Wandelt, F. K. Hansen, M. Reinecke, and M. Bartelmann, “HEALPix: A framework for high-resolution discretization and fast analysis of data distributed on the sphere,” Astrophys. J.622, 759–771 (2005).
[CrossRef]

Grimes, L. M.

G. K. Naganathan, L. M. Grimes, J. Subbiah, C. R. Calkins, A. Samal, and G. E. Meyer, “Visible/near-infrared hyperspectral imaging for beef tenderness prediction,” Comput. Electron. Agr.64, 225–233 (2008).
[CrossRef]

Hansen, F. K.

K. M. Górski, E. Hivon, A. J. Banday, B. D. Wandelt, F. K. Hansen, M. Reinecke, and M. Bartelmann, “HEALPix: A framework for high-resolution discretization and fast analysis of data distributed on the sphere,” Astrophys. J.622, 759–771 (2005).
[CrossRef]

Heijden, G. V. D.

G. Polder, G. V. D. Heijden, and I. Young, “Spectral image analysis for measuring ripeness of tomatoes,” T. ASAE45, 1155–1161 (2002).

Hivon, E.

K. M. Górski, E. Hivon, A. J. Banday, B. D. Wandelt, F. K. Hansen, M. Reinecke, and M. Bartelmann, “HEALPix: A framework for high-resolution discretization and fast analysis of data distributed on the sphere,” Astrophys. J.622, 759–771 (2005).
[CrossRef]

Humphreys, G.

M. Pharr and G. Humphreys, Physically Based Rendering: From Theory To Implementation, The Morgan Kaufmann Series in Interactive 3D Technology (Elsevier/Morgan Kaufmann, 2004).

Katrašnik, J.

J. Katrašnik, F. Pernuš, and B. Likar, “Illumination system characterization for hyperspectral imaging,” Proc. SPIE7891, 78910T (2011).
[CrossRef]

J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging systems,” Chemometr. Intell. Lab.101, 23–29 (2010).
[CrossRef]

J. Katrašnik, F. Pernuš, and B. Likar, “Radiometric calibration and noise estimation of hyperspectral imaging systems,” Submitted for publication (2012).

Kim, M.

Y. Chen, K. Chao, and M. Kim, “Machine vision technology for agricultural applications,” Comput. Electron. Agr.36, 173–191 (2002).
[CrossRef]

Kojima, K.

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci.102, 852–857 (2011).
[CrossRef] [PubMed]

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE T. Bio.-Med. Eng.57, 2011–2017 (2010).
[CrossRef]

Kollias, N.

G. N. Stamatas and N. Kollias, “In vivo documentation of cutaneous inflammation using spectral imaging,” J. Biomed. Opt.12, 051603 (2007).
[CrossRef] [PubMed]

Kosugi, Y.

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci.102, 852–857 (2011).
[CrossRef] [PubMed]

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE T. Bio.-Med. Eng.57, 2011–2017 (2010).
[CrossRef]

Lammertyn, J.

B. M. Nicolaï, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K. I. Theron, and J. Lammertyn, “Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review,” Postharvest Biol. Tec.46, 99–118 (2007).
[CrossRef]

Larsen, E. L. P.

L. L. Randeberg, E. L. P. Larsen, and L. O. Svaasand, “Characterization of vascular structures and skin bruises using hyperspectral imaging, image analysis and diffusion theory,” J. Biophotonics3, 53–65 (2010).
[CrossRef]

Lee, C.

C. Lee, D. Lee, C. L. Darling, and D. Fried, “Nondestructive assessment of the severity of occlusal caries lesions with near-infrared imaging at 1310 nm,” J. Biomed. Opt.15, 047011 (2010).
[CrossRef] [PubMed]

Lee, D.

C. Lee, D. Lee, C. L. Darling, and D. Fried, “Nondestructive assessment of the severity of occlusal caries lesions with near-infrared imaging at 1310 nm,” J. Biomed. Opt.15, 047011 (2010).
[CrossRef] [PubMed]

Lestander, T.

P. Geladi, J. Burger, and T. Lestander, “Hyperspectral imaging: calibration problems and solutions,” Chemometr. Intell. Lab.72, 209–217 (2004).
[CrossRef]

Lew, R. A.

S. V. Panasyuk, S. Yang, D. V. Faller, D. Ngo, R. A. Lew, J. E. Freeman, and A. E. Rogers, “Medical hyperspectral imaging to facilitate residual tumor identification during surgery,” Cancer Biol. Ther.6, 439–446 (2007).
[CrossRef] [PubMed]

Likar, B.

J. Katrašnik, F. Pernuš, and B. Likar, “Illumination system characterization for hyperspectral imaging,” Proc. SPIE7891, 78910T (2011).
[CrossRef]

J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging systems,” Chemometr. Intell. Lab.101, 23–29 (2010).
[CrossRef]

J. Katrašnik, F. Pernuš, and B. Likar, “Radiometric calibration and noise estimation of hyperspectral imaging systems,” Submitted for publication (2012).

Lorente, D.

D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment,” Food Bioprocess Tech.5, 1121–1142 (2011).
[CrossRef]

Meyer, G. E.

G. K. Naganathan, L. M. Grimes, J. Subbiah, C. R. Calkins, A. Samal, and G. E. Meyer, “Visible/near-infrared hyperspectral imaging for beef tenderness prediction,” Comput. Electron. Agr.64, 225–233 (2008).
[CrossRef]

Moltó, E.

J. Gómez-Sanchis, E. Moltó, G. Camps-Valls, L. Gómez-Chova, N. Aleixos, and J. Blasco, “Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits,” J. Food Eng.85, 191–200 (2008).
[CrossRef]

Naganathan, G. K.

G. K. Naganathan, L. M. Grimes, J. Subbiah, C. R. Calkins, A. Samal, and G. E. Meyer, “Visible/near-infrared hyperspectral imaging for beef tenderness prediction,” Comput. Electron. Agr.64, 225–233 (2008).
[CrossRef]

Ngo, D.

S. V. Panasyuk, S. Yang, D. V. Faller, D. Ngo, R. A. Lew, J. E. Freeman, and A. E. Rogers, “Medical hyperspectral imaging to facilitate residual tumor identification during surgery,” Cancer Biol. Ther.6, 439–446 (2007).
[CrossRef] [PubMed]

Nicolaï, B. M.

B. M. Nicolaï, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K. I. Theron, and J. Lammertyn, “Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review,” Postharvest Biol. Tec.46, 99–118 (2007).
[CrossRef]

Panasyuk, S. V.

S. V. Panasyuk, S. Yang, D. V. Faller, D. Ngo, R. A. Lew, J. E. Freeman, and A. E. Rogers, “Medical hyperspectral imaging to facilitate residual tumor identification during surgery,” Cancer Biol. Ther.6, 439–446 (2007).
[CrossRef] [PubMed]

Peirs, A.

B. M. Nicolaï, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K. I. Theron, and J. Lammertyn, “Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review,” Postharvest Biol. Tec.46, 99–118 (2007).
[CrossRef]

Pernuš, F.

J. Katrašnik, F. Pernuš, and B. Likar, “Illumination system characterization for hyperspectral imaging,” Proc. SPIE7891, 78910T (2011).
[CrossRef]

J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging systems,” Chemometr. Intell. Lab.101, 23–29 (2010).
[CrossRef]

J. Katrašnik, F. Pernuš, and B. Likar, “Radiometric calibration and noise estimation of hyperspectral imaging systems,” Submitted for publication (2012).

Peterson, D.

B. Bennedsen, D. Peterson, and A. Tabb, “Identifying defects in images of rotating apples,” Comput. Electron. Agr.48, 92–102 (2005).
[CrossRef]

Pharr, M.

M. Pharr and G. Humphreys, Physically Based Rendering: From Theory To Implementation, The Morgan Kaufmann Series in Interactive 3D Technology (Elsevier/Morgan Kaufmann, 2004).

Polder, G.

G. Polder, G. V. D. Heijden, and I. Young, “Spectral image analysis for measuring ripeness of tomatoes,” T. ASAE45, 1155–1161 (2002).

Pretty, I.

C. Zakian, I. Pretty, and R. Ellwood, “Near-infared hyperspectral imaging of teeth for dental caries detection,” J. Biomed. Opt.14, 064047 (2009).
[CrossRef]

Randeberg, L. L.

L. L. Randeberg, E. L. P. Larsen, and L. O. Svaasand, “Characterization of vascular structures and skin bruises using hyperspectral imaging, image analysis and diffusion theory,” J. Biophotonics3, 53–65 (2010).
[CrossRef]

Reich, G.

G. Reich, “Near-infrared spectroscopy and imaging: basic principles and pharmaceutical applications,” Adv. Drug Deliver. Rev.57, 1109–1143 (2005).
[CrossRef]

Reinecke, M.

K. M. Górski, E. Hivon, A. J. Banday, B. D. Wandelt, F. K. Hansen, M. Reinecke, and M. Bartelmann, “HEALPix: A framework for high-resolution discretization and fast analysis of data distributed on the sphere,” Astrophys. J.622, 759–771 (2005).
[CrossRef]

Rogers, A. E.

S. V. Panasyuk, S. Yang, D. V. Faller, D. Ngo, R. A. Lew, J. E. Freeman, and A. E. Rogers, “Medical hyperspectral imaging to facilitate residual tumor identification during surgery,” Cancer Biol. Ther.6, 439–446 (2007).
[CrossRef] [PubMed]

Roggo, Y.

C. Gendrin, Y. Roggo, and C. Collet, “Pharmaceutical applications of vibrational chemical imaging and chemometrics: A review,” J. Pharmaceut. Biomed.48, 533–553 (2008).
[CrossRef]

Saeys, W.

B. M. Nicolaï, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K. I. Theron, and J. Lammertyn, “Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review,” Postharvest Biol. Tec.46, 99–118 (2007).
[CrossRef]

Samal, A.

G. K. Naganathan, L. M. Grimes, J. Subbiah, C. R. Calkins, A. Samal, and G. E. Meyer, “Visible/near-infrared hyperspectral imaging for beef tenderness prediction,” Comput. Electron. Agr.64, 225–233 (2008).
[CrossRef]

Stamatas, G. N.

G. N. Stamatas and N. Kollias, “In vivo documentation of cutaneous inflammation using spectral imaging,” J. Biomed. Opt.12, 051603 (2007).
[CrossRef] [PubMed]

Subbiah, J.

G. K. Naganathan, L. M. Grimes, J. Subbiah, C. R. Calkins, A. Samal, and G. E. Meyer, “Visible/near-infrared hyperspectral imaging for beef tenderness prediction,” Comput. Electron. Agr.64, 225–233 (2008).
[CrossRef]

Svaasand, L. O.

L. L. Randeberg, E. L. P. Larsen, and L. O. Svaasand, “Characterization of vascular structures and skin bruises using hyperspectral imaging, image analysis and diffusion theory,” J. Biophotonics3, 53–65 (2010).
[CrossRef]

Tabb, A.

B. Bennedsen, D. Peterson, and A. Tabb, “Identifying defects in images of rotating apples,” Comput. Electron. Agr.48, 92–102 (2005).
[CrossRef]

Tanaka, N.

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci.102, 852–857 (2011).
[CrossRef] [PubMed]

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE T. Bio.-Med. Eng.57, 2011–2017 (2010).
[CrossRef]

Theron, K. I.

B. M. Nicolaï, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K. I. Theron, and J. Lammertyn, “Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review,” Postharvest Biol. Tec.46, 99–118 (2007).
[CrossRef]

Uto, K.

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci.102, 852–857 (2011).
[CrossRef] [PubMed]

Wandelt, B. D.

K. M. Górski, E. Hivon, A. J. Banday, B. D. Wandelt, F. K. Hansen, M. Reinecke, and M. Bartelmann, “HEALPix: A framework for high-resolution discretization and fast analysis of data distributed on the sphere,” Astrophys. J.622, 759–771 (2005).
[CrossRef]

Yang, S.

S. V. Panasyuk, S. Yang, D. V. Faller, D. Ngo, R. A. Lew, J. E. Freeman, and A. E. Rogers, “Medical hyperspectral imaging to facilitate residual tumor identification during surgery,” Cancer Biol. Ther.6, 439–446 (2007).
[CrossRef] [PubMed]

Young, I.

G. Polder, G. V. D. Heijden, and I. Young, “Spectral image analysis for measuring ripeness of tomatoes,” T. ASAE45, 1155–1161 (2002).

Zakian, C.

C. Zakian, I. Pretty, and R. Ellwood, “Near-infared hyperspectral imaging of teeth for dental caries detection,” J. Biomed. Opt.14, 064047 (2009).
[CrossRef]

Adv. Drug Deliver. Rev.

G. Reich, “Near-infrared spectroscopy and imaging: basic principles and pharmaceutical applications,” Adv. Drug Deliver. Rev.57, 1109–1143 (2005).
[CrossRef]

Astrophys. J.

K. M. Górski, E. Hivon, A. J. Banday, B. D. Wandelt, F. K. Hansen, M. Reinecke, and M. Bartelmann, “HEALPix: A framework for high-resolution discretization and fast analysis of data distributed on the sphere,” Astrophys. J.622, 759–771 (2005).
[CrossRef]

Cancer Biol. Ther.

S. V. Panasyuk, S. Yang, D. V. Faller, D. Ngo, R. A. Lew, J. E. Freeman, and A. E. Rogers, “Medical hyperspectral imaging to facilitate residual tumor identification during surgery,” Cancer Biol. Ther.6, 439–446 (2007).
[CrossRef] [PubMed]

Cancer Sci.

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci.102, 852–857 (2011).
[CrossRef] [PubMed]

Chemometr. Intell. Lab.

P. Geladi, J. Burger, and T. Lestander, “Hyperspectral imaging: calibration problems and solutions,” Chemometr. Intell. Lab.72, 209–217 (2004).
[CrossRef]

J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging systems,” Chemometr. Intell. Lab.101, 23–29 (2010).
[CrossRef]

Comput. Electron. Agr.

G. K. Naganathan, L. M. Grimes, J. Subbiah, C. R. Calkins, A. Samal, and G. E. Meyer, “Visible/near-infrared hyperspectral imaging for beef tenderness prediction,” Comput. Electron. Agr.64, 225–233 (2008).
[CrossRef]

Y. Chen, K. Chao, and M. Kim, “Machine vision technology for agricultural applications,” Comput. Electron. Agr.36, 173–191 (2002).
[CrossRef]

B. Bennedsen, D. Peterson, and A. Tabb, “Identifying defects in images of rotating apples,” Comput. Electron. Agr.48, 92–102 (2005).
[CrossRef]

Food Bioprocess Tech.

D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment,” Food Bioprocess Tech.5, 1121–1142 (2011).
[CrossRef]

IEEE T. Bio.-Med. Eng.

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE T. Bio.-Med. Eng.57, 2011–2017 (2010).
[CrossRef]

J. Biomed. Opt.

G. N. Stamatas and N. Kollias, “In vivo documentation of cutaneous inflammation using spectral imaging,” J. Biomed. Opt.12, 051603 (2007).
[CrossRef] [PubMed]

C. Zakian, I. Pretty, and R. Ellwood, “Near-infared hyperspectral imaging of teeth for dental caries detection,” J. Biomed. Opt.14, 064047 (2009).
[CrossRef]

C. Lee, D. Lee, C. L. Darling, and D. Fried, “Nondestructive assessment of the severity of occlusal caries lesions with near-infrared imaging at 1310 nm,” J. Biomed. Opt.15, 047011 (2010).
[CrossRef] [PubMed]

J. Biophotonics

L. L. Randeberg, E. L. P. Larsen, and L. O. Svaasand, “Characterization of vascular structures and skin bruises using hyperspectral imaging, image analysis and diffusion theory,” J. Biophotonics3, 53–65 (2010).
[CrossRef]

J. Food Eng.

J. Gómez-Sanchis, E. Moltó, G. Camps-Valls, L. Gómez-Chova, N. Aleixos, and J. Blasco, “Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits,” J. Food Eng.85, 191–200 (2008).
[CrossRef]

J. Pharmaceut. Biomed.

C. Gendrin, Y. Roggo, and C. Collet, “Pharmaceutical applications of vibrational chemical imaging and chemometrics: A review,” J. Pharmaceut. Biomed.48, 533–553 (2008).
[CrossRef]

Postharvest Biol. Tec.

B. M. Nicolaï, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K. I. Theron, and J. Lammertyn, “Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review,” Postharvest Biol. Tec.46, 99–118 (2007).
[CrossRef]

Proc. SPIE

J. Katrašnik, F. Pernuš, and B. Likar, “Illumination system characterization for hyperspectral imaging,” Proc. SPIE7891, 78910T (2011).
[CrossRef]

T. ASAE

G. Polder, G. V. D. Heijden, and I. Young, “Spectral image analysis for measuring ripeness of tomatoes,” T. ASAE45, 1155–1161 (2002).

Other

J. Katrašnik, F. Pernuš, and B. Likar, “Radiometric calibration and noise estimation of hyperspectral imaging systems,” Submitted for publication (2012).

M. Pharr and G. Humphreys, Physically Based Rendering: From Theory To Implementation, The Morgan Kaufmann Series in Interactive 3D Technology (Elsevier/Morgan Kaufmann, 2004).

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Figures (10)

Fig. 1
Fig. 1

Drawings of tested and characterized illumination systems.

Fig. 2
Fig. 2

Target for measuring illumination system’s angular uniformity.

Fig. 3
Fig. 3

Acquisition of the sphere image for calculation of sphere positions.

Fig. 4
Fig. 4

The method for obtaining the angular image.

Fig. 5
Fig. 5

Mean spectral intensity images of the diffuse reflectance target illuminated with the tested illumination systems.

Fig. 6
Fig. 6

Images of differences between each pixel spectrum to the mean image spectrum of the tested illumination systems (Eq. (6)).

Fig. 7
Fig. 7

Spatial non-uniformity results for the tested illumination systems.

Fig. 8
Fig. 8

Angular intensity images of the tested illumination systems (Eq. (9)).

Fig. 9
Fig. 9

Image of differences between each incoming light ray direction spectrum to the mean incoming light direction spectrum (Eq. (13)).

Fig. 10
Fig. 10

Angular non-uniformity results for the tested illumination systems.

Equations (14)

Equations on this page are rendered with MathJax. Learn more.

HS ¯ ( x i , y j ) = 1 K k = 1 K HS ( x i , y j , λ k ) ,
HS ¯ ¯ = 1 N M i = 1 N j = 1 M HS ¯ ( x i , y j ) ,
SIN = 1 N M i = 1 N j = 1 M ( HS ¯ ( x i , y j ) HS ¯ ¯ ) 2
HS ^ ( x i , y j , λ k ) = HS ( x i , y j , λ k ) HS ¯ ( x i , y j ) std λ [ HS ( x i , y j , λ k ) ] ,
s ¯ ( λ k ) = 1 N M i = 1 N j = 1 M HS ^ ( x i , y j , λ k ) .
d SSN ( x i , y j ) = 1 K k = 1 K ( HS ^ ( x i , y j , λ k ) s ¯ ( λ k ) ) 2 .
SSN = 1 N M i = 1 N j = 1 M d SSN ( x i , y j ) .
c ¯ l = 1 K k = 1 K c l ( λ k ) ,
c ^ l = c ¯ l max c ¯ l
AIN = 1 L l = 1 L ( 1 c ^ l )
c ˜ l ( λ k ) = c l ( λ k ) c ¯ l std λ [ c l ( λ k ) ] .
č ( λ k ) = l = 1 L c ^ l c ˜ l ( λ k ) l = 1 L c ^ l
d ASN ( l ) = c ^ l 1 K k = 1 K ( c ˜ l ( λ k ) č ( λ k ) ) 2 .
ASN = 1 L l = 1 L d ASN ( l ) .

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