Abstract

Hyperspectral imaging (HSI) is an emerging technology for medical diagnosis. This research work presents a proof-of-concept on the use of HSI data to automatically detect human brain tumor tissue in pathological slides. The samples, consisting of hyperspectral cubes collected from 400 nm to 1000 nm, were acquired from ten different patients diagnosed with high-grade glioma. Based on the diagnosis provided by pathologists, a spectral library of normal and tumor tissues was created and processed using three different supervised classification algorithms. Results prove that HSI is a suitable technique to automatically detect high-grade tumors from pathological slides.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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Corrections

28 February 2018: A typographical correction was made to Ref. 2.


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References

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2017 (3)

Q. Ang, J. Ang, M. Hou, Q. Ingli, Y. Iting, and W. Ang, “Spectral-spatial feature-based neural network method for acute lymphoblastic leukemia cell identification via microscopic hyperspectral imaging technology,” Biomed. Opt. Express 8, 3017–3028 (2017).

S. Ortega, H. Fabelo, R. Camacho, M. L. Plaza, G. M. Callico, R. Lazcano, D. Madroñal, R. Salvador, E. Juárez, and R. Sarmiento, “P03.18 Detection of human brain cancer in pathological slides using hyperspectral images,” Neuro-oncol. 19(suppl_3), iii37 (2017).
[Crossref]

P. Ghamisi, J. Plaza, Y. Chen, J. Li, and A. J. Plaza, “Advanced Spectral Classifiers for Hyperspectral Images: A review,” IEEE Geosci. Remote Sens. Mag. 5(1), 8–32 (2017).
[Crossref]

2016 (4)

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).

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

D. N. Louis, A. Perry, G. Reifenberger, A. von Deimling, D. Figarella-Branger, W. K. Cavenee, H. Ohgaki, O. D. Wiestler, P. Kleihues, and D. W. Ellison, “The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary,” Acta Neuropathol. 131(6), 803–820 (2016).
[Crossref] [PubMed]

R. Pike, G. Lu, D. Wang, Z. G. Chen, and B. Fei, “A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging,” IEEE Trans. Biomed. Eng. 63(3), 653–663 (2016).
[Crossref] [PubMed]

2014 (2)

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

C. Lu and M. Mandal, “Toward Automatic Mitotic Cell Detection and Segmentation in Multispectral Histopathological Images,” IEEE J. Biomed. Health Informatics 18, 594–605 (2014).

2013 (1)

Q. Li, X. He, Y. Wang, H. Liu, D. Xu, and F. Guo, “Review of spectral imaging technology in biomedical engineering: achievements and challenges,” J. Biomed. Opt. 18(10), 100901 (2013).
[Crossref] [PubMed]

2012 (3)

F. Blanco, M. López-Mesas, S. Serranti, G. Bonifazi, J. Havel, and M. Valiente, “Hyperspectral imaging based method for fast characterization of kidney stone types,” J. Biomed. Opt. 17(7), 076027 (2012).
[Crossref] [PubMed]

Z. Liu, H. Wang, and Q. Li, “Tongue tumor detection in medical hyperspectral images,” Sensors (Basel) 12(1), 162–174 (2012).
[PubMed]

H. Akbari, L. V. Halig, D. M. Schuster, A. Osunkoya, V. Master, P. T. Nieh, G. Z. Chen, and B. Fei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 076005 (2012).
[Crossref] [PubMed]

2011 (2)

D. J. Mordant, I. Al-Abboud, G. Muyo, A. Gorman, A. Sallam, P. Ritchie, A. R. Harvey, and A. I. McNaught, “Spectral imaging of the retina,” Eye (Lond.) 25(3), 309–320 (2011).
[Crossref] [PubMed]

J. Burger and A. Gowen, “Data handling in hyperspectral image analysis,” Chemom. Intell. Lab. Syst. 108(1), 13–22 (2011).
[Crossref]

2010 (1)

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE Trans. Biomed. Eng. 57(8), 2011–2017 (2010).
[Crossref] [PubMed]

2009 (1)

A. Plaza, J. Atli, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Remote Sensing of Environment Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

2008 (2)

J. C.-W. Chan and D. Paelinckx, “Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery,” Remote Sens. Environ. 112(6), 2999–3011 (2008).
[Crossref]

R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “LIBLINEAR: A Library for Large Linear Classification,” J. Mach. Learn. Res. 9, 1871–1874 (2008).

2007 (1)

D. N. Louis, H. Ohgaki, O. D. Wiestler, W. K. Cavenee, P. C. Burger, A. Jouvet, B. W. Scheithauer, and P. Kleihues, “The 2007 WHO Classification of Tumours of the Central Nervous System,” WHO 2007, 97–109 (2007).

2005 (2)

G. Camps-Valls and L. Bruzzone, “Kernel-based methods for hyperspectral image classification,” IEEE Trans. Geosci. Remote Sens. 43(6), 1351–1362 (2005).
[Crossref]

J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification of hyperspectral data from urban areas based on extended morphological profiles,” IEEE Trans. Geosci. Remote Sens. 43(3), 480–491 (2005).
[Crossref]

2003 (1)

R. Gillies, J. E. Freeman, L. C. Cancio, D. Brand, M. Hopmeier, and J. R. Mansfield, “Systemic Effects of Shock and Resuscitation Monitored by Visible Hyperspectral Imaging,” Diabetes Technol. Ther. 5(5), 847–855 (2003).
[Crossref] [PubMed]

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).
[PubMed]

Akbari, H.

H. Akbari, L. V. Halig, D. M. Schuster, A. Osunkoya, V. Master, P. T. Nieh, G. Z. Chen, and B. Fei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 076005 (2012).
[Crossref] [PubMed]

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE Trans. Biomed. Eng. 57(8), 2011–2017 (2010).
[Crossref] [PubMed]

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Blood vessel detection and artery-vein differentiation using hyperspectral imaging,” in Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 (2009), pp. 1461–1464.
[Crossref]

Al-Abboud, I.

D. J. Mordant, I. Al-Abboud, G. Muyo, A. Gorman, A. Sallam, P. Ritchie, A. R. Harvey, and A. I. McNaught, “Spectral imaging of the retina,” Eye (Lond.) 25(3), 309–320 (2011).
[Crossref] [PubMed]

Ang, J.

Ang, Q.

Ang, W.

Atli, J.

A. Plaza, J. Atli, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Remote Sensing of Environment Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Benediktsson, J. A.

J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification of hyperspectral data from urban areas based on extended morphological profiles,” IEEE Trans. Geosci. Remote Sens. 43(3), 480–491 (2005).
[Crossref]

Blanco, F.

F. Blanco, M. López-Mesas, S. Serranti, G. Bonifazi, J. Havel, and M. Valiente, “Hyperspectral imaging based method for fast characterization of kidney stone types,” J. Biomed. Opt. 17(7), 076027 (2012).
[Crossref] [PubMed]

Boardman, J. W.

A. Plaza, J. Atli, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Remote Sensing of Environment Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Bonifazi, G.

F. Blanco, M. López-Mesas, S. Serranti, G. Bonifazi, J. Havel, and M. Valiente, “Hyperspectral imaging based method for fast characterization of kidney stone types,” J. Biomed. Opt. 17(7), 076027 (2012).
[Crossref] [PubMed]

Brand, D.

R. Gillies, J. E. Freeman, L. C. Cancio, D. Brand, M. Hopmeier, and J. R. Mansfield, “Systemic Effects of Shock and Resuscitation Monitored by Visible Hyperspectral Imaging,” Diabetes Technol. Ther. 5(5), 847–855 (2003).
[Crossref] [PubMed]

Brazile, J.

A. Plaza, J. Atli, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Remote Sensing of Environment Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Bruzzone, L.

A. Plaza, J. Atli, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Remote Sensing of Environment Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

G. Camps-Valls and L. Bruzzone, “Kernel-based methods for hyperspectral image classification,” IEEE Trans. Geosci. Remote Sens. 43(6), 1351–1362 (2005).
[Crossref]

Burger, J.

J. Burger and A. Gowen, “Data handling in hyperspectral image analysis,” Chemom. Intell. Lab. Syst. 108(1), 13–22 (2011).
[Crossref]

Burger, P. C.

D. N. Louis, H. Ohgaki, O. D. Wiestler, W. K. Cavenee, P. C. Burger, A. Jouvet, B. W. Scheithauer, and P. Kleihues, “The 2007 WHO Classification of Tumours of the Central Nervous System,” WHO 2007, 97–109 (2007).

Callico, G. M.

S. Ortega, H. Fabelo, R. Camacho, M. L. Plaza, G. M. Callico, R. Lazcano, D. Madroñal, R. Salvador, E. Juárez, and R. Sarmiento, “P03.18 Detection of human brain cancer in pathological slides using hyperspectral images,” Neuro-oncol. 19(suppl_3), iii37 (2017).
[Crossref]

Camacho, R.

S. Ortega, H. Fabelo, R. Camacho, M. L. Plaza, G. M. Callico, R. Lazcano, D. Madroñal, R. Salvador, E. Juárez, and R. Sarmiento, “P03.18 Detection of human brain cancer in pathological slides using hyperspectral images,” Neuro-oncol. 19(suppl_3), iii37 (2017).
[Crossref]

Camps-valls, G.

A. Plaza, J. Atli, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Remote Sensing of Environment Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

G. Camps-Valls and L. Bruzzone, “Kernel-based methods for hyperspectral image classification,” IEEE Trans. Geosci. Remote Sens. 43(6), 1351–1362 (2005).
[Crossref]

Cancio, L. C.

R. Gillies, J. E. Freeman, L. C. Cancio, D. Brand, M. Hopmeier, and J. R. Mansfield, “Systemic Effects of Shock and Resuscitation Monitored by Visible Hyperspectral Imaging,” Diabetes Technol. Ther. 5(5), 847–855 (2003).
[Crossref] [PubMed]

Cavenee, W. K.

D. N. Louis, A. Perry, G. Reifenberger, A. von Deimling, D. Figarella-Branger, W. K. Cavenee, H. Ohgaki, O. D. Wiestler, P. Kleihues, and D. W. Ellison, “The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary,” Acta Neuropathol. 131(6), 803–820 (2016).
[Crossref] [PubMed]

D. N. Louis, H. Ohgaki, O. D. Wiestler, W. K. Cavenee, P. C. Burger, A. Jouvet, B. W. Scheithauer, and P. Kleihues, “The 2007 WHO Classification of Tumours of the Central Nervous System,” WHO 2007, 97–109 (2007).

Chan, J. C.-W.

J. C.-W. Chan and D. Paelinckx, “Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery,” Remote Sens. Environ. 112(6), 2999–3011 (2008).
[Crossref]

Chang, K.-W.

R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “LIBLINEAR: A Library for Large Linear Classification,” J. Mach. Learn. Res. 9, 1871–1874 (2008).

Chanussot, J.

A. Plaza, J. Atli, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Remote Sensing of Environment Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Chen, G. Z.

H. Akbari, L. V. Halig, D. M. Schuster, A. Osunkoya, V. Master, P. T. Nieh, G. Z. Chen, and B. Fei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 076005 (2012).
[Crossref] [PubMed]

Chen, Y.

P. Ghamisi, J. Plaza, Y. Chen, J. Li, and A. J. Plaza, “Advanced Spectral Classifiers for Hyperspectral Images: A review,” IEEE Geosci. Remote Sens. Mag. 5(1), 8–32 (2017).
[Crossref]

Chen, Z. G.

R. Pike, G. Lu, D. Wang, Z. G. Chen, and B. Fei, “A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging,” IEEE Trans. Biomed. Eng. 63(3), 653–663 (2016).
[Crossref] [PubMed]

Ellison, D. W.

D. N. Louis, A. Perry, G. Reifenberger, A. von Deimling, D. Figarella-Branger, W. K. Cavenee, H. Ohgaki, O. D. Wiestler, P. Kleihues, and D. W. Ellison, “The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary,” Acta Neuropathol. 131(6), 803–820 (2016).
[Crossref] [PubMed]

Fabelo, H.

S. Ortega, H. Fabelo, R. Camacho, M. L. Plaza, G. M. Callico, R. Lazcano, D. Madroñal, R. Salvador, E. Juárez, and R. Sarmiento, “P03.18 Detection of human brain cancer in pathological slides using hyperspectral images,” Neuro-oncol. 19(suppl_3), iii37 (2017).
[Crossref]

Fan, R.-E.

R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “LIBLINEAR: A Library for Large Linear Classification,” J. Mach. Learn. Res. 9, 1871–1874 (2008).

Fauvel, M.

A. Plaza, J. Atli, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Remote Sensing of Environment Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Fei, B.

R. Pike, G. Lu, D. Wang, Z. G. Chen, and B. Fei, “A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging,” IEEE Trans. Biomed. Eng. 63(3), 653–663 (2016).
[Crossref] [PubMed]

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

H. Akbari, L. V. Halig, D. M. Schuster, A. Osunkoya, V. Master, P. T. Nieh, G. Z. Chen, and B. Fei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 076005 (2012).
[Crossref] [PubMed]

Figarella-Branger, D.

D. N. Louis, A. Perry, G. Reifenberger, A. von Deimling, D. Figarella-Branger, W. K. Cavenee, H. Ohgaki, O. D. Wiestler, P. Kleihues, and D. W. Ellison, “The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary,” Acta Neuropathol. 131(6), 803–820 (2016).
[Crossref] [PubMed]

Freeman, J. E.

R. Gillies, J. E. Freeman, L. C. Cancio, D. Brand, M. Hopmeier, and J. R. Mansfield, “Systemic Effects of Shock and Resuscitation Monitored by Visible Hyperspectral Imaging,” Diabetes Technol. Ther. 5(5), 847–855 (2003).
[Crossref] [PubMed]

Fujita, Y.

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

Gamba, P.

A. Plaza, J. Atli, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Remote Sensing of Environment Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Ghamisi, P.

P. Ghamisi, J. Plaza, Y. Chen, J. Li, and A. J. Plaza, “Advanced Spectral Classifiers for Hyperspectral Images: A review,” IEEE Geosci. Remote Sens. Mag. 5(1), 8–32 (2017).
[Crossref]

Gillies, R.

R. Gillies, J. E. Freeman, L. C. Cancio, D. Brand, M. Hopmeier, and J. R. Mansfield, “Systemic Effects of Shock and Resuscitation Monitored by Visible Hyperspectral Imaging,” Diabetes Technol. Ther. 5(5), 847–855 (2003).
[Crossref] [PubMed]

Goetz, A. F. H.

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).
[PubMed]

Gorman, A.

D. J. Mordant, I. Al-Abboud, G. Muyo, A. Gorman, A. Sallam, P. Ritchie, A. R. Harvey, and A. I. McNaught, “Spectral imaging of the retina,” Eye (Lond.) 25(3), 309–320 (2011).
[Crossref] [PubMed]

Goto, A.

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

Gowen, A.

J. Burger and A. Gowen, “Data handling in hyperspectral image analysis,” Chemom. Intell. Lab. Syst. 108(1), 13–22 (2011).
[Crossref]

Gualtieri, A.

A. Plaza, J. Atli, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Remote Sensing of Environment Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Guo, F.

Q. Li, X. He, Y. Wang, H. Liu, D. Xu, and F. Guo, “Review of spectral imaging technology in biomedical engineering: achievements and challenges,” J. Biomed. Opt. 18(10), 100901 (2013).
[Crossref] [PubMed]

Halig, L. V.

H. Akbari, L. V. Halig, D. M. Schuster, A. Osunkoya, V. Master, P. T. Nieh, G. Z. Chen, and B. Fei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 076005 (2012).
[Crossref] [PubMed]

Hamamoto, Y.

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

Harvey, A. R.

D. J. Mordant, I. Al-Abboud, G. Muyo, A. Gorman, A. Sallam, P. Ritchie, A. R. Harvey, and A. I. McNaught, “Spectral imaging of the retina,” Eye (Lond.) 25(3), 309–320 (2011).
[Crossref] [PubMed]

Havel, J.

F. Blanco, M. López-Mesas, S. Serranti, G. Bonifazi, J. Havel, and M. Valiente, “Hyperspectral imaging based method for fast characterization of kidney stone types,” J. Biomed. Opt. 17(7), 076027 (2012).
[Crossref] [PubMed]

He, X.

Q. Li, X. He, Y. Wang, H. Liu, D. Xu, and F. Guo, “Review of spectral imaging technology in biomedical engineering: achievements and challenges,” J. Biomed. Opt. 18(10), 100901 (2013).
[Crossref] [PubMed]

Hopmeier, M.

R. Gillies, J. E. Freeman, L. C. Cancio, D. Brand, M. Hopmeier, and J. R. Mansfield, “Systemic Effects of Shock and Resuscitation Monitored by Visible Hyperspectral Imaging,” Diabetes Technol. Ther. 5(5), 847–855 (2003).
[Crossref] [PubMed]

Hou, M.

Hsieh, C.-J.

R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “LIBLINEAR: A Library for Large Linear Classification,” J. Mach. Learn. Res. 9, 1871–1874 (2008).

Ingli, Q.

Iting, Y.

Jouvet, A.

D. N. Louis, H. Ohgaki, O. D. Wiestler, W. K. Cavenee, P. C. Burger, A. Jouvet, B. W. Scheithauer, and P. Kleihues, “The 2007 WHO Classification of Tumours of the Central Nervous System,” WHO 2007, 97–109 (2007).

Juárez, E.

S. Ortega, H. Fabelo, R. Camacho, M. L. Plaza, G. M. Callico, R. Lazcano, D. Madroñal, R. Salvador, E. Juárez, and R. Sarmiento, “P03.18 Detection of human brain cancer in pathological slides using hyperspectral images,” Neuro-oncol. 19(suppl_3), iii37 (2017).
[Crossref]

Kleihues, P.

D. N. Louis, A. Perry, G. Reifenberger, A. von Deimling, D. Figarella-Branger, W. K. Cavenee, H. Ohgaki, O. D. Wiestler, P. Kleihues, and D. W. Ellison, “The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary,” Acta Neuropathol. 131(6), 803–820 (2016).
[Crossref] [PubMed]

D. N. Louis, H. Ohgaki, O. D. Wiestler, W. K. Cavenee, P. C. Burger, A. Jouvet, B. W. Scheithauer, and P. Kleihues, “The 2007 WHO Classification of Tumours of the Central Nervous System,” WHO 2007, 97–109 (2007).

Kojima, K.

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE Trans. Biomed. Eng. 57(8), 2011–2017 (2010).
[Crossref] [PubMed]

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Blood vessel detection and artery-vein differentiation using hyperspectral imaging,” in Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 (2009), pp. 1461–1464.
[Crossref]

Kosugi, Y.

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE Trans. Biomed. Eng. 57(8), 2011–2017 (2010).
[Crossref] [PubMed]

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Blood vessel detection and artery-vein differentiation using hyperspectral imaging,” in Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 (2009), pp. 1461–1464.
[Crossref]

Lazcano, R.

S. Ortega, H. Fabelo, R. Camacho, M. L. Plaza, G. M. Callico, R. Lazcano, D. Madroñal, R. Salvador, E. Juárez, and R. Sarmiento, “P03.18 Detection of human brain cancer in pathological slides using hyperspectral images,” Neuro-oncol. 19(suppl_3), iii37 (2017).
[Crossref]

Li, J.

P. Ghamisi, J. Plaza, Y. Chen, J. Li, and A. J. Plaza, “Advanced Spectral Classifiers for Hyperspectral Images: A review,” IEEE Geosci. Remote Sens. Mag. 5(1), 8–32 (2017).
[Crossref]

Li, Q.

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).

Q. Li, X. He, Y. Wang, H. Liu, D. Xu, and F. Guo, “Review of spectral imaging technology in biomedical engineering: achievements and challenges,” J. Biomed. Opt. 18(10), 100901 (2013).
[Crossref] [PubMed]

Z. Liu, H. Wang, and Q. Li, “Tongue tumor detection in medical hyperspectral images,” Sensors (Basel) 12(1), 162–174 (2012).
[PubMed]

Lin, C.-J.

R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “LIBLINEAR: A Library for Large Linear Classification,” J. Mach. Learn. Res. 9, 1871–1874 (2008).

Liu, H.

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).

Q. Li, X. He, Y. Wang, H. Liu, D. Xu, and F. Guo, “Review of spectral imaging technology in biomedical engineering: achievements and challenges,” J. Biomed. Opt. 18(10), 100901 (2013).
[Crossref] [PubMed]

Liu, Z.

Z. Liu, H. Wang, and Q. Li, “Tongue tumor detection in medical hyperspectral images,” Sensors (Basel) 12(1), 162–174 (2012).
[PubMed]

López-Mesas, M.

F. Blanco, M. López-Mesas, S. Serranti, G. Bonifazi, J. Havel, and M. Valiente, “Hyperspectral imaging based method for fast characterization of kidney stone types,” J. Biomed. Opt. 17(7), 076027 (2012).
[Crossref] [PubMed]

Lou, J.

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).

Louis, D. N.

D. N. Louis, A. Perry, G. Reifenberger, A. von Deimling, D. Figarella-Branger, W. K. Cavenee, H. Ohgaki, O. D. Wiestler, P. Kleihues, and D. W. Ellison, “The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary,” Acta Neuropathol. 131(6), 803–820 (2016).
[Crossref] [PubMed]

D. N. Louis, H. Ohgaki, O. D. Wiestler, W. K. Cavenee, P. C. Burger, A. Jouvet, B. W. Scheithauer, and P. Kleihues, “The 2007 WHO Classification of Tumours of the Central Nervous System,” WHO 2007, 97–109 (2007).

Lu, C.

C. Lu and M. Mandal, “Toward Automatic Mitotic Cell Detection and Segmentation in Multispectral Histopathological Images,” IEEE J. Biomed. Health Informatics 18, 594–605 (2014).

Lu, G.

R. Pike, G. Lu, D. Wang, Z. G. Chen, and B. Fei, “A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging,” IEEE Trans. Biomed. Eng. 63(3), 653–663 (2016).
[Crossref] [PubMed]

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

Madroñal, D.

S. Ortega, H. Fabelo, R. Camacho, M. L. Plaza, G. M. Callico, R. Lazcano, D. Madroñal, R. Salvador, E. Juárez, and R. Sarmiento, “P03.18 Detection of human brain cancer in pathological slides using hyperspectral images,” Neuro-oncol. 19(suppl_3), iii37 (2017).
[Crossref]

Mandal, M.

C. Lu and M. Mandal, “Toward Automatic Mitotic Cell Detection and Segmentation in Multispectral Histopathological Images,” IEEE J. Biomed. Health Informatics 18, 594–605 (2014).

Mansfield, J. R.

R. Gillies, J. E. Freeman, L. C. Cancio, D. Brand, M. Hopmeier, and J. R. Mansfield, “Systemic Effects of Shock and Resuscitation Monitored by Visible Hyperspectral Imaging,” Diabetes Technol. Ther. 5(5), 847–855 (2003).
[Crossref] [PubMed]

Marconcini, M.

A. Plaza, J. Atli, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Remote Sensing of Environment Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Master, V.

H. Akbari, L. V. Halig, D. M. Schuster, A. Osunkoya, V. Master, P. T. Nieh, G. Z. Chen, and B. Fei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 076005 (2012).
[Crossref] [PubMed]

McNaught, A. I.

D. J. Mordant, I. Al-Abboud, G. Muyo, A. Gorman, A. Sallam, P. Ritchie, A. R. Harvey, and A. I. McNaught, “Spectral imaging of the retina,” Eye (Lond.) 25(3), 309–320 (2011).
[Crossref] [PubMed]

Mordant, D. J.

D. J. Mordant, I. Al-Abboud, G. Muyo, A. Gorman, A. Sallam, P. Ritchie, A. R. Harvey, and A. I. McNaught, “Spectral imaging of the retina,” Eye (Lond.) 25(3), 309–320 (2011).
[Crossref] [PubMed]

Muyo, G.

D. J. Mordant, I. Al-Abboud, G. Muyo, A. Gorman, A. Sallam, P. Ritchie, A. R. Harvey, and A. I. McNaught, “Spectral imaging of the retina,” Eye (Lond.) 25(3), 309–320 (2011).
[Crossref] [PubMed]

Nieh, P. T.

H. Akbari, L. V. Halig, D. M. Schuster, A. Osunkoya, V. Master, P. T. Nieh, G. Z. Chen, and B. Fei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 076005 (2012).
[Crossref] [PubMed]

Nishikawa, J.

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

Ogihara, H.

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

Ohgaki, H.

D. N. Louis, A. Perry, G. Reifenberger, A. von Deimling, D. Figarella-Branger, W. K. Cavenee, H. Ohgaki, O. D. Wiestler, P. Kleihues, and D. W. Ellison, “The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary,” Acta Neuropathol. 131(6), 803–820 (2016).
[Crossref] [PubMed]

D. N. Louis, H. Ohgaki, O. D. Wiestler, W. K. Cavenee, P. C. Burger, A. Jouvet, B. W. Scheithauer, and P. Kleihues, “The 2007 WHO Classification of Tumours of the Central Nervous System,” WHO 2007, 97–109 (2007).

Ortega, S.

S. Ortega, H. Fabelo, R. Camacho, M. L. Plaza, G. M. Callico, R. Lazcano, D. Madroñal, R. Salvador, E. Juárez, and R. Sarmiento, “P03.18 Detection of human brain cancer in pathological slides using hyperspectral images,” Neuro-oncol. 19(suppl_3), iii37 (2017).
[Crossref]

Osunkoya, A.

H. Akbari, L. V. Halig, D. M. Schuster, A. Osunkoya, V. Master, P. T. Nieh, G. Z. Chen, and B. Fei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 076005 (2012).
[Crossref] [PubMed]

Paelinckx, D.

J. C.-W. Chan and D. Paelinckx, “Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery,” Remote Sens. Environ. 112(6), 2999–3011 (2008).
[Crossref]

Palmason, J. A.

J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification of hyperspectral data from urban areas based on extended morphological profiles,” IEEE Trans. Geosci. Remote Sens. 43(3), 480–491 (2005).
[Crossref]

Perry, A.

D. N. Louis, A. Perry, G. Reifenberger, A. von Deimling, D. Figarella-Branger, W. K. Cavenee, H. Ohgaki, O. D. Wiestler, P. Kleihues, and D. W. Ellison, “The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary,” Acta Neuropathol. 131(6), 803–820 (2016).
[Crossref] [PubMed]

Pike, R.

R. Pike, G. Lu, D. Wang, Z. G. Chen, and B. Fei, “A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging,” IEEE Trans. Biomed. Eng. 63(3), 653–663 (2016).
[Crossref] [PubMed]

Plaza, A.

A. Plaza, J. Atli, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Remote Sensing of Environment Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Plaza, A. J.

P. Ghamisi, J. Plaza, Y. Chen, J. Li, and A. J. Plaza, “Advanced Spectral Classifiers for Hyperspectral Images: A review,” IEEE Geosci. Remote Sens. Mag. 5(1), 8–32 (2017).
[Crossref]

Plaza, J.

P. Ghamisi, J. Plaza, Y. Chen, J. Li, and A. J. Plaza, “Advanced Spectral Classifiers for Hyperspectral Images: A review,” IEEE Geosci. Remote Sens. Mag. 5(1), 8–32 (2017).
[Crossref]

Plaza, M. L.

S. Ortega, H. Fabelo, R. Camacho, M. L. Plaza, G. M. Callico, R. Lazcano, D. Madroñal, R. Salvador, E. Juárez, and R. Sarmiento, “P03.18 Detection of human brain cancer in pathological slides using hyperspectral images,” Neuro-oncol. 19(suppl_3), iii37 (2017).
[Crossref]

Reifenberger, G.

D. N. Louis, A. Perry, G. Reifenberger, A. von Deimling, D. Figarella-Branger, W. K. Cavenee, H. Ohgaki, O. D. Wiestler, P. Kleihues, and D. W. Ellison, “The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary,” Acta Neuropathol. 131(6), 803–820 (2016).
[Crossref] [PubMed]

Ritchie, P.

D. J. Mordant, I. Al-Abboud, G. Muyo, A. Gorman, A. Sallam, P. Ritchie, A. R. Harvey, and A. I. McNaught, “Spectral imaging of the retina,” Eye (Lond.) 25(3), 309–320 (2011).
[Crossref] [PubMed]

Rock, B. N.

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).
[PubMed]

Sakaida, I.

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

Sallam, A.

D. J. Mordant, I. Al-Abboud, G. Muyo, A. Gorman, A. Sallam, P. Ritchie, A. R. Harvey, and A. I. McNaught, “Spectral imaging of the retina,” Eye (Lond.) 25(3), 309–320 (2011).
[Crossref] [PubMed]

Salvador, R.

S. Ortega, H. Fabelo, R. Camacho, M. L. Plaza, G. M. Callico, R. Lazcano, D. Madroñal, R. Salvador, E. Juárez, and R. Sarmiento, “P03.18 Detection of human brain cancer in pathological slides using hyperspectral images,” Neuro-oncol. 19(suppl_3), iii37 (2017).
[Crossref]

Sarmiento, R.

S. Ortega, H. Fabelo, R. Camacho, M. L. Plaza, G. M. Callico, R. Lazcano, D. Madroñal, R. Salvador, E. Juárez, and R. Sarmiento, “P03.18 Detection of human brain cancer in pathological slides using hyperspectral images,” Neuro-oncol. 19(suppl_3), iii37 (2017).
[Crossref]

Scheithauer, B. W.

D. N. Louis, H. Ohgaki, O. D. Wiestler, W. K. Cavenee, P. C. Burger, A. Jouvet, B. W. Scheithauer, and P. Kleihues, “The 2007 WHO Classification of Tumours of the Central Nervous System,” WHO 2007, 97–109 (2007).

Schuster, D. M.

H. Akbari, L. V. Halig, D. M. Schuster, A. Osunkoya, V. Master, P. T. Nieh, G. Z. Chen, and B. Fei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 076005 (2012).
[Crossref] [PubMed]

Serranti, S.

F. Blanco, M. López-Mesas, S. Serranti, G. Bonifazi, J. Havel, and M. Valiente, “Hyperspectral imaging based method for fast characterization of kidney stone types,” J. Biomed. Opt. 17(7), 076027 (2012).
[Crossref] [PubMed]

Solomon, J. E.

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).
[PubMed]

Sveinsson, J. R.

J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification of hyperspectral data from urban areas based on extended morphological profiles,” IEEE Trans. Geosci. Remote Sens. 43(3), 480–491 (2005).
[Crossref]

Tanaka, N.

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE Trans. Biomed. Eng. 57(8), 2011–2017 (2010).
[Crossref] [PubMed]

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Blood vessel detection and artery-vein differentiation using hyperspectral imaging,” in Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 (2009), pp. 1461–1464.
[Crossref]

Tilton, J. C.

A. Plaza, J. Atli, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Remote Sensing of Environment Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Trianni, G.

A. Plaza, J. Atli, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Remote Sensing of Environment Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Valiente, M.

F. Blanco, M. López-Mesas, S. Serranti, G. Bonifazi, J. Havel, and M. Valiente, “Hyperspectral imaging based method for fast characterization of kidney stone types,” J. Biomed. Opt. 17(7), 076027 (2012).
[Crossref] [PubMed]

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).
[PubMed]

von Deimling, A.

D. N. Louis, A. Perry, G. Reifenberger, A. von Deimling, D. Figarella-Branger, W. K. Cavenee, H. Ohgaki, O. D. Wiestler, P. Kleihues, and D. W. Ellison, “The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary,” Acta Neuropathol. 131(6), 803–820 (2016).
[Crossref] [PubMed]

Wang, D.

R. Pike, G. Lu, D. Wang, Z. G. Chen, and B. Fei, “A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging,” IEEE Trans. Biomed. Eng. 63(3), 653–663 (2016).
[Crossref] [PubMed]

Wang, H.

Z. Liu, H. Wang, and Q. Li, “Tongue tumor detection in medical hyperspectral images,” Sensors (Basel) 12(1), 162–174 (2012).
[PubMed]

Wang, X.-R.

R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “LIBLINEAR: A Library for Large Linear Classification,” J. Mach. Learn. Res. 9, 1871–1874 (2008).

Wang, Y.

Q. Li, X. He, Y. Wang, H. Liu, D. Xu, and F. Guo, “Review of spectral imaging technology in biomedical engineering: achievements and challenges,” J. Biomed. Opt. 18(10), 100901 (2013).
[Crossref] [PubMed]

Wiestler, O. D.

D. N. Louis, A. Perry, G. Reifenberger, A. von Deimling, D. Figarella-Branger, W. K. Cavenee, H. Ohgaki, O. D. Wiestler, P. Kleihues, and D. W. Ellison, “The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary,” Acta Neuropathol. 131(6), 803–820 (2016).
[Crossref] [PubMed]

D. N. Louis, H. Ohgaki, O. D. Wiestler, W. K. Cavenee, P. C. Burger, A. Jouvet, B. W. Scheithauer, and P. Kleihues, “The 2007 WHO Classification of Tumours of the Central Nervous System,” WHO 2007, 97–109 (2007).

Xu, D.

Q. Li, X. He, Y. Wang, H. Liu, D. Xu, and F. Guo, “Review of spectral imaging technology in biomedical engineering: achievements and challenges,” J. Biomed. Opt. 18(10), 100901 (2013).
[Crossref] [PubMed]

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).

Zhou, M.

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).

Acta Neuropathol. (1)

D. N. Louis, A. Perry, G. Reifenberger, A. von Deimling, D. Figarella-Branger, W. K. Cavenee, H. Ohgaki, O. D. Wiestler, P. Kleihues, and D. W. Ellison, “The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary,” Acta Neuropathol. 131(6), 803–820 (2016).
[Crossref] [PubMed]

Biomed. Opt. Express (1)

Chemom. Intell. Lab. Syst. (1)

J. Burger and A. Gowen, “Data handling in hyperspectral image analysis,” Chemom. Intell. Lab. Syst. 108(1), 13–22 (2011).
[Crossref]

Diabetes Technol. Ther. (1)

R. Gillies, J. E. Freeman, L. C. Cancio, D. Brand, M. Hopmeier, and J. R. Mansfield, “Systemic Effects of Shock and Resuscitation Monitored by Visible Hyperspectral Imaging,” Diabetes Technol. Ther. 5(5), 847–855 (2003).
[Crossref] [PubMed]

Eye (Lond.) (1)

D. J. Mordant, I. Al-Abboud, G. Muyo, A. Gorman, A. Sallam, P. Ritchie, A. R. Harvey, and A. I. McNaught, “Spectral imaging of the retina,” Eye (Lond.) 25(3), 309–320 (2011).
[Crossref] [PubMed]

IEEE Geosci. Remote Sens. Mag. (1)

P. Ghamisi, J. Plaza, Y. Chen, J. Li, and A. J. Plaza, “Advanced Spectral Classifiers for Hyperspectral Images: A review,” IEEE Geosci. Remote Sens. Mag. 5(1), 8–32 (2017).
[Crossref]

IEEE J. Biomed. Health Informatics (1)

C. Lu and M. Mandal, “Toward Automatic Mitotic Cell Detection and Segmentation in Multispectral Histopathological Images,” IEEE J. Biomed. Health Informatics 18, 594–605 (2014).

IEEE Trans. Biomed. Eng. (2)

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE Trans. Biomed. Eng. 57(8), 2011–2017 (2010).
[Crossref] [PubMed]

R. Pike, G. Lu, D. Wang, Z. G. Chen, and B. Fei, “A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging,” IEEE Trans. Biomed. Eng. 63(3), 653–663 (2016).
[Crossref] [PubMed]

IEEE Trans. Geosci. Remote Sens. (2)

J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification of hyperspectral data from urban areas based on extended morphological profiles,” IEEE Trans. Geosci. Remote Sens. 43(3), 480–491 (2005).
[Crossref]

G. Camps-Valls and L. Bruzzone, “Kernel-based methods for hyperspectral image classification,” IEEE Trans. Geosci. Remote Sens. 43(6), 1351–1362 (2005).
[Crossref]

Image and Signal Processing (1)

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).

J. Biomed. Opt. (4)

H. Akbari, L. V. Halig, D. M. Schuster, A. Osunkoya, V. Master, P. T. Nieh, G. Z. Chen, and B. Fei, “Hyperspectral imaging and quantitative analysis for prostate cancer detection,” J. Biomed. Opt. 17(7), 076005 (2012).
[Crossref] [PubMed]

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

Q. Li, X. He, Y. Wang, H. Liu, D. Xu, and F. Guo, “Review of spectral imaging technology in biomedical engineering: achievements and challenges,” J. Biomed. Opt. 18(10), 100901 (2013).
[Crossref] [PubMed]

F. Blanco, M. López-Mesas, S. Serranti, G. Bonifazi, J. Havel, and M. Valiente, “Hyperspectral imaging based method for fast characterization of kidney stone types,” J. Biomed. Opt. 17(7), 076027 (2012).
[Crossref] [PubMed]

J. Mach. Learn. Res. (1)

R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “LIBLINEAR: A Library for Large Linear Classification,” J. Mach. Learn. Res. 9, 1871–1874 (2008).

J. Sens. (1)

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

Neuro-oncol. (1)

S. Ortega, H. Fabelo, R. Camacho, M. L. Plaza, G. M. Callico, R. Lazcano, D. Madroñal, R. Salvador, E. Juárez, and R. Sarmiento, “P03.18 Detection of human brain cancer in pathological slides using hyperspectral images,” Neuro-oncol. 19(suppl_3), iii37 (2017).
[Crossref]

Remote Sens. Environ. (2)

A. Plaza, J. Atli, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Remote Sensing of Environment Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

J. C.-W. Chan and D. Paelinckx, “Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery,” Remote Sens. Environ. 112(6), 2999–3011 (2008).
[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).
[PubMed]

Sensors (Basel) (1)

Z. Liu, H. Wang, and Q. Li, “Tongue tumor detection in medical hyperspectral images,” Sensors (Basel) 12(1), 162–174 (2012).
[PubMed]

WHO (1)

D. N. Louis, H. Ohgaki, O. D. Wiestler, W. K. Cavenee, P. C. Burger, A. Jouvet, B. W. Scheithauer, and P. Kleihues, “The 2007 WHO Classification of Tumours of the Central Nervous System,” WHO 2007, 97–109 (2007).

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E. E. Holt, M. Aikio, and V. teknillinen tutkimuskeskus, Hyperspectral Prism-Grating-Prism Imaging Spectrograph, VTT Julkaisuja (Technical Research Centre of Finland, 2001).

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 Proceedings - International Symposium on Biomedical Imaging (2016), Vol. 2016–June.
[Crossref]

M. Milanic, A. Bjorgan, M. Larsson, T. Strömberg, and L. L. Randeberg, “Detection of hypercholesterolemia using hyperspectral imaging of human skin,” in Clinical and Biomedical Spectroscopy and Imaging IV, J. Q. Brown and V. Deckert, eds. (SPIE-Intl Soc Optical Eng, 2015).

H. Fabelo, S. Ortega, S. Kabwama, G. M. Callico, D. Bulters, A. Szolna, J. F. Pineiro, and R. Sarmiento, “HELICoiD project: a new use of hyperspectral imaging for brain cancer detection in real-time during neurosurgical operations,” in Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2016, D. P. Bannon, ed. (SPIE-Intl Soc Optical Eng, 2016).

A. Bjorgan, M. Denstedt, M. Milanič, L. A. Paluchowski, and L. L. Randeberg, “Vessel contrast enhancement in hyperspectral images,” in Optical Biopsy XIII: Toward Real-Time Spectroscopic Imaging and Diagnosis, R. R. Alfano and S. G. Demos, eds. (SPIE-Intl Soc Optical Eng, 2015).

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Blood vessel detection and artery-vein differentiation using hyperspectral imaging,” in Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 (2009), pp. 1461–1464.
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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,” in Progress in Biomedical Optics and Imaging - Proceedings of SPIE (2012), Vol. 8317.

G. Lu, L. Halig, D. Wang, Z. G. Chen, and B. Fei, “Spectral-Spatial Classification Using Tensor Modeling for Cancer Detection with Hyperspectral Imaging.,” Proc. SPIE–the Int. Soc. Opt. Eng. 9034, 903413 (2014).

T. G. Dietterich, “Ensemble Methods in Machine Learning,” in Multiple Classifier Systems (Springer Nature, 2000), pp. 1–15.

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Wavelet-Based Compression and Segmentation of Hyperspectral Images in Surgery,” in Lecture Notes in Computer Science (Springer Nature, n.d.), pp. 142–149.

J. A. Richards, Remote Sensing Digital Image Analysis (Springer Berlin Heidelberg, 2012).

S. Lopez, “A Novel Use of Hyperspectral Images for Human Brain Cancer Detection using in-Vivo A Novel Use of Hyperspectral Images for Human Brain Cancer,” BIOSIGNALS 311–320 (2016).

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Figures (8)

Fig. 1
Fig. 1 Biological samples. (a) Pathological slides overview. (b) and (c) Diagnosed pathological slides with the tumor and normal tissue surrounded by red and blue color respectively.
Fig. 2
Fig. 2 Microscopic HS acquisition system. (a) System overview modified for HIS acquisition. (b) Designed flat base of the scanning platform. (c) Scanning platform attached to the microscope.
Fig. 3
Fig. 3 Synthetic RGB representations of a HS cube acquired from a pathological slide of (a) tumor tissue and (b) normal tissue. (c) Histological image of a brain tissue sample (100x).
Fig. 4
Fig. 4 Average spectral signatures of tumor tissue (red) and normal tissue (blue) and their respective standard deviation.
Fig. 5
Fig. 5 Processing framework block diagram.
Fig. 6
Fig. 6 Spectral signatures of a single tumor pixel in each calibration step. (a) Raw spectrum. (b) Reference spectrum. (c) Calibrated spectrum.
Fig. 7
Fig. 7 Spectral signatures of a single pixel in the band reduction step. (a) Selected operating bandwidth in the reference spectrum. (b) Calibrated spectral signature after the spectral band reduction.
Fig. 8
Fig. 8 Synthetic RGB representations of a HS cube acquired from a healthy area of pathological slide. (a) Synthetic RGB image without light pixels removal. (b) Binarized image. (c) Synthetic RGB image after the binarization process application to remove light pixels.

Tables (4)

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Table 1 Spectral signature labelled data set summary

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Table 2 Supervised classification results in CS1

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Table 3 Supervised classification results in CS2

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Table 4 Classification results in CS3

Equations (4)

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

I a b s = l o g I r a w I r e f
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 = T N T N + F P
O A = T o t a l S u c c e s s T o t a l P o p u l a t i o n

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