Abstract

In order to explore the application of hyperspectral technology in the pathological diagnosis of tumor tissue, we used microscopic hyperspectral imaging technology to establish a hyperspectral database of 30 patients with gastric cancer. Based on the difference in spectral-spatial features between gastric cancer tissue and normal tissue in the wavelength of 410-910 nm, we propose a deep-learning model-based analysis method for gastric cancer tissue. The microscopic hyperspectral feature and individual difference of gastric tissue, spatial-spectral joint feature and medical contact are studied. The experimental results show that the classification accuracy of proposed model for cancerous and normal gastric tissue is 97.57%, the sensitivity and specificity of gastric cancer tissue are 97.19% and 97.96% respectively. Compared with the shallow learning method, CNN can fully extract the deep spectral-spatial features of tumor tissue. The combination of deep learning model and micro-spectral analysis provides new ideas for the research of medical pathology.

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

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References

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2017 (3)

A. Dong, J. Li, B. Zhang, and M. Liang, “Hyperspectral Image Classification Algorithm Based on Spectral Clustering and Sparse Representation,” Acta Optica Sinica. 37(8), 0828005 (2017).
[Crossref]

L. Wei, G. Wu, Z. Fan, and D. Qian, “Hyperspectral Image Classification Using Deep Pixel-Pair Features,” IEEE Trans. Geosci. Remote Sens. 55(2), 844–853 (2017).
[Crossref]

L. Ying, H. Zhang, and S. Qiang, “Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network,” Remote Sens. 9(1), 67–69 (2017).
[Crossref]

2016 (4)

Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks,” IEEE Trans. Geosci. Remote Sens. 54(10), 6232–6251 (2016).
[Crossref]

H. Hou, Z. Fang, Y. Zhang, M. Dong, and Y. Liu, “Simulation and in vivo Experimental Study on Noninvasive Spectral Detection of Skin Cholesterol,” Chin. J. Laser 43(9), 0907001 (2016).
[Crossref]

H. Liang and Q. Li, “Hyperspectral imagery classification using sparse representations of convolutional neural network features,” Remote Sens. 8(2), 99 (2016).
[Crossref]

J. A. Sanz, A. M. Fernandes, E. Barrenechea, S. Silva, V. Santos, N. Gonçalves, D. Paternain, A. Jurio, and P. Melo-Pinto, “Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms,” J. Food Eng. 174, 92–100 (2016).
[Crossref]

2015 (5)

Q. Li, C. Li, H. Liu, Z. Mei, Y. Wang, and F. Guo, “Skin cells segmentation algorithm based on spectral angle and distance score,” Opt. Laser Technol. 74, 79–86 (2015).
[Crossref]

H. Liu, W. Gu, Q. Li, Y. Wang, Z. Chen, and X. Qin, “Nerve Classification with Hyperspectral Imaging Technology,” Spectrosc. Spect. Anal. 35(1), 38–43 (2015).
[Crossref]

J. Ferlay, I. Soerjomataram, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D. M. Parkin, D. Forman, and F. Bray, “Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012,” Int. J. Cancer 136(5), E359–E386 (2015).
[Crossref]

J. Yue, W. Zhao, S. Mao, and H. Liu, “Spectral–spatial classification of hyperspectral images using deep convolutional neural networks,” Remote Sensing Letters. 6(6), 468–477 (2015).
[Crossref]

S. Zhu, K. Su, Y. Liu, H. Yin, Z. Li, F. Huang, Z. Chen, W. Chen, G. Zhang, and Y. Chen, “Identification of cancerous gastric cells based on common features extracted from hyperspectral microscopic images,” Biomed. Opt. Express 6(4), 1135–1145 (2015).
[Crossref]

2014 (2)

B. Luo and L. Zhang, “Robust Autodual Morphological Profiles for the Classification of High-Resolution Satellite Images,” IEEE Trans. Geosci. Remote Sens. 52(2), 1451–1462 (2014).
[Crossref]

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

2013 (1)

D. Wu and D. Sun, “Color measurements by computer vision for food quality control – A review,” Trends Food Sci. Technol. 29(1), 5–20 (2013).
[Crossref]

2012 (5)

M. Kamruzzaman, D. Barbin, G. Elmasry, D. W. Sun, and P. Allen, “Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat,” Innovative Food Sci. Emerging Technol. 16(1), 316–325 (2012).
[Crossref]

H. Akbari, L. V. Halig, D. M. Schuster, O. Adeboye, M. Viraj, P. T. Nieh, G. Z. Chen, and F. Baowei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 0760051 (2012).
[Crossref]

A. O. Gerstner, W. Laffers, F. Bootz, D. L. Farkas, R. Martin, J. Bendix, and B. Thies, “Hyperspectral imaging of mucosal surfaces in patients,” J. Biophoton. 5(3), 255–262 (2012).
[Crossref]

Y. Suzuki, H. Okamoto, M. Takahashi, T. Kataoka, and Y. Shibata, “Mapping the spatial distribution of botanical composition and herbage mass in pastures using hyperspectral imaging,” Grassland Science. 58(1), 1–7 (2012).
[Crossref]

H. Akbari, L. V. Halig, H. Zhang, D. Wang, Z. G. Chen, and B. Fei, “Detection of Cancer Metastasis Using a Novel Macroscopic Hyperspectral Method,” Proc. SPIE 8317, 831711 (2012).
[Crossref]

2006 (1)

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313(5786), 504–507 (2006).
[Crossref]

2004 (2)

D. C. Kellicut, J. M. Weiswasser, S. Arora, E. Freeman, R. A. Lew, C. Shuman, J. R. Mansfield, and A. N. Sidawy, “Emerging Technology: Hyperspectral Imaging,” Perspect. Vasc. Surg. 16(1), 53–57 (2004).
[Crossref]

T. Vodinh, D. L. Stokes, M. B. Wabuyele, M. E. Martin, J. M. Song, R. Jagannathan, E. Michaud, R. J. Lee, and X. Pan, “A hyperspectral imaging system for in vivo optical diagnostics. Hyperspectral imaging basic principles, instrumental systems, and applications of biomedical interest,” IEEE Eng. Med. Biol. Mag. 23(5), 40–49 (2004).
[Crossref]

1998 (1)

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Adeboye, O.

H. Akbari, L. V. Halig, D. M. Schuster, O. Adeboye, M. Viraj, P. T. Nieh, G. Z. Chen, and F. Baowei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 0760051 (2012).
[Crossref]

Akbari, H.

H. Akbari, L. V. Halig, D. M. Schuster, O. Adeboye, M. Viraj, P. T. Nieh, G. Z. Chen, and F. Baowei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 0760051 (2012).
[Crossref]

H. Akbari, L. V. Halig, H. Zhang, D. Wang, Z. G. Chen, and B. Fei, “Detection of Cancer Metastasis Using a Novel Macroscopic Hyperspectral Method,” Proc. SPIE 8317, 831711 (2012).
[Crossref]

Allen, P.

M. Kamruzzaman, D. Barbin, G. Elmasry, D. W. Sun, and P. Allen, “Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat,” Innovative Food Sci. Emerging Technol. 16(1), 316–325 (2012).
[Crossref]

Arora, S.

D. C. Kellicut, J. M. Weiswasser, S. Arora, E. Freeman, R. A. Lew, C. Shuman, J. R. Mansfield, and A. N. Sidawy, “Emerging Technology: Hyperspectral Imaging,” Perspect. Vasc. Surg. 16(1), 53–57 (2004).
[Crossref]

Baker, M. J.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Baowei, F.

H. Akbari, L. V. Halig, D. M. Schuster, O. Adeboye, M. Viraj, P. T. Nieh, G. Z. Chen, and F. Baowei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 0760051 (2012).
[Crossref]

Barbin, D.

M. Kamruzzaman, D. Barbin, G. Elmasry, D. W. Sun, and P. Allen, “Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat,” Innovative Food Sci. Emerging Technol. 16(1), 316–325 (2012).
[Crossref]

Barrenechea, E.

J. A. Sanz, A. M. Fernandes, E. Barrenechea, S. Silva, V. Santos, N. Gonçalves, D. Paternain, A. Jurio, and P. Melo-Pinto, “Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms,” J. Food Eng. 174, 92–100 (2016).
[Crossref]

Bassan, P.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Bendix, J.

A. O. Gerstner, W. Laffers, F. Bootz, D. L. Farkas, R. Martin, J. Bendix, and B. Thies, “Hyperspectral imaging of mucosal surfaces in patients,” J. Biophoton. 5(3), 255–262 (2012).
[Crossref]

Bengio, Y.

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Bhargava, R.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Bootz, F.

A. O. Gerstner, W. Laffers, F. Bootz, D. L. Farkas, R. Martin, J. Bendix, and B. Thies, “Hyperspectral imaging of mucosal surfaces in patients,” J. Biophoton. 5(3), 255–262 (2012).
[Crossref]

Bottou, L.

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Bourdev, L.

D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in Proceedings of the IEEE international conference on computer vision (2015), pp. 4489–4497.

Bray, F.

J. Ferlay, I. Soerjomataram, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D. M. Parkin, D. Forman, and F. Bray, “Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012,” Int. J. Cancer 136(5), E359–E386 (2015).
[Crossref]

Butler, H. J.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Callico, G. M.

S. Ortega, G. M. Callico, M. L. Plaza, R. Camacho, H. Fabelo, and R. Sarmiento, “Hyperspectral database of pathological in-vitro human brain samples to detect carcinogenic tissues,” in IEEE International Symposium on Biomedical Imaging (2016), pp. 369–372.

Camacho, R.

S. Ortega, G. M. Callico, M. L. Plaza, R. Camacho, H. Fabelo, and R. Sarmiento, “Hyperspectral database of pathological in-vitro human brain samples to detect carcinogenic tissues,” in IEEE International Symposium on Biomedical Imaging (2016), pp. 369–372.

Chen, G. Z.

H. Akbari, L. V. Halig, D. M. Schuster, O. Adeboye, M. Viraj, P. T. Nieh, G. Z. Chen, and F. Baowei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 0760051 (2012).
[Crossref]

Chen, W.

Chen, Y.

Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks,” IEEE Trans. Geosci. Remote Sens. 54(10), 6232–6251 (2016).
[Crossref]

S. Zhu, K. Su, Y. Liu, H. Yin, Z. Li, F. Huang, Z. Chen, W. Chen, G. Zhang, and Y. Chen, “Identification of cancerous gastric cells based on common features extracted from hyperspectral microscopic images,” Biomed. Opt. Express 6(4), 1135–1145 (2015).
[Crossref]

Chen, Z.

Chen, Z. G.

H. Akbari, L. V. Halig, H. Zhang, D. Wang, Z. G. Chen, and B. Fei, “Detection of Cancer Metastasis Using a Novel Macroscopic Hyperspectral Method,” Proc. SPIE 8317, 831711 (2012).
[Crossref]

Dikshit, R.

J. Ferlay, I. Soerjomataram, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D. M. Parkin, D. Forman, and F. Bray, “Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012,” Int. J. Cancer 136(5), E359–E386 (2015).
[Crossref]

Dong, A.

A. Dong, J. Li, B. Zhang, and M. Liang, “Hyperspectral Image Classification Algorithm Based on Spectral Clustering and Sparse Representation,” Acta Optica Sinica. 37(8), 0828005 (2017).
[Crossref]

Dong, H.

L. Lin, H. Dong, and X. Song, “DBN-based Classification of Spatial-spectral Hyperspectral Data,” in Advances in Intelligent Information Hiding and Multimedia Signal Processing (Springer, 2017), pp. 53–60.

Dong, M.

H. Hou, Z. Fang, Y. Zhang, M. Dong, and Y. Liu, “Simulation and in vivo Experimental Study on Noninvasive Spectral Detection of Skin Cholesterol,” Chin. J. Laser 43(9), 0907001 (2016).
[Crossref]

Dorling, K. M.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Doulamis, A.

K. Makantasis, K. Karantzalos, A. Doulamis, and N. Doulamis, “Deep supervised learning for hyperspectral data classification through convolutional neural networks,” in 2015 IEEE International Geoscience and Remote Sensing Symposium (2015), pp. 4959–4962.

Doulamis, N.

K. Makantasis, K. Karantzalos, A. Doulamis, and N. Doulamis, “Deep supervised learning for hyperspectral data classification through convolutional neural networks,” in 2015 IEEE International Geoscience and Remote Sensing Symposium (2015), pp. 4959–4962.

Elmasry, G.

M. Kamruzzaman, D. Barbin, G. Elmasry, D. W. Sun, and P. Allen, “Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat,” Innovative Food Sci. Emerging Technol. 16(1), 316–325 (2012).
[Crossref]

Eser, S.

J. Ferlay, I. Soerjomataram, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D. M. Parkin, D. Forman, and F. Bray, “Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012,” Int. J. Cancer 136(5), E359–E386 (2015).
[Crossref]

Fabelo, H.

S. Ortega, G. M. Callico, M. L. Plaza, R. Camacho, H. Fabelo, and R. Sarmiento, “Hyperspectral database of pathological in-vitro human brain samples to detect carcinogenic tissues,” in IEEE International Symposium on Biomedical Imaging (2016), pp. 369–372.

Fan, Z.

L. Wei, G. Wu, Z. Fan, and D. Qian, “Hyperspectral Image Classification Using Deep Pixel-Pair Features,” IEEE Trans. Geosci. Remote Sens. 55(2), 844–853 (2017).
[Crossref]

Fang, Z.

H. Hou, Z. Fang, Y. Zhang, M. Dong, and Y. Liu, “Simulation and in vivo Experimental Study on Noninvasive Spectral Detection of Skin Cholesterol,” Chin. J. Laser 43(9), 0907001 (2016).
[Crossref]

Farkas, D. L.

A. O. Gerstner, W. Laffers, F. Bootz, D. L. Farkas, R. Martin, J. Bendix, and B. Thies, “Hyperspectral imaging of mucosal surfaces in patients,” J. Biophoton. 5(3), 255–262 (2012).
[Crossref]

Fei, B.

H. Akbari, L. V. Halig, H. Zhang, D. Wang, Z. G. Chen, and B. Fei, “Detection of Cancer Metastasis Using a Novel Macroscopic Hyperspectral Method,” Proc. SPIE 8317, 831711 (2012).
[Crossref]

Fergus, R.

D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in Proceedings of the IEEE international conference on computer vision (2015), pp. 4489–4497.

Ferlay, J.

J. Ferlay, I. Soerjomataram, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D. M. Parkin, D. Forman, and F. Bray, “Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012,” Int. J. Cancer 136(5), E359–E386 (2015).
[Crossref]

Fernandes, A. M.

J. A. Sanz, A. M. Fernandes, E. Barrenechea, S. Silva, V. Santos, N. Gonçalves, D. Paternain, A. Jurio, and P. Melo-Pinto, “Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms,” J. Food Eng. 174, 92–100 (2016).
[Crossref]

Fielden, P. R.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Fogarty, S. W.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Forman, D.

J. Ferlay, I. Soerjomataram, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D. M. Parkin, D. Forman, and F. Bray, “Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012,” Int. J. Cancer 136(5), E359–E386 (2015).
[Crossref]

Freeman, E.

D. C. Kellicut, J. M. Weiswasser, S. Arora, E. Freeman, R. A. Lew, C. Shuman, J. R. Mansfield, and A. N. Sidawy, “Emerging Technology: Hyperspectral Imaging,” Perspect. Vasc. Surg. 16(1), 53–57 (2004).
[Crossref]

Fullwood, N. J.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Gardner, P.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Gerstner, A. O.

A. O. Gerstner, W. Laffers, F. Bootz, D. L. Farkas, R. Martin, J. Bendix, and B. Thies, “Hyperspectral imaging of mucosal surfaces in patients,” J. Biophoton. 5(3), 255–262 (2012).
[Crossref]

Ghamisi, P.

Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks,” IEEE Trans. Geosci. Remote Sens. 54(10), 6232–6251 (2016).
[Crossref]

Gonçalves, N.

J. A. Sanz, A. M. Fernandes, E. Barrenechea, S. Silva, V. Santos, N. Gonçalves, D. Paternain, A. Jurio, and P. Melo-Pinto, “Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms,” J. Food Eng. 174, 92–100 (2016).
[Crossref]

Gu, W.

H. Liu, W. Gu, Q. Li, Y. Wang, Z. Chen, and X. Qin, “Nerve Classification with Hyperspectral Imaging Technology,” Spectrosc. Spect. Anal. 35(1), 38–43 (2015).
[Crossref]

Guo, F.

Q. Li, C. Li, H. Liu, Z. Mei, Y. Wang, and F. Guo, “Skin cells segmentation algorithm based on spectral angle and distance score,” Opt. Laser Technol. 74, 79–86 (2015).
[Crossref]

Haffner, P.

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Halig, L. V.

H. Akbari, L. V. Halig, H. Zhang, D. Wang, Z. G. Chen, and B. Fei, “Detection of Cancer Metastasis Using a Novel Macroscopic Hyperspectral Method,” Proc. SPIE 8317, 831711 (2012).
[Crossref]

H. Akbari, L. V. Halig, D. M. Schuster, O. Adeboye, M. Viraj, P. T. Nieh, G. Z. Chen, and F. Baowei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 0760051 (2012).
[Crossref]

Heys, K. A.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Hinton, G. E.

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313(5786), 504–507 (2006).
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A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems (2012), pp. 1097–1105.

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H. Hou, Z. Fang, Y. Zhang, M. Dong, and Y. Liu, “Simulation and in vivo Experimental Study on Noninvasive Spectral Detection of Skin Cholesterol,” Chin. J. Laser 43(9), 0907001 (2016).
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Huang, F.

Hughes, C.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
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Jagannathan, R.

T. Vodinh, D. L. Stokes, M. B. Wabuyele, M. E. Martin, J. M. Song, R. Jagannathan, E. Michaud, R. J. Lee, and X. Pan, “A hyperspectral imaging system for in vivo optical diagnostics. Hyperspectral imaging basic principles, instrumental systems, and applications of biomedical interest,” IEEE Eng. Med. Biol. Mag. 23(5), 40–49 (2004).
[Crossref]

Jia, X.

Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks,” IEEE Trans. Geosci. Remote Sens. 54(10), 6232–6251 (2016).
[Crossref]

Jiang, H.

Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks,” IEEE Trans. Geosci. Remote Sens. 54(10), 6232–6251 (2016).
[Crossref]

Jurio, A.

J. A. Sanz, A. M. Fernandes, E. Barrenechea, S. Silva, V. Santos, N. Gonçalves, D. Paternain, A. Jurio, and P. Melo-Pinto, “Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms,” J. Food Eng. 174, 92–100 (2016).
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Kamruzzaman, M.

M. Kamruzzaman, D. Barbin, G. Elmasry, D. W. Sun, and P. Allen, “Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat,” Innovative Food Sci. Emerging Technol. 16(1), 316–325 (2012).
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K. Makantasis, K. Karantzalos, A. Doulamis, and N. Doulamis, “Deep supervised learning for hyperspectral data classification through convolutional neural networks,” in 2015 IEEE International Geoscience and Remote Sensing Symposium (2015), pp. 4959–4962.

Kataoka, T.

Y. Suzuki, H. Okamoto, M. Takahashi, T. Kataoka, and Y. Shibata, “Mapping the spatial distribution of botanical composition and herbage mass in pastures using hyperspectral imaging,” Grassland Science. 58(1), 1–7 (2012).
[Crossref]

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D. C. Kellicut, J. M. Weiswasser, S. Arora, E. Freeman, R. A. Lew, C. Shuman, J. R. Mansfield, and A. N. Sidawy, “Emerging Technology: Hyperspectral Imaging,” Perspect. Vasc. Surg. 16(1), 53–57 (2004).
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A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems (2012), pp. 1097–1105.

Laffers, W.

A. O. Gerstner, W. Laffers, F. Bootz, D. L. Farkas, R. Martin, J. Bendix, and B. Thies, “Hyperspectral imaging of mucosal surfaces in patients,” J. Biophoton. 5(3), 255–262 (2012).
[Crossref]

Lasch, P.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

LeCun, Y.

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Lee, R. J.

T. Vodinh, D. L. Stokes, M. B. Wabuyele, M. E. Martin, J. M. Song, R. Jagannathan, E. Michaud, R. J. Lee, and X. Pan, “A hyperspectral imaging system for in vivo optical diagnostics. Hyperspectral imaging basic principles, instrumental systems, and applications of biomedical interest,” IEEE Eng. Med. Biol. Mag. 23(5), 40–49 (2004).
[Crossref]

Lew, R. A.

D. C. Kellicut, J. M. Weiswasser, S. Arora, E. Freeman, R. A. Lew, C. Shuman, J. R. Mansfield, and A. N. Sidawy, “Emerging Technology: Hyperspectral Imaging,” Perspect. Vasc. Surg. 16(1), 53–57 (2004).
[Crossref]

Li, C.

Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks,” IEEE Trans. Geosci. Remote Sens. 54(10), 6232–6251 (2016).
[Crossref]

Q. Li, C. Li, H. Liu, Z. Mei, Y. Wang, and F. Guo, “Skin cells segmentation algorithm based on spectral angle and distance score,” Opt. Laser Technol. 74, 79–86 (2015).
[Crossref]

Li, J.

A. Dong, J. Li, B. Zhang, and M. Liang, “Hyperspectral Image Classification Algorithm Based on Spectral Clustering and Sparse Representation,” Acta Optica Sinica. 37(8), 0828005 (2017).
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Li, Q.

H. Liang and Q. Li, “Hyperspectral imagery classification using sparse representations of convolutional neural network features,” Remote Sens. 8(2), 99 (2016).
[Crossref]

Q. Li, C. Li, H. Liu, Z. Mei, Y. Wang, and F. Guo, “Skin cells segmentation algorithm based on spectral angle and distance score,” Opt. Laser Technol. 74, 79–86 (2015).
[Crossref]

H. Liu, W. Gu, Q. Li, Y. Wang, Z. Chen, and X. Qin, “Nerve Classification with Hyperspectral Imaging Technology,” Spectrosc. Spect. Anal. 35(1), 38–43 (2015).
[Crossref]

Li, Z.

Liang, H.

H. Liang and Q. Li, “Hyperspectral imagery classification using sparse representations of convolutional neural network features,” Remote Sens. 8(2), 99 (2016).
[Crossref]

Liang, M.

A. Dong, J. Li, B. Zhang, and M. Liang, “Hyperspectral Image Classification Algorithm Based on Spectral Clustering and Sparse Representation,” Acta Optica Sinica. 37(8), 0828005 (2017).
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Lin, L.

L. Lin, H. Dong, and X. Song, “DBN-based Classification of Spatial-spectral Hyperspectral Data,” in Advances in Intelligent Information Hiding and Multimedia Signal Processing (Springer, 2017), pp. 53–60.

Liu, H.

H. Liu, W. Gu, Q. Li, Y. Wang, Z. Chen, and X. Qin, “Nerve Classification with Hyperspectral Imaging Technology,” Spectrosc. Spect. Anal. 35(1), 38–43 (2015).
[Crossref]

Q. Li, C. Li, H. Liu, Z. Mei, Y. Wang, and F. Guo, “Skin cells segmentation algorithm based on spectral angle and distance score,” Opt. Laser Technol. 74, 79–86 (2015).
[Crossref]

J. Yue, W. Zhao, S. Mao, and H. Liu, “Spectral–spatial classification of hyperspectral images using deep convolutional neural networks,” Remote Sensing Letters. 6(6), 468–477 (2015).
[Crossref]

Liu, Y.

H. Hou, Z. Fang, Y. Zhang, M. Dong, and Y. Liu, “Simulation and in vivo Experimental Study on Noninvasive Spectral Detection of Skin Cholesterol,” Chin. J. Laser 43(9), 0907001 (2016).
[Crossref]

S. Zhu, K. Su, Y. Liu, H. Yin, Z. Li, F. Huang, Z. Chen, W. Chen, G. Zhang, and Y. Chen, “Identification of cancerous gastric cells based on common features extracted from hyperspectral microscopic images,” Biomed. Opt. Express 6(4), 1135–1145 (2015).
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Luo, B.

B. Luo and L. Zhang, “Robust Autodual Morphological Profiles for the Classification of High-Resolution Satellite Images,” IEEE Trans. Geosci. Remote Sens. 52(2), 1451–1462 (2014).
[Crossref]

Makantasis, K.

K. Makantasis, K. Karantzalos, A. Doulamis, and N. Doulamis, “Deep supervised learning for hyperspectral data classification through convolutional neural networks,” in 2015 IEEE International Geoscience and Remote Sensing Symposium (2015), pp. 4959–4962.

Mansfield, J. R.

D. C. Kellicut, J. M. Weiswasser, S. Arora, E. Freeman, R. A. Lew, C. Shuman, J. R. Mansfield, and A. N. Sidawy, “Emerging Technology: Hyperspectral Imaging,” Perspect. Vasc. Surg. 16(1), 53–57 (2004).
[Crossref]

Mao, S.

J. Yue, W. Zhao, S. Mao, and H. Liu, “Spectral–spatial classification of hyperspectral images using deep convolutional neural networks,” Remote Sensing Letters. 6(6), 468–477 (2015).
[Crossref]

Martin, F. L.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Martin, M. E.

T. Vodinh, D. L. Stokes, M. B. Wabuyele, M. E. Martin, J. M. Song, R. Jagannathan, E. Michaud, R. J. Lee, and X. Pan, “A hyperspectral imaging system for in vivo optical diagnostics. Hyperspectral imaging basic principles, instrumental systems, and applications of biomedical interest,” IEEE Eng. Med. Biol. Mag. 23(5), 40–49 (2004).
[Crossref]

Martin, R.

A. O. Gerstner, W. Laffers, F. Bootz, D. L. Farkas, R. Martin, J. Bendix, and B. Thies, “Hyperspectral imaging of mucosal surfaces in patients,” J. Biophoton. 5(3), 255–262 (2012).
[Crossref]

Martin-Hirsch, P. L.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Mathers, C.

J. Ferlay, I. Soerjomataram, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D. M. Parkin, D. Forman, and F. Bray, “Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012,” Int. J. Cancer 136(5), E359–E386 (2015).
[Crossref]

Mei, Z.

Q. Li, C. Li, H. Liu, Z. Mei, Y. Wang, and F. Guo, “Skin cells segmentation algorithm based on spectral angle and distance score,” Opt. Laser Technol. 74, 79–86 (2015).
[Crossref]

Melo-Pinto, P.

J. A. Sanz, A. M. Fernandes, E. Barrenechea, S. Silva, V. Santos, N. Gonçalves, D. Paternain, A. Jurio, and P. Melo-Pinto, “Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms,” J. Food Eng. 174, 92–100 (2016).
[Crossref]

Michaud, E.

T. Vodinh, D. L. Stokes, M. B. Wabuyele, M. E. Martin, J. M. Song, R. Jagannathan, E. Michaud, R. J. Lee, and X. Pan, “A hyperspectral imaging system for in vivo optical diagnostics. Hyperspectral imaging basic principles, instrumental systems, and applications of biomedical interest,” IEEE Eng. Med. Biol. Mag. 23(5), 40–49 (2004).
[Crossref]

Nieh, P. T.

H. Akbari, L. V. Halig, D. M. Schuster, O. Adeboye, M. Viraj, P. T. Nieh, G. Z. Chen, and F. Baowei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 0760051 (2012).
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Obinaju, B.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Okamoto, H.

Y. Suzuki, H. Okamoto, M. Takahashi, T. Kataoka, and Y. Shibata, “Mapping the spatial distribution of botanical composition and herbage mass in pastures using hyperspectral imaging,” Grassland Science. 58(1), 1–7 (2012).
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Ortega, S.

S. Ortega, G. M. Callico, M. L. Plaza, R. Camacho, H. Fabelo, and R. Sarmiento, “Hyperspectral database of pathological in-vitro human brain samples to detect carcinogenic tissues,” in IEEE International Symposium on Biomedical Imaging (2016), pp. 369–372.

Paluri, M.

D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in Proceedings of the IEEE international conference on computer vision (2015), pp. 4489–4497.

Pan, X.

T. Vodinh, D. L. Stokes, M. B. Wabuyele, M. E. Martin, J. M. Song, R. Jagannathan, E. Michaud, R. J. Lee, and X. Pan, “A hyperspectral imaging system for in vivo optical diagnostics. Hyperspectral imaging basic principles, instrumental systems, and applications of biomedical interest,” IEEE Eng. Med. Biol. Mag. 23(5), 40–49 (2004).
[Crossref]

Parkin, D. M.

J. Ferlay, I. Soerjomataram, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D. M. Parkin, D. Forman, and F. Bray, “Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012,” Int. J. Cancer 136(5), E359–E386 (2015).
[Crossref]

Paternain, D.

J. A. Sanz, A. M. Fernandes, E. Barrenechea, S. Silva, V. Santos, N. Gonçalves, D. Paternain, A. Jurio, and P. Melo-Pinto, “Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms,” J. Food Eng. 174, 92–100 (2016).
[Crossref]

Plaza, M. L.

S. Ortega, G. M. Callico, M. L. Plaza, R. Camacho, H. Fabelo, and R. Sarmiento, “Hyperspectral database of pathological in-vitro human brain samples to detect carcinogenic tissues,” in IEEE International Symposium on Biomedical Imaging (2016), pp. 369–372.

Qian, D.

L. Wei, G. Wu, Z. Fan, and D. Qian, “Hyperspectral Image Classification Using Deep Pixel-Pair Features,” IEEE Trans. Geosci. Remote Sens. 55(2), 844–853 (2017).
[Crossref]

Qiang, S.

L. Ying, H. Zhang, and S. Qiang, “Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network,” Remote Sens. 9(1), 67–69 (2017).
[Crossref]

Qin, X.

H. Liu, W. Gu, Q. Li, Y. Wang, Z. Chen, and X. Qin, “Nerve Classification with Hyperspectral Imaging Technology,” Spectrosc. Spect. Anal. 35(1), 38–43 (2015).
[Crossref]

Rebelo, M.

J. Ferlay, I. Soerjomataram, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D. M. Parkin, D. Forman, and F. Bray, “Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012,” Int. J. Cancer 136(5), E359–E386 (2015).
[Crossref]

Salakhutdinov, R. R.

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313(5786), 504–507 (2006).
[Crossref]

Santos, V.

J. A. Sanz, A. M. Fernandes, E. Barrenechea, S. Silva, V. Santos, N. Gonçalves, D. Paternain, A. Jurio, and P. Melo-Pinto, “Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms,” J. Food Eng. 174, 92–100 (2016).
[Crossref]

Sanz, J. A.

J. A. Sanz, A. M. Fernandes, E. Barrenechea, S. Silva, V. Santos, N. Gonçalves, D. Paternain, A. Jurio, and P. Melo-Pinto, “Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms,” J. Food Eng. 174, 92–100 (2016).
[Crossref]

Sarmiento, R.

S. Ortega, G. M. Callico, M. L. Plaza, R. Camacho, H. Fabelo, and R. Sarmiento, “Hyperspectral database of pathological in-vitro human brain samples to detect carcinogenic tissues,” in IEEE International Symposium on Biomedical Imaging (2016), pp. 369–372.

Schuster, D. M.

H. Akbari, L. V. Halig, D. M. Schuster, O. Adeboye, M. Viraj, P. T. Nieh, G. Z. Chen, and F. Baowei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 0760051 (2012).
[Crossref]

Shibata, Y.

Y. Suzuki, H. Okamoto, M. Takahashi, T. Kataoka, and Y. Shibata, “Mapping the spatial distribution of botanical composition and herbage mass in pastures using hyperspectral imaging,” Grassland Science. 58(1), 1–7 (2012).
[Crossref]

Shuman, C.

D. C. Kellicut, J. M. Weiswasser, S. Arora, E. Freeman, R. A. Lew, C. Shuman, J. R. Mansfield, and A. N. Sidawy, “Emerging Technology: Hyperspectral Imaging,” Perspect. Vasc. Surg. 16(1), 53–57 (2004).
[Crossref]

Sidawy, A. N.

D. C. Kellicut, J. M. Weiswasser, S. Arora, E. Freeman, R. A. Lew, C. Shuman, J. R. Mansfield, and A. N. Sidawy, “Emerging Technology: Hyperspectral Imaging,” Perspect. Vasc. Surg. 16(1), 53–57 (2004).
[Crossref]

Silva, S.

J. A. Sanz, A. M. Fernandes, E. Barrenechea, S. Silva, V. Santos, N. Gonçalves, D. Paternain, A. Jurio, and P. Melo-Pinto, “Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms,” J. Food Eng. 174, 92–100 (2016).
[Crossref]

Sockalingum, G. D.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Soerjomataram, I.

J. Ferlay, I. Soerjomataram, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D. M. Parkin, D. Forman, and F. Bray, “Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012,” Int. J. Cancer 136(5), E359–E386 (2015).
[Crossref]

Song, J. M.

T. Vodinh, D. L. Stokes, M. B. Wabuyele, M. E. Martin, J. M. Song, R. Jagannathan, E. Michaud, R. J. Lee, and X. Pan, “A hyperspectral imaging system for in vivo optical diagnostics. Hyperspectral imaging basic principles, instrumental systems, and applications of biomedical interest,” IEEE Eng. Med. Biol. Mag. 23(5), 40–49 (2004).
[Crossref]

Song, X.

L. Lin, H. Dong, and X. Song, “DBN-based Classification of Spatial-spectral Hyperspectral Data,” in Advances in Intelligent Information Hiding and Multimedia Signal Processing (Springer, 2017), pp. 53–60.

Stokes, D. L.

T. Vodinh, D. L. Stokes, M. B. Wabuyele, M. E. Martin, J. M. Song, R. Jagannathan, E. Michaud, R. J. Lee, and X. Pan, “A hyperspectral imaging system for in vivo optical diagnostics. Hyperspectral imaging basic principles, instrumental systems, and applications of biomedical interest,” IEEE Eng. Med. Biol. Mag. 23(5), 40–49 (2004).
[Crossref]

Strong, R. J.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Su, K.

Sulé-Suso, J.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Sun, D.

D. Wu and D. Sun, “Color measurements by computer vision for food quality control – A review,” Trends Food Sci. Technol. 29(1), 5–20 (2013).
[Crossref]

Sun, D. W.

M. Kamruzzaman, D. Barbin, G. Elmasry, D. W. Sun, and P. Allen, “Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat,” Innovative Food Sci. Emerging Technol. 16(1), 316–325 (2012).
[Crossref]

Sutskever, I.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems (2012), pp. 1097–1105.

Suzuki, Y.

Y. Suzuki, H. Okamoto, M. Takahashi, T. Kataoka, and Y. Shibata, “Mapping the spatial distribution of botanical composition and herbage mass in pastures using hyperspectral imaging,” Grassland Science. 58(1), 1–7 (2012).
[Crossref]

Takahashi, M.

Y. Suzuki, H. Okamoto, M. Takahashi, T. Kataoka, and Y. Shibata, “Mapping the spatial distribution of botanical composition and herbage mass in pastures using hyperspectral imaging,” Grassland Science. 58(1), 1–7 (2012).
[Crossref]

Thies, B.

A. O. Gerstner, W. Laffers, F. Bootz, D. L. Farkas, R. Martin, J. Bendix, and B. Thies, “Hyperspectral imaging of mucosal surfaces in patients,” J. Biophoton. 5(3), 255–262 (2012).
[Crossref]

Torresani, L.

D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in Proceedings of the IEEE international conference on computer vision (2015), pp. 4489–4497.

Tran, D.

D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in Proceedings of the IEEE international conference on computer vision (2015), pp. 4489–4497.

Trevisan, J.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Viraj, M.

H. Akbari, L. V. Halig, D. M. Schuster, O. Adeboye, M. Viraj, P. T. Nieh, G. Z. Chen, and F. Baowei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 0760051 (2012).
[Crossref]

Vodinh, T.

T. Vodinh, D. L. Stokes, M. B. Wabuyele, M. E. Martin, J. M. Song, R. Jagannathan, E. Michaud, R. J. Lee, and X. Pan, “A hyperspectral imaging system for in vivo optical diagnostics. Hyperspectral imaging basic principles, instrumental systems, and applications of biomedical interest,” IEEE Eng. Med. Biol. Mag. 23(5), 40–49 (2004).
[Crossref]

Wabuyele, M. B.

T. Vodinh, D. L. Stokes, M. B. Wabuyele, M. E. Martin, J. M. Song, R. Jagannathan, E. Michaud, R. J. Lee, and X. Pan, “A hyperspectral imaging system for in vivo optical diagnostics. Hyperspectral imaging basic principles, instrumental systems, and applications of biomedical interest,” IEEE Eng. Med. Biol. Mag. 23(5), 40–49 (2004).
[Crossref]

Walsh, M. J.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Wang, D.

H. Akbari, L. V. Halig, H. Zhang, D. Wang, Z. G. Chen, and B. Fei, “Detection of Cancer Metastasis Using a Novel Macroscopic Hyperspectral Method,” Proc. SPIE 8317, 831711 (2012).
[Crossref]

Wang, Y.

Q. Li, C. Li, H. Liu, Z. Mei, Y. Wang, and F. Guo, “Skin cells segmentation algorithm based on spectral angle and distance score,” Opt. Laser Technol. 74, 79–86 (2015).
[Crossref]

H. Liu, W. Gu, Q. Li, Y. Wang, Z. Chen, and X. Qin, “Nerve Classification with Hyperspectral Imaging Technology,” Spectrosc. Spect. Anal. 35(1), 38–43 (2015).
[Crossref]

Wei, L.

L. Wei, G. Wu, Z. Fan, and D. Qian, “Hyperspectral Image Classification Using Deep Pixel-Pair Features,” IEEE Trans. Geosci. Remote Sens. 55(2), 844–853 (2017).
[Crossref]

Weiswasser, J. M.

D. C. Kellicut, J. M. Weiswasser, S. Arora, E. Freeman, R. A. Lew, C. Shuman, J. R. Mansfield, and A. N. Sidawy, “Emerging Technology: Hyperspectral Imaging,” Perspect. Vasc. Surg. 16(1), 53–57 (2004).
[Crossref]

Wood, B. R.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Wu, D.

D. Wu and D. Sun, “Color measurements by computer vision for food quality control – A review,” Trends Food Sci. Technol. 29(1), 5–20 (2013).
[Crossref]

Wu, G.

L. Wei, G. Wu, Z. Fan, and D. Qian, “Hyperspectral Image Classification Using Deep Pixel-Pair Features,” IEEE Trans. Geosci. Remote Sens. 55(2), 844–853 (2017).
[Crossref]

Yin, H.

Ying, L.

L. Ying, H. Zhang, and S. Qiang, “Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network,” Remote Sens. 9(1), 67–69 (2017).
[Crossref]

Yue, J.

J. Yue, W. Zhao, S. Mao, and H. Liu, “Spectral–spatial classification of hyperspectral images using deep convolutional neural networks,” Remote Sensing Letters. 6(6), 468–477 (2015).
[Crossref]

Zhang, B.

A. Dong, J. Li, B. Zhang, and M. Liang, “Hyperspectral Image Classification Algorithm Based on Spectral Clustering and Sparse Representation,” Acta Optica Sinica. 37(8), 0828005 (2017).
[Crossref]

Zhang, G.

Zhang, H.

L. Ying, H. Zhang, and S. Qiang, “Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network,” Remote Sens. 9(1), 67–69 (2017).
[Crossref]

H. Akbari, L. V. Halig, H. Zhang, D. Wang, Z. G. Chen, and B. Fei, “Detection of Cancer Metastasis Using a Novel Macroscopic Hyperspectral Method,” Proc. SPIE 8317, 831711 (2012).
[Crossref]

Zhang, L.

B. Luo and L. Zhang, “Robust Autodual Morphological Profiles for the Classification of High-Resolution Satellite Images,” IEEE Trans. Geosci. Remote Sens. 52(2), 1451–1462 (2014).
[Crossref]

Zhang, Y.

H. Hou, Z. Fang, Y. Zhang, M. Dong, and Y. Liu, “Simulation and in vivo Experimental Study on Noninvasive Spectral Detection of Skin Cholesterol,” Chin. J. Laser 43(9), 0907001 (2016).
[Crossref]

Zhao, W.

J. Yue, W. Zhao, S. Mao, and H. Liu, “Spectral–spatial classification of hyperspectral images using deep convolutional neural networks,” Remote Sensing Letters. 6(6), 468–477 (2015).
[Crossref]

Zhu, S.

Acta Optica Sinica. (1)

A. Dong, J. Li, B. Zhang, and M. Liang, “Hyperspectral Image Classification Algorithm Based on Spectral Clustering and Sparse Representation,” Acta Optica Sinica. 37(8), 0828005 (2017).
[Crossref]

Biomed. Opt. Express (1)

Chin. J. Laser (1)

H. Hou, Z. Fang, Y. Zhang, M. Dong, and Y. Liu, “Simulation and in vivo Experimental Study on Noninvasive Spectral Detection of Skin Cholesterol,” Chin. J. Laser 43(9), 0907001 (2016).
[Crossref]

Grassland Science. (1)

Y. Suzuki, H. Okamoto, M. Takahashi, T. Kataoka, and Y. Shibata, “Mapping the spatial distribution of botanical composition and herbage mass in pastures using hyperspectral imaging,” Grassland Science. 58(1), 1–7 (2012).
[Crossref]

IEEE Eng. Med. Biol. Mag. (1)

T. Vodinh, D. L. Stokes, M. B. Wabuyele, M. E. Martin, J. M. Song, R. Jagannathan, E. Michaud, R. J. Lee, and X. Pan, “A hyperspectral imaging system for in vivo optical diagnostics. Hyperspectral imaging basic principles, instrumental systems, and applications of biomedical interest,” IEEE Eng. Med. Biol. Mag. 23(5), 40–49 (2004).
[Crossref]

IEEE Trans. Geosci. Remote Sens. (3)

B. Luo and L. Zhang, “Robust Autodual Morphological Profiles for the Classification of High-Resolution Satellite Images,” IEEE Trans. Geosci. Remote Sens. 52(2), 1451–1462 (2014).
[Crossref]

L. Wei, G. Wu, Z. Fan, and D. Qian, “Hyperspectral Image Classification Using Deep Pixel-Pair Features,” IEEE Trans. Geosci. Remote Sens. 55(2), 844–853 (2017).
[Crossref]

Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks,” IEEE Trans. Geosci. Remote Sens. 54(10), 6232–6251 (2016).
[Crossref]

Innovative Food Sci. Emerging Technol. (1)

M. Kamruzzaman, D. Barbin, G. Elmasry, D. W. Sun, and P. Allen, “Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat,” Innovative Food Sci. Emerging Technol. 16(1), 316–325 (2012).
[Crossref]

Int. J. Cancer (1)

J. Ferlay, I. Soerjomataram, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D. M. Parkin, D. Forman, and F. Bray, “Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012,” Int. J. Cancer 136(5), E359–E386 (2015).
[Crossref]

J. Biomed. Opt. (1)

H. Akbari, L. V. Halig, D. M. Schuster, O. Adeboye, M. Viraj, P. T. Nieh, G. Z. Chen, and F. Baowei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 0760051 (2012).
[Crossref]

J. Biophoton. (1)

A. O. Gerstner, W. Laffers, F. Bootz, D. L. Farkas, R. Martin, J. Bendix, and B. Thies, “Hyperspectral imaging of mucosal surfaces in patients,” J. Biophoton. 5(3), 255–262 (2012).
[Crossref]

J. Food Eng. (1)

J. A. Sanz, A. M. Fernandes, E. Barrenechea, S. Silva, V. Santos, N. Gonçalves, D. Paternain, A. Jurio, and P. Melo-Pinto, “Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms,” J. Food Eng. 174, 92–100 (2016).
[Crossref]

Nat. Protoc. (1)

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9(8), 1771–1791 (2014).
[Crossref]

Opt. Laser Technol. (1)

Q. Li, C. Li, H. Liu, Z. Mei, Y. Wang, and F. Guo, “Skin cells segmentation algorithm based on spectral angle and distance score,” Opt. Laser Technol. 74, 79–86 (2015).
[Crossref]

Perspect. Vasc. Surg. (1)

D. C. Kellicut, J. M. Weiswasser, S. Arora, E. Freeman, R. A. Lew, C. Shuman, J. R. Mansfield, and A. N. Sidawy, “Emerging Technology: Hyperspectral Imaging,” Perspect. Vasc. Surg. 16(1), 53–57 (2004).
[Crossref]

Proc. IEEE (1)

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Proc. SPIE (1)

H. Akbari, L. V. Halig, H. Zhang, D. Wang, Z. G. Chen, and B. Fei, “Detection of Cancer Metastasis Using a Novel Macroscopic Hyperspectral Method,” Proc. SPIE 8317, 831711 (2012).
[Crossref]

Remote Sens. (2)

H. Liang and Q. Li, “Hyperspectral imagery classification using sparse representations of convolutional neural network features,” Remote Sens. 8(2), 99 (2016).
[Crossref]

L. Ying, H. Zhang, and S. Qiang, “Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network,” Remote Sens. 9(1), 67–69 (2017).
[Crossref]

Remote Sensing Letters. (1)

J. Yue, W. Zhao, S. Mao, and H. Liu, “Spectral–spatial classification of hyperspectral images using deep convolutional neural networks,” Remote Sensing Letters. 6(6), 468–477 (2015).
[Crossref]

Science (1)

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313(5786), 504–507 (2006).
[Crossref]

Spectrosc. Spect. Anal. (1)

H. Liu, W. Gu, Q. Li, Y. Wang, Z. Chen, and X. Qin, “Nerve Classification with Hyperspectral Imaging Technology,” Spectrosc. Spect. Anal. 35(1), 38–43 (2015).
[Crossref]

Trends Food Sci. Technol. (1)

D. Wu and D. Sun, “Color measurements by computer vision for food quality control – A review,” Trends Food Sci. Technol. 29(1), 5–20 (2013).
[Crossref]

Other (6)

K. Makantasis, K. Karantzalos, A. Doulamis, and N. Doulamis, “Deep supervised learning for hyperspectral data classification through convolutional neural networks,” in 2015 IEEE International Geoscience and Remote Sensing Symposium (2015), pp. 4959–4962.

D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in Proceedings of the IEEE international conference on computer vision (2015), pp. 4489–4497.

L. Lin, H. Dong, and X. Song, “DBN-based Classification of Spatial-spectral Hyperspectral Data,” in Advances in Intelligent Information Hiding and Multimedia Signal Processing (Springer, 2017), pp. 53–60.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems (2012), pp. 1097–1105.

S. Ortega, G. M. Callico, M. L. Plaza, R. Camacho, H. Fabelo, and R. Sarmiento, “Hyperspectral database of pathological in-vitro human brain samples to detect carcinogenic tissues,” in IEEE International Symposium on Biomedical Imaging (2016), pp. 369–372.

Keras Documentation. http://keras.io .

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Figures (13)

Fig. 1.
Fig. 1. Micro-hyperspectral data processing framework of gastric cancer.
Fig. 2.
Fig. 2. Schematic of micro-hyperspectral imaging system. (a) Imaging system. (b) Software surface. (c) Electric control scanning mechanism.
Fig. 3.
Fig. 3. (a) Gastric cancer tissue markers by red line. (b) Gastric cancer cell markers in green circles.
Fig. 4.
Fig. 4. The comparison between the pre-calibration (upper row) and post-calibration (lower row). The spectral image of (a) 80th, (b) 130th, (c) 180th, (d) 230th, (e) pseudo-color image.
Fig. 5.
Fig. 5. 1st-derivative spectral curves of gastric cancer and normal tissue.
Fig. 6.
Fig. 6. Schematic diagram of local connection theory.
Fig. 7.
Fig. 7. Establishment process of four CNN models.
Fig. 8.
Fig. 8. Schematic diagram of Spec-CNN model structure.
Fig. 9.
Fig. 9. Influences of different parameters on Spec-CNN. (a) Number of convolution layers. (b) Number and size of convolution kernels. (c) Number of neurons in fully connected layer.
Fig. 10.
Fig. 10. Influences of different parameters on SS-CNN-1. (a) Classification results of different neighborhood sizes. (b) Classification results of different principal components.
Fig. 11.
Fig. 11. Convolution kernel visualization of C2 layer.
Fig. 12.
Fig. 12. Feature maps of each layer of gastric tissue.
Fig. 13.
Fig. 13. Training error curves of ReLU and Sigmoid in (a) dataset III and (b) IV.

Tables (11)

Tables Icon

Table 1. Experimental dataset.

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Table 2. Structure and parameters of Spec-CNN.

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Table 3. Dataset division.

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Table 4. Classification results of Spec-CNN in dataset I, II, III and IV.

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Table 5. Classification results of three types of models in dataset IV.

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Table 6. Experimental results of Case study 1.

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Table 7. Experimental results of Case study 2.

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Table 8. Structure and parameters of SS-CNN-1 and SS-CNN-2.

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Table 9. Classification results of different neighborhood sizes.

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Table 10. Model structure and parameters of SS-CNN-3.

Tables Icon

Table 11. Training results of each model.

Equations (2)

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

α = cos 1 [ i = 1 n b t i r i ( i = 1 n b t i 2 ) 1 2 ( i = 1 n b r i 2 ) 1 2 ]
h i k = ReLU ( ( W k x ) i + b k )

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