Abstract

Hyperspectral imaging (HSI) provides more detailed information than red-green-blue (RGB) imaging, and therefore has potential applications in computer-aided pathological diagnosis. This study aimed to develop a pattern recognition method based on HSI, called hyperspectral analysis of pathological slides based on stain spectrum (HAPSS), to detect cancers in hematoxylin and eosin-stained pathological slides of pancreatic tumors. The samples, comprising hyperspectral cubes of 420–750 nm, were harvested for HSI and tissue microarray (TMA) analysis. As a result of conducting HAPSS experiments with a support vector machine (SVM) classifier, we obtained maximal accuracy of 94%, a 14% improvement over the widely used RGB images. Thus, HAPSS is a suitable method to automatically detect tumors in pathological slides of the pancreas.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (2)

2017 (2)

T. Kim, M. A. Visbal-Onufrak, R. L. Konger, and Y. L. Kim, “Data-driven imaging of tissue inflammation using RGB-based hyperspectral reconstruction toward personal monitoring of dermatologic health,” Biomed. Opt. Express 8(11), 5282–5296 (2017).
[Crossref]

A. Loupy, M. Haas, K. Solez, L. Racusen, D. Glotz, D. Seron, B. J. Nankivell, R. B. Colvin, M. Afrouzian, E. Akalin, N. Alachkar, S. Bagnasco, J. U. Becker, L. Cornell, C. Drachenberg, D. Dragun, H. de Kort, I. W. Gibson, E. S. Kraus, C. Lefaucheur, C. Legendre, H. Liapis, T. Muthukumar, V. Nickeleit, B. Orandi, W. Park, M. Rabant, P. Randhawa, E. F. Reed, C. Roufosse, S. V. Seshan, B. Sis, H. K. Singh, C. Schinstock, A. Tambur, A. Zeevi, and M. Mengel, “The Banff 2015 Kidney Meeting Report: Current Challenges in Rejection Classification and Prospects for Adopting Molecular Pathology,” Am. J. Transplant. 17(1), 28–41 (2017).
[Crossref]

2016 (2)

M. Ishikawa, Y. Murakami, S. T. Ahi, M. Yamaguchi, N. Kobayashi, T. Kiyuna, Y. Yamashita, A. Saito, T. Abe, A. Hashiguchi, and M. Sakamoto, “Automatic quantification of morphological features for hepatic trabeculae analysis in stained liver specimens,” J. Med. Imag. 3(2), 027502 (2016).
[Crossref]

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[Crossref]

2015 (3)

D. U. Campos-Delgado, O. G. Navarro, E. R. Arce-Santana, and J. A. Jo, “Extended output phasor representation of multi-spectral fluorescence lifetime imaging microscopy,” Biomed. Opt. Express 6(6), 2088–2105 (2015).
[Crossref]

A. Goto, J. Nishikawa, and S. Kiyotoki et al., “Use of hyperspectral imaging technology to develop a diagnostic support system for gastric cancer,” J. Biomed. Opt. 20(1), 016017 (2015).
[Crossref]

B. Mortazavi, E. Nemati, K. VanderWall, H. G. Flores-Rodriguez, J. Y. Jacinta Cai, J. Lucier, A. Naeim, and M. Sarrafzadeh, “Can Smartwatches Replace Smartphones for Posture Tracking?” Sensors 15(10), 26783–26800 (2015).
[Crossref]

2014 (3)

C. Lu and M. Mandal, “Toward Automatic Mitotic Cell Detection and Segmentation in Multispectral HSI to pathological Images,” IEEE J. Biomed. Health Inform. 18(2), 594–605 (2014).
[Crossref]

G. Lu and B. Fei, “Medical hyperspectral imaging: a review,” J. Biomed. Opt. 19(1), 010901 (2014).
[Crossref]

C. Atupelage, H. Nagahashi, F. Kimura, M. Yamaguchi, A. Tokiya, A. Hashiguchi, and M. Sakamoto, “Computational hepatocellular carcinoma tumor grading based on cell nuclei classification,” J. Med. Imag. 1(3), 034501 (2014).
[Crossref]

2013 (2)

2012 (3)

I. Diebele, I. Kuzmina, A. Lihachev, J. Kapostinsh, A. Derjabo, L. Valeine, and J. Spigulis, “Clinical evaluation of melanomas and common nevi by spectral imaging,” Biomed. Opt. Express 3(3), 467–472 (2012).
[Crossref]

H. Akbari, L. Halig, D. M. Schuster, B. Fei, A. Osunkoya, V. Master, P. Nieh, and G. Chen, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 0760051 (2012).
[Crossref]

Y. Takara, N. Manago, H. Saito, Y. Mabuchi, A. Kondoh, T. Fujimori, F. Ando, M. Suzuki, and H. Kuze, “Remote sensing applications with NH hyperspectral portable video camera,” Proc. SPIE 8527, 85271G (2012).
[Crossref]

2011 (1)

2010 (2)

M. Esterman, B. J. Tamber-Rosenau, Y. Chiu, and S. Yantis, “Avoiding non-independence in fMRI data analysis: Leave one subject out,” NeuroImage 50(2), 572–576 (2010).
[Crossref]

M. Hidalgo, “Pancreatic cancer,” N. Engl. J. Med. 362(17), 1605–1617 (2010).
[Crossref]

2009 (2)

R. S. Weinstein, A. R. Graham, L. C. Richter, G. P. Barker, E. A. Krupinski, A. M. Lopes, K. A. Erps, A. K. Bhattacharyya, Y. Yagi, and J. R. Gilbertson, “Overview of telepathology, virtual microscopy, and whole slide imaging: prospects for the future,” Hum. Pathol. 40(8), 1057–1069 (2009).
[Crossref]

M. N. Gurcan, L. E. Boucheron, A. Can, A. Madabhushi, N. M. Rajpoot, and B. Yener, “histopathological image analysis: a review,” IEEE Rev. Biomed. Eng. 2, 147–171 (2009).
[Crossref]

2008 (1)

A. H. Fischer, K. A. Jacobson, J. Rose, and R. Zeller, “Hematoxylin and eosin staining of tissue and cell sections,” Cold Spring Harb. Protoc. 2008(6), 4986 (2008).
[Crossref]

2007 (1)

L. E. Boucheron, Z. Bi, N. R. Harvey, BS. Manjunath, and D. L. Rimm, “Utility of multispectral imaging for nuclear classification of routine clinical histopathology imagery,” BMC Cell Biol. 8(S1), S8 (2007).
[Crossref]

2005 (2)

T. Abe, Y. Murakami, M. Yamaguchi, N. Ohyama, and Y. Yagi, “Color Correction of Pathological Images Based on Dye Amount Quantification,” Opt. Rev. 12(4), 293–300 (2005).
[Crossref]

P. A. Bautista, T. Abe, M. Yamaguchi, Y. Yagi, and N. Ohyama, “Digital staining for multispectral images of pathological tissue specimens based on combined classification of spectral transmittance,” Comput. Med. Imag. Grap. 29(8), 649–657 (2005).
[Crossref]

2003 (1)

A. J. Sims, M. K. Bennett, and A. Murray, “Image analysis can be used to detect spatial changes in the histopathology of pancreatic tumours,” Phys. Med. Biol. 48(13), N183–N191 (2003).
[Crossref]

2002 (1)

J. Gil, H. Wu, and B. Y. Wang, “Image Analysis and Morphometry in the Diagnosis of Breast Cancer,” Microsc. Res. Tech. 59(2), 109–118 (2002).
[Crossref]

2001 (1)

L. Breiman, “Random Forests,” Machine Learning 45(1), 5–32 (2001).
[Crossref]

1985 (1)

A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging Spectrometry for Earth Remote Sensing,” Science 228(4704), 1147–1153 (1985).
[Crossref]

Abe, T.

M. Ishikawa, Y. Murakami, S. T. Ahi, M. Yamaguchi, N. Kobayashi, T. Kiyuna, Y. Yamashita, A. Saito, T. Abe, A. Hashiguchi, and M. Sakamoto, “Automatic quantification of morphological features for hepatic trabeculae analysis in stained liver specimens,” J. Med. Imag. 3(2), 027502 (2016).
[Crossref]

P. A. Bautista, T. Abe, M. Yamaguchi, Y. Yagi, and N. Ohyama, “Digital staining for multispectral images of pathological tissue specimens based on combined classification of spectral transmittance,” Comput. Med. Imag. Grap. 29(8), 649–657 (2005).
[Crossref]

T. Abe, Y. Murakami, M. Yamaguchi, N. Ohyama, and Y. Yagi, “Color Correction of Pathological Images Based on Dye Amount Quantification,” Opt. Rev. 12(4), 293–300 (2005).
[Crossref]

M. Ishikawa, N. Kobayashi, H. Komagata, K. Shinoda, M. Yamaguchi, T. Abe, A. Hashiguchi, and M. Sakamoto, “An accurate method of extracting fat droplets in liver images for quantitative evaluation,” Proc. SPIE 9420, Medical Imaging 2015: Digital Pathology, 94200Y (2015).

Afrouzian, M.

A. Loupy, M. Haas, K. Solez, L. Racusen, D. Glotz, D. Seron, B. J. Nankivell, R. B. Colvin, M. Afrouzian, E. Akalin, N. Alachkar, S. Bagnasco, J. U. Becker, L. Cornell, C. Drachenberg, D. Dragun, H. de Kort, I. W. Gibson, E. S. Kraus, C. Lefaucheur, C. Legendre, H. Liapis, T. Muthukumar, V. Nickeleit, B. Orandi, W. Park, M. Rabant, P. Randhawa, E. F. Reed, C. Roufosse, S. V. Seshan, B. Sis, H. K. Singh, C. Schinstock, A. Tambur, A. Zeevi, and M. Mengel, “The Banff 2015 Kidney Meeting Report: Current Challenges in Rejection Classification and Prospects for Adopting Molecular Pathology,” Am. J. Transplant. 17(1), 28–41 (2017).
[Crossref]

Agner, S.

S. Naik, S. Doyle, S. Agner, A. Madabhushi, M. Feldman, and J. Tomaszewski, “Automated Gland Segmentation and Gleason Grading of Prostate histology by Integrating Low-, High-level and Domain Specific Information,” Workshop on Microscopic Image Analysis with Applications in Biology Piscataway (2007).

Ahi, S. T.

M. Ishikawa, Y. Murakami, S. T. Ahi, M. Yamaguchi, N. Kobayashi, T. Kiyuna, Y. Yamashita, A. Saito, T. Abe, A. Hashiguchi, and M. Sakamoto, “Automatic quantification of morphological features for hepatic trabeculae analysis in stained liver specimens,” J. Med. Imag. 3(2), 027502 (2016).
[Crossref]

Akalin, E.

A. Loupy, M. Haas, K. Solez, L. Racusen, D. Glotz, D. Seron, B. J. Nankivell, R. B. Colvin, M. Afrouzian, E. Akalin, N. Alachkar, S. Bagnasco, J. U. Becker, L. Cornell, C. Drachenberg, D. Dragun, H. de Kort, I. W. Gibson, E. S. Kraus, C. Lefaucheur, C. Legendre, H. Liapis, T. Muthukumar, V. Nickeleit, B. Orandi, W. Park, M. Rabant, P. Randhawa, E. F. Reed, C. Roufosse, S. V. Seshan, B. Sis, H. K. Singh, C. Schinstock, A. Tambur, A. Zeevi, and M. Mengel, “The Banff 2015 Kidney Meeting Report: Current Challenges in Rejection Classification and Prospects for Adopting Molecular Pathology,” Am. J. Transplant. 17(1), 28–41 (2017).
[Crossref]

Akbari, H.

H. Akbari, L. Halig, D. M. Schuster, B. Fei, A. Osunkoya, V. Master, P. Nieh, and G. Chen, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 0760051 (2012).
[Crossref]

Alachkar, N.

A. Loupy, M. Haas, K. Solez, L. Racusen, D. Glotz, D. Seron, B. J. Nankivell, R. B. Colvin, M. Afrouzian, E. Akalin, N. Alachkar, S. Bagnasco, J. U. Becker, L. Cornell, C. Drachenberg, D. Dragun, H. de Kort, I. W. Gibson, E. S. Kraus, C. Lefaucheur, C. Legendre, H. Liapis, T. Muthukumar, V. Nickeleit, B. Orandi, W. Park, M. Rabant, P. Randhawa, E. F. Reed, C. Roufosse, S. V. Seshan, B. Sis, H. K. Singh, C. Schinstock, A. Tambur, A. Zeevi, and M. Mengel, “The Banff 2015 Kidney Meeting Report: Current Challenges in Rejection Classification and Prospects for Adopting Molecular Pathology,” Am. J. Transplant. 17(1), 28–41 (2017).
[Crossref]

Amyot, F.

Ando, F.

Y. Takara, N. Manago, H. Saito, Y. Mabuchi, A. Kondoh, T. Fujimori, F. Ando, M. Suzuki, and H. Kuze, “Remote sensing applications with NH hyperspectral portable video camera,” Proc. SPIE 8527, 85271G (2012).
[Crossref]

Arce-Santana, E. R.

Atupelage, C.

C. Atupelage, H. Nagahashi, F. Kimura, M. Yamaguchi, A. Tokiya, A. Hashiguchi, and M. Sakamoto, “Computational hepatocellular carcinoma tumor grading based on cell nuclei classification,” J. Med. Imag. 1(3), 034501 (2014).
[Crossref]

Bagnasco, S.

A. Loupy, M. Haas, K. Solez, L. Racusen, D. Glotz, D. Seron, B. J. Nankivell, R. B. Colvin, M. Afrouzian, E. Akalin, N. Alachkar, S. Bagnasco, J. U. Becker, L. Cornell, C. Drachenberg, D. Dragun, H. de Kort, I. W. Gibson, E. S. Kraus, C. Lefaucheur, C. Legendre, H. Liapis, T. Muthukumar, V. Nickeleit, B. Orandi, W. Park, M. Rabant, P. Randhawa, E. F. Reed, C. Roufosse, S. V. Seshan, B. Sis, H. K. Singh, C. Schinstock, A. Tambur, A. Zeevi, and M. Mengel, “The Banff 2015 Kidney Meeting Report: Current Challenges in Rejection Classification and Prospects for Adopting Molecular Pathology,” Am. J. Transplant. 17(1), 28–41 (2017).
[Crossref]

Barker, G. P.

R. S. Weinstein, A. R. Graham, L. C. Richter, G. P. Barker, E. A. Krupinski, A. M. Lopes, K. A. Erps, A. K. Bhattacharyya, Y. Yagi, and J. R. Gilbertson, “Overview of telepathology, virtual microscopy, and whole slide imaging: prospects for the future,” Hum. Pathol. 40(8), 1057–1069 (2009).
[Crossref]

Bautista, P. A.

P. A. Bautista, T. Abe, M. Yamaguchi, Y. Yagi, and N. Ohyama, “Digital staining for multispectral images of pathological tissue specimens based on combined classification of spectral transmittance,” Comput. Med. Imag. Grap. 29(8), 649–657 (2005).
[Crossref]

Becker, J. U.

A. Loupy, M. Haas, K. Solez, L. Racusen, D. Glotz, D. Seron, B. J. Nankivell, R. B. Colvin, M. Afrouzian, E. Akalin, N. Alachkar, S. Bagnasco, J. U. Becker, L. Cornell, C. Drachenberg, D. Dragun, H. de Kort, I. W. Gibson, E. S. Kraus, C. Lefaucheur, C. Legendre, H. Liapis, T. Muthukumar, V. Nickeleit, B. Orandi, W. Park, M. Rabant, P. Randhawa, E. F. Reed, C. Roufosse, S. V. Seshan, B. Sis, H. K. Singh, C. Schinstock, A. Tambur, A. Zeevi, and M. Mengel, “The Banff 2015 Kidney Meeting Report: Current Challenges in Rejection Classification and Prospects for Adopting Molecular Pathology,” Am. J. Transplant. 17(1), 28–41 (2017).
[Crossref]

Bennett, M. K.

A. J. Sims, M. K. Bennett, and A. Murray, “Image analysis can be used to detect spatial changes in the histopathology of pancreatic tumours,” Phys. Med. Biol. 48(13), N183–N191 (2003).
[Crossref]

Bhattacharyya, A. K.

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B. Mortazavi, E. Nemati, K. VanderWall, H. G. Flores-Rodriguez, J. Y. Jacinta Cai, J. Lucier, A. Naeim, and M. Sarrafzadeh, “Can Smartwatches Replace Smartphones for Posture Tracking?” Sensors 15(10), 26783–26800 (2015).
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H. Ogihara, Y. Hamamoto, Y. Fujita, A. Goto, J. Nishikawa, and I. Sakaida, “Development of a Gastric CancerDiagnostic Support System with a Pattern Recognition Method Using a Hyperspectral Camera,” J. Sens. 2016, 1–6 (2016).
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A. Loupy, M. Haas, K. Solez, L. Racusen, D. Glotz, D. Seron, B. J. Nankivell, R. B. Colvin, M. Afrouzian, E. Akalin, N. Alachkar, S. Bagnasco, J. U. Becker, L. Cornell, C. Drachenberg, D. Dragun, H. de Kort, I. W. Gibson, E. S. Kraus, C. Lefaucheur, C. Legendre, H. Liapis, T. Muthukumar, V. Nickeleit, B. Orandi, W. Park, M. Rabant, P. Randhawa, E. F. Reed, C. Roufosse, S. V. Seshan, B. Sis, H. K. Singh, C. Schinstock, A. Tambur, A. Zeevi, and M. Mengel, “The Banff 2015 Kidney Meeting Report: Current Challenges in Rejection Classification and Prospects for Adopting Molecular Pathology,” Am. J. Transplant. 17(1), 28–41 (2017).
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Shinoda, K.

M. Ishikawa, N. Kobayashi, H. Komagata, K. Shinoda, M. Yamaguchi, T. Abe, A. Hashiguchi, and M. Sakamoto, “An accurate method of extracting fat droplets in liver images for quantitative evaluation,” Proc. SPIE 9420, Medical Imaging 2015: Digital Pathology, 94200Y (2015).

Simeone, D.

Sims, A. J.

A. J. Sims, M. K. Bennett, and A. Murray, “Image analysis can be used to detect spatial changes in the histopathology of pancreatic tumours,” Phys. Med. Biol. 48(13), N183–N191 (2003).
[Crossref]

Singh, H. K.

A. Loupy, M. Haas, K. Solez, L. Racusen, D. Glotz, D. Seron, B. J. Nankivell, R. B. Colvin, M. Afrouzian, E. Akalin, N. Alachkar, S. Bagnasco, J. U. Becker, L. Cornell, C. Drachenberg, D. Dragun, H. de Kort, I. W. Gibson, E. S. Kraus, C. Lefaucheur, C. Legendre, H. Liapis, T. Muthukumar, V. Nickeleit, B. Orandi, W. Park, M. Rabant, P. Randhawa, E. F. Reed, C. Roufosse, S. V. Seshan, B. Sis, H. K. Singh, C. Schinstock, A. Tambur, A. Zeevi, and M. Mengel, “The Banff 2015 Kidney Meeting Report: Current Challenges in Rejection Classification and Prospects for Adopting Molecular Pathology,” Am. J. Transplant. 17(1), 28–41 (2017).
[Crossref]

Sis, B.

A. Loupy, M. Haas, K. Solez, L. Racusen, D. Glotz, D. Seron, B. J. Nankivell, R. B. Colvin, M. Afrouzian, E. Akalin, N. Alachkar, S. Bagnasco, J. U. Becker, L. Cornell, C. Drachenberg, D. Dragun, H. de Kort, I. W. Gibson, E. S. Kraus, C. Lefaucheur, C. Legendre, H. Liapis, T. Muthukumar, V. Nickeleit, B. Orandi, W. Park, M. Rabant, P. Randhawa, E. F. Reed, C. Roufosse, S. V. Seshan, B. Sis, H. K. Singh, C. Schinstock, A. Tambur, A. Zeevi, and M. Mengel, “The Banff 2015 Kidney Meeting Report: Current Challenges in Rejection Classification and Prospects for Adopting Molecular Pathology,” Am. J. Transplant. 17(1), 28–41 (2017).
[Crossref]

Smola, A.

B. Scholkopf and A. Smola, “Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond, Adaptive Computation and Machine Learning,” The MIT Press, Cambridge, MA: 2002.

Solez, K.

A. Loupy, M. Haas, K. Solez, L. Racusen, D. Glotz, D. Seron, B. J. Nankivell, R. B. Colvin, M. Afrouzian, E. Akalin, N. Alachkar, S. Bagnasco, J. U. Becker, L. Cornell, C. Drachenberg, D. Dragun, H. de Kort, I. W. Gibson, E. S. Kraus, C. Lefaucheur, C. Legendre, H. Liapis, T. Muthukumar, V. Nickeleit, B. Orandi, W. Park, M. Rabant, P. Randhawa, E. F. Reed, C. Roufosse, S. V. Seshan, B. Sis, H. K. Singh, C. Schinstock, A. Tambur, A. Zeevi, and M. Mengel, “The Banff 2015 Kidney Meeting Report: Current Challenges in Rejection Classification and Prospects for Adopting Molecular Pathology,” Am. J. Transplant. 17(1), 28–41 (2017).
[Crossref]

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A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging Spectrometry for Earth Remote Sensing,” Science 228(4704), 1147–1153 (1985).
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I. Diebele, I. Kuzmina, A. Lihachev, J. Kapostinsh, A. Derjabo, L. Valeine, and J. Spigulis, “Clinical evaluation of melanomas and common nevi by spectral imaging,” Biomed. Opt. Express 3(3), 467–472 (2012).
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I. Lihacova, A. Derjabo, and J. Spigulis, “A multispectral imaging approach for diagnostics of skin pathologies,” in Clinical and Biomedical Spectroscopy and Imaging III, V. Deckert and N. Ramanujam, eds., Vol. 8798 of SPIE Proceeding, Optical Society of America (2013).

Sun, L.

Suzuki, M.

Y. Takara, N. Manago, H. Saito, Y. Mabuchi, A. Kondoh, T. Fujimori, F. Ando, M. Suzuki, and H. Kuze, “Remote sensing applications with NH hyperspectral portable video camera,” Proc. SPIE 8527, 85271G (2012).
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Takara, Y.

Y. Takara, N. Manago, H. Saito, Y. Mabuchi, A. Kondoh, T. Fujimori, F. Ando, M. Suzuki, and H. Kuze, “Remote sensing applications with NH hyperspectral portable video camera,” Proc. SPIE 8527, 85271G (2012).
[Crossref]

Tamber-Rosenau, B. J.

M. Esterman, B. J. Tamber-Rosenau, Y. Chiu, and S. Yantis, “Avoiding non-independence in fMRI data analysis: Leave one subject out,” NeuroImage 50(2), 572–576 (2010).
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Tambur, A.

A. Loupy, M. Haas, K. Solez, L. Racusen, D. Glotz, D. Seron, B. J. Nankivell, R. B. Colvin, M. Afrouzian, E. Akalin, N. Alachkar, S. Bagnasco, J. U. Becker, L. Cornell, C. Drachenberg, D. Dragun, H. de Kort, I. W. Gibson, E. S. Kraus, C. Lefaucheur, C. Legendre, H. Liapis, T. Muthukumar, V. Nickeleit, B. Orandi, W. Park, M. Rabant, P. Randhawa, E. F. Reed, C. Roufosse, S. V. Seshan, B. Sis, H. K. Singh, C. Schinstock, A. Tambur, A. Zeevi, and M. Mengel, “The Banff 2015 Kidney Meeting Report: Current Challenges in Rejection Classification and Prospects for Adopting Molecular Pathology,” Am. J. Transplant. 17(1), 28–41 (2017).
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B. Mortazavi, E. Nemati, K. VanderWall, H. G. Flores-Rodriguez, J. Y. Jacinta Cai, J. Lucier, A. Naeim, and M. Sarrafzadeh, “Can Smartwatches Replace Smartphones for Posture Tracking?” Sensors 15(10), 26783–26800 (2015).
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Vane, G.

A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging Spectrometry for Earth Remote Sensing,” Science 228(4704), 1147–1153 (1985).
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Wilson, R. H.

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R. S. Weinstein, A. R. Graham, L. C. Richter, G. P. Barker, E. A. Krupinski, A. M. Lopes, K. A. Erps, A. K. Bhattacharyya, Y. Yagi, and J. R. Gilbertson, “Overview of telepathology, virtual microscopy, and whole slide imaging: prospects for the future,” Hum. Pathol. 40(8), 1057–1069 (2009).
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Yamaguchi, M.

M. Ishikawa, Y. Murakami, S. T. Ahi, M. Yamaguchi, N. Kobayashi, T. Kiyuna, Y. Yamashita, A. Saito, T. Abe, A. Hashiguchi, and M. Sakamoto, “Automatic quantification of morphological features for hepatic trabeculae analysis in stained liver specimens,” J. Med. Imag. 3(2), 027502 (2016).
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C. Atupelage, H. Nagahashi, F. Kimura, M. Yamaguchi, A. Tokiya, A. Hashiguchi, and M. Sakamoto, “Computational hepatocellular carcinoma tumor grading based on cell nuclei classification,” J. Med. Imag. 1(3), 034501 (2014).
[Crossref]

T. Abe, Y. Murakami, M. Yamaguchi, N. Ohyama, and Y. Yagi, “Color Correction of Pathological Images Based on Dye Amount Quantification,” Opt. Rev. 12(4), 293–300 (2005).
[Crossref]

P. A. Bautista, T. Abe, M. Yamaguchi, Y. Yagi, and N. Ohyama, “Digital staining for multispectral images of pathological tissue specimens based on combined classification of spectral transmittance,” Comput. Med. Imag. Grap. 29(8), 649–657 (2005).
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M. Ishikawa, N. Kobayashi, H. Komagata, K. Shinoda, M. Yamaguchi, T. Abe, A. Hashiguchi, and M. Sakamoto, “An accurate method of extracting fat droplets in liver images for quantitative evaluation,” Proc. SPIE 9420, Medical Imaging 2015: Digital Pathology, 94200Y (2015).

Yamashita, Y.

M. Ishikawa, Y. Murakami, S. T. Ahi, M. Yamaguchi, N. Kobayashi, T. Kiyuna, Y. Yamashita, A. Saito, T. Abe, A. Hashiguchi, and M. Sakamoto, “Automatic quantification of morphological features for hepatic trabeculae analysis in stained liver specimens,” J. Med. Imag. 3(2), 027502 (2016).
[Crossref]

Yantis, S.

M. Esterman, B. J. Tamber-Rosenau, Y. Chiu, and S. Yantis, “Avoiding non-independence in fMRI data analysis: Leave one subject out,” NeuroImage 50(2), 572–576 (2010).
[Crossref]

Yener, B.

M. N. Gurcan, L. E. Boucheron, A. Can, A. Madabhushi, N. M. Rajpoot, and B. Yener, “histopathological image analysis: a review,” IEEE Rev. Biomed. Eng. 2, 147–171 (2009).
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Yuan, C.

J. Lou, M. Zhou, Q. Li, C. Yuan, and H. Liu, “An Automatic Red Blood Cell Counting Method Based on Spectral Images,” Image and Signal Processing 2016, 1391–1396 (2016).

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A. Loupy, M. Haas, K. Solez, L. Racusen, D. Glotz, D. Seron, B. J. Nankivell, R. B. Colvin, M. Afrouzian, E. Akalin, N. Alachkar, S. Bagnasco, J. U. Becker, L. Cornell, C. Drachenberg, D. Dragun, H. de Kort, I. W. Gibson, E. S. Kraus, C. Lefaucheur, C. Legendre, H. Liapis, T. Muthukumar, V. Nickeleit, B. Orandi, W. Park, M. Rabant, P. Randhawa, E. F. Reed, C. Roufosse, S. V. Seshan, B. Sis, H. K. Singh, C. Schinstock, A. Tambur, A. Zeevi, and M. Mengel, “The Banff 2015 Kidney Meeting Report: Current Challenges in Rejection Classification and Prospects for Adopting Molecular Pathology,” Am. J. Transplant. 17(1), 28–41 (2017).
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A. H. Fischer, K. A. Jacobson, J. Rose, and R. Zeller, “Hematoxylin and eosin staining of tissue and cell sections,” Cold Spring Harb. Protoc. 2008(6), 4986 (2008).
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J. Lou, M. Zhou, Q. Li, C. Yuan, and H. Liu, “An Automatic Red Blood Cell Counting Method Based on Spectral Images,” Image and Signal Processing 2016, 1391–1396 (2016).

Am. J. Transplant. (1)

A. Loupy, M. Haas, K. Solez, L. Racusen, D. Glotz, D. Seron, B. J. Nankivell, R. B. Colvin, M. Afrouzian, E. Akalin, N. Alachkar, S. Bagnasco, J. U. Becker, L. Cornell, C. Drachenberg, D. Dragun, H. de Kort, I. W. Gibson, E. S. Kraus, C. Lefaucheur, C. Legendre, H. Liapis, T. Muthukumar, V. Nickeleit, B. Orandi, W. Park, M. Rabant, P. Randhawa, E. F. Reed, C. Roufosse, S. V. Seshan, B. Sis, H. K. Singh, C. Schinstock, A. Tambur, A. Zeevi, and M. Mengel, “The Banff 2015 Kidney Meeting Report: Current Challenges in Rejection Classification and Prospects for Adopting Molecular Pathology,” Am. J. Transplant. 17(1), 28–41 (2017).
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Appl. Spectrosc. (1)

Biomed. Opt. Express (7)

J. M. Kainerstorfer., J. D. Riley, M. Ehler, and L. Najafizadeh., F. Amyot, M. Hassan, and A. H. Gandjbakhche, “Quantitative principal component model for skin chromophore mapping using multi-spectral images and spatial priors,” Biomed. Opt. Express 2(5), 1040–1058 (2011).
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I. Diebele, I. Kuzmina, A. Lihachev, J. Kapostinsh, A. Derjabo, L. Valeine, and J. Spigulis, “Clinical evaluation of melanomas and common nevi by spectral imaging,” Biomed. Opt. Express 3(3), 467–472 (2012).
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A. Eguizabal, A. M. Laughney, P. B. García-Allende, V. Krishnaswamy, W. A. Wells, K. D. Paulsen, B. W. Pogue, J. M. Lopez-Higuera, and O. M. Conde, “Direct identification of breast cancer pathologies using blind separation of label-free localized reflectance measurements,” Biomed. Opt. Express 4(7), 1104–1118 (2013).
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L. Y. Seung, W. R. Lloyd, M. Chandra, R. H. Wilson, B. Mckenna, D. Simeone, J. Scheiman, and M. A. Mycek, “Characterizing human pancreatic cancer precursor using quantitative tissue optical spectroscopy,” Biomed. Opt. Express 4(12), 2828–2834 (2013).
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D. U. Campos-Delgado, O. G. Navarro, E. R. Arce-Santana, and J. A. Jo, “Extended output phasor representation of multi-spectral fluorescence lifetime imaging microscopy,” Biomed. Opt. Express 6(6), 2088–2105 (2015).
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T. Kim, M. A. Visbal-Onufrak, R. L. Konger, and Y. L. Kim, “Data-driven imaging of tissue inflammation using RGB-based hyperspectral reconstruction toward personal monitoring of dermatologic health,” Biomed. Opt. Express 8(11), 5282–5296 (2017).
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S. Ortega, H. Fabelo, R. Camacho, M. D. L. L. Plaza, G. M. Callico, and R. Sarmiento, “Detecting brain tumor in pathological slides using hyperspectral imaging,” Biomed. Opt. Express 9(2), 818–831 (2018).
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BMC Cell Biol. (1)

L. E. Boucheron, Z. Bi, N. R. Harvey, BS. Manjunath, and D. L. Rimm, “Utility of multispectral imaging for nuclear classification of routine clinical histopathology imagery,” BMC Cell Biol. 8(S1), S8 (2007).
[Crossref]

Cold Spring Harb. Protoc. (1)

A. H. Fischer, K. A. Jacobson, J. Rose, and R. Zeller, “Hematoxylin and eosin staining of tissue and cell sections,” Cold Spring Harb. Protoc. 2008(6), 4986 (2008).
[Crossref]

Comput. Med. Imag. Grap. (1)

P. A. Bautista, T. Abe, M. Yamaguchi, Y. Yagi, and N. Ohyama, “Digital staining for multispectral images of pathological tissue specimens based on combined classification of spectral transmittance,” Comput. Med. Imag. Grap. 29(8), 649–657 (2005).
[Crossref]

Hum. Pathol. (1)

R. S. Weinstein, A. R. Graham, L. C. Richter, G. P. Barker, E. A. Krupinski, A. M. Lopes, K. A. Erps, A. K. Bhattacharyya, Y. Yagi, and J. R. Gilbertson, “Overview of telepathology, virtual microscopy, and whole slide imaging: prospects for the future,” Hum. Pathol. 40(8), 1057–1069 (2009).
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IEEE J. Biomed. Health Inform. (1)

C. Lu and M. Mandal, “Toward Automatic Mitotic Cell Detection and Segmentation in Multispectral HSI to pathological Images,” IEEE J. Biomed. Health Inform. 18(2), 594–605 (2014).
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IEEE Rev. Biomed. Eng. (1)

M. N. Gurcan, L. E. Boucheron, A. Can, A. Madabhushi, N. M. Rajpoot, and B. Yener, “histopathological image analysis: a review,” IEEE Rev. Biomed. Eng. 2, 147–171 (2009).
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J. Biomed. Opt. (3)

G. Lu and B. Fei, “Medical hyperspectral imaging: a review,” J. Biomed. Opt. 19(1), 010901 (2014).
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H. Akbari, L. Halig, D. M. Schuster, B. Fei, A. Osunkoya, V. Master, P. Nieh, and G. Chen, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 0760051 (2012).
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A. Goto, J. Nishikawa, and S. Kiyotoki et al., “Use of hyperspectral imaging technology to develop a diagnostic support system for gastric cancer,” J. Biomed. Opt. 20(1), 016017 (2015).
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J. Med. Imag. (2)

M. Ishikawa, Y. Murakami, S. T. Ahi, M. Yamaguchi, N. Kobayashi, T. Kiyuna, Y. Yamashita, A. Saito, T. Abe, A. Hashiguchi, and M. Sakamoto, “Automatic quantification of morphological features for hepatic trabeculae analysis in stained liver specimens,” J. Med. Imag. 3(2), 027502 (2016).
[Crossref]

C. Atupelage, H. Nagahashi, F. Kimura, M. Yamaguchi, A. Tokiya, A. Hashiguchi, and M. Sakamoto, “Computational hepatocellular carcinoma tumor grading based on cell nuclei classification,” J. Med. Imag. 1(3), 034501 (2014).
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J. Sens. (1)

H. Ogihara, Y. Hamamoto, Y. Fujita, A. Goto, J. Nishikawa, and I. Sakaida, “Development of a Gastric CancerDiagnostic Support System with a Pattern Recognition Method Using a Hyperspectral Camera,” J. Sens. 2016, 1–6 (2016).
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Machine Learning (1)

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Microsc. Res. Tech. (1)

J. Gil, H. Wu, and B. Y. Wang, “Image Analysis and Morphometry in the Diagnosis of Breast Cancer,” Microsc. Res. Tech. 59(2), 109–118 (2002).
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N. Engl. J. Med. (1)

M. Hidalgo, “Pancreatic cancer,” N. Engl. J. Med. 362(17), 1605–1617 (2010).
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NeuroImage (1)

M. Esterman, B. J. Tamber-Rosenau, Y. Chiu, and S. Yantis, “Avoiding non-independence in fMRI data analysis: Leave one subject out,” NeuroImage 50(2), 572–576 (2010).
[Crossref]

Opt. Rev. (1)

T. Abe, Y. Murakami, M. Yamaguchi, N. Ohyama, and Y. Yagi, “Color Correction of Pathological Images Based on Dye Amount Quantification,” Opt. Rev. 12(4), 293–300 (2005).
[Crossref]

Phys. Med. Biol. (1)

A. J. Sims, M. K. Bennett, and A. Murray, “Image analysis can be used to detect spatial changes in the histopathology of pancreatic tumours,” Phys. Med. Biol. 48(13), N183–N191 (2003).
[Crossref]

Proc. SPIE (1)

Y. Takara, N. Manago, H. Saito, Y. Mabuchi, A. Kondoh, T. Fujimori, F. Ando, M. Suzuki, and H. Kuze, “Remote sensing applications with NH hyperspectral portable video camera,” Proc. SPIE 8527, 85271G (2012).
[Crossref]

Science (1)

A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging Spectrometry for Earth Remote Sensing,” Science 228(4704), 1147–1153 (1985).
[Crossref]

Sensors (1)

B. Mortazavi, E. Nemati, K. VanderWall, H. G. Flores-Rodriguez, J. Y. Jacinta Cai, J. Lucier, A. Naeim, and M. Sarrafzadeh, “Can Smartwatches Replace Smartphones for Posture Tracking?” Sensors 15(10), 26783–26800 (2015).
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P. E. Debevec and J. Malik, “Recovering High Dynamic Range Radiance Maps from Photographs,” Proc. of SIGGRAPH ‘97 (Special issue of ACM Transactions on Graphics), (1997).

B. Scholkopf and A. Smola, “Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond, Adaptive Computation and Machine Learning,” The MIT Press, Cambridge, MA: 2002.

J. Lou, M. Zhou, Q. Li, C. Yuan, and H. Liu, “An Automatic Red Blood Cell Counting Method Based on Spectral Images,” Image and Signal Processing 2016, 1391–1396 (2016).

I. Lihacova, A. Derjabo, and J. Spigulis, “A multispectral imaging approach for diagnostics of skin pathologies,” in Clinical and Biomedical Spectroscopy and Imaging III, V. Deckert and N. Ramanujam, eds., Vol. 8798 of SPIE Proceeding, Optical Society of America (2013).

S. Naik, S. Doyle, S. Agner, A. Madabhushi, M. Feldman, and J. Tomaszewski, “Automated Gland Segmentation and Gleason Grading of Prostate histology by Integrating Low-, High-level and Domain Specific Information,” Workshop on Microscopic Image Analysis with Applications in Biology Piscataway (2007).

M. Ishikawa, N. Kobayashi, H. Komagata, K. Shinoda, M. Yamaguchi, T. Abe, A. Hashiguchi, and M. Sakamoto, “An accurate method of extracting fat droplets in liver images for quantitative evaluation,” Proc. SPIE 9420, Medical Imaging 2015: Digital Pathology, 94200Y (2015).

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Figures (17)

Fig. 1.
Fig. 1. Tissue microarray. Diagnosed pathological slides with the cancerous, non-cancerous, and cancerous tissue in the experiment surrounded by red, green, and orange boxes, respectively.
Fig. 2.
Fig. 2. HS image acquisition system.
Fig. 3.
Fig. 3. Processing framework of HS analysis of pathological slides based on staining.
Fig. 4.
Fig. 4. Actual scan path used to image pathological slides.
Fig. 5.
Fig. 5. Selection images of RGB representations of HSI. Images #1–6 are cancerous tissues, while image #7–12 are non-cancerous tissues.
Fig. 6.
Fig. 6. HDR image with strong absorption. (a) Original image. (b) Long exposure image. (c) Short exposure image. (d) Long exposure (red pixels) and short exposure (green pixels). The red arrow shows the strong absorption pixels.
Fig. 7.
Fig. 7. Waveform for long exposure (orange) and short exposure (blue) with strong absorption pixel.
Fig. 8.
Fig. 8. Transmission of light from the microscope light source through the filter of microscope.
Fig. 9.
Fig. 9. Detection of nuclei. (a) RGB representations of HSI of non-cancerous tissue and (b) manual detection of nuclei of non-cancerous tissue (green pixels). (c) RGB representations of HSI of cancer tissue and (d) manual detection of nuclei of cancer tissue (green pixels).
Fig. 10.
Fig. 10. Absorbance of eosin (blue), hematoxylin (orange), red blood cells (yellow), nuclei (light blue), cytoplasm (green), and re-estimation of the spectrum (purple).
Fig. 11.
Fig. 11. RGB image reconstructed from the HS image of the single stained specimen. (a) Reconstructed RGB images of hematoxylin stained specimens (b) Reconstructed RGB images of eosin stained specimens. The red arrow denotes sampling pixels of the average of waveforms.
Fig. 12.
Fig. 12. Representative images for the estimation of the dye amount. (a) RGB representations of HSI. (b) Dye amount image for hematoxylin. (c) Dye amount image for eosin.
Fig. 13.
Fig. 13. Difference value between absorbance and re-estimation of absorbance
Fig. 14.
Fig. 14. Transmittance (blue), absorbance (red) and REH (orange) spectra of nuclei of non-cancerous tissue.
Fig. 15.
Fig. 15. Accuracy of C-HSI and HAPSS the threshold value via eosin, REH and hematoxylin staining. Accuracy of C-HSI the threshold value via hematoxylin (blue), HAPSS the threshold value via hematoxylin (orange), C-HSI the threshold value via REH (yellow), HAPSS the threshold value via REH (purple), C-HSI the threshold value via eosin (green) and HAPSS the threshold value via eosin (light blue).
Fig. 16.
Fig. 16. Bar graph of classification accuracy obtained using the support vector machine and random forest classifiers for cancerous and non-cancerous tissue.
Fig. 17.
Fig. 17. Pseudocolor images of classification result of dark pixels corresponding to cancerous tissue (orange), bright pixels corresponding to cancerous tissue (yellow), dark pixels corresponding to non-cancerous tissue (blue) and bright pixels corresponding to non-cancerous tissue (light blue) by SVM.

Tables (3)

Tables Icon

Table 1. Number of manually detected nuclei and pixels

Tables Icon

Table 2. Classification results via the support vector machine and random forest for cancerous and non-cancerous tissue

Tables Icon

Table 3. Classification accuracy of support vector machine and random forest for cancerous and non-cancerous tissue of each subject

Equations (11)

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

L E = ( R a w L R a w D ) ( R a w W R a w D ) E x p W E x p L .
S E = ( R a w S R a w D ) ( R a w W R a w D ) E x p W E x p S .
H S I H D R ( x , y ) = { L E ( x , y ) max 1 b 151 R a w L ( x , y , b ) > 3800 , S E ( x , y ) max 1 b 151 R a w L ( x , y , b ) 3800.
a = Xc + n ,
a = log ( T ) .
X + = ( X T X ) 1 X T .
n = a ( X c ¯ ) ,
c ¯ = X + a .
A c c u r a c y = T P + T N T P + F P + F N + T N ,
S e n s i t i v i t y = T P T P + F N ,
S p e c i f i c i t y = F P T N + F P .

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