Abstract

The neuroanatomical morphology of nerve fibers is an important description for understanding the pathological aspects of nerves. Different from the traditional automatic nerve morphometry methods, a molecular hyperspectral imaging system based on an acousto-optic tunable filter (AOTF) was developed and used to identify unstained nerve histological sections. The hardware, software, and system performance of the imaging system are presented and discussed. The gray correction coefficient was used to calibrate the system’s spectral response and to remove the effects of noises and artifacts. A spatial–spectral kernel-based approach through the support vector machine formulation was proposed to identify nerve fibers. This algorithm can jointly use both the spatial and spectral information of molecular hyperspectral images for segmentation. Then, the morphological parameters such as fiber diameter, axon diameter, myelin sheath thickness, fiber area, and g-ratio were calculated and evaluated. Experimental results show that the hyperspectral-based method has the potential to recognize and measure the nerve fiber more accurately than traditional methods.

© 2013 Optical Society of America

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2012 (7)

Y. N. Guan, Q. L. Li, Y. T. Wang, H. Y. Liu, and Z. Q. Zhu, “Pathological leucocyte segmentation algorithm based on hyperspectral imaging technique,” Opt. Eng. 51, 053202 (2012).
[CrossRef]

H. Y. Liu, Q. L. Li, L. Xu, Y. T. Wang, J. G. Liu, and Y. Q. Xue, “Evaluation of erythropoietin efficacy on diabetic retinopathy based on molecular hyperspectral imaging (MHSI) system,” J. Infrared Millimeter Waves 31, 248–253 (2012).
[CrossRef]

S. J. Leavesley, N. Annamdevula, J. Boni, S. Stocker, K. Grant, B. Troyanovsky, T. C. Rich, and D. F. Alvarez, “Hyperspectral imaging microscopy for identification and quantitative analysis of fluorescently-labeled cells in highly autofluorescent tissue,” J. Biophotonics 5, 67–84 (2012).
[CrossRef]

L. Jun, J. M. Bioucas-Dias, and A. Plaza, “Spectral and spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields,” IEEE Trans. Geosci. Remote Sens. 50, 809–823 (2012).
[CrossRef]

M. Fauvel, J. Chanussot, and J. A. Benediktsson, “A spatial-spectral kernel-based approach for the classification of remote-sensing images,” Pattern Recogn. 45, 381–392 (2012).
[CrossRef]

K. Bernard, Y. Tarabalka, J. Angulo, J. Chanussot, and J. A. Benediktsson, “Spectral-spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach,” IEEE Trans. Image Process. 21, 2008–2021 (2012).
[CrossRef]

Q. Li, Z. Chen, X. He, Y. Wang, H. Liu, and Q. Xu, “Automatic identification and quantitative morphometry of unstained spinal nerve using molecular hyperspectral imaging technology,” Neurochem. Int. 61, 1375–1384 (2012).
[CrossRef]

2011 (2)

H. L. More, J. Chen, E. Gibson, J. M. Donelan, and M. F. Beg, “A semi-automated method for identifying and measuring myelinated nerve fibers in scanning electron microscope images,” J. Neurosci. Methods 201, 149–158 (2011).
[CrossRef]

G. Lauria, D. Cazzato, C. Porretta-Serapiglia, J. Casanova-Molla, M. Taiana, P. Penza, R. Lombardi, C. G. Faber, and I. S. J. Merkies, “Morphometry of dermal nerve fibers in human skin,” Neurology 77, 242–249 (2011).
[CrossRef]

2010 (2)

S. C. Marx, P. Kumar, S. Dhalapathy, K. Prasad, and C. A. Marx, “Microanatomical and immunohistochemical study of the human lateral antebrachial cutaneous nerve of forearm at the antecubital fossa and its clinical implications,” Clin. Anat. 23, 693–701 (2010).
[CrossRef]

X. M. Zhao, Z. K. Pan, J. Y. Wu, G. M. Zhou, and Y. J. Zeng, “Automatic identification and morphometry of optic nerve fibers in electron microscopy images,” Comput. Med. Imaging Graph. 34, 179–184 (2010).
[CrossRef]

2009 (2)

A. F. H. Goetz, “Three decades of hyperspectral remote sensing of the earth: a personal view,” Rem. Sensing Environ. 113, S5–S16 (2009).
[CrossRef]

Q. Li, J. Zhang, Y. Wang, and G. Xu, “Molecular spectral imaging system for quantitative immunohistochemical analysis of early diabetic retinopathy,” Appl. Spectrosc. 63, 1336–1342 (2009).
[CrossRef]

2008 (2)

S. A. R. Campos, L. S. Sanada, K. L. Sato, and V. P. S. Fazan, “Morphometry of saphenous nerve in young rats,” J. Neurosci. Methods 168, 8–14 (2008).
[CrossRef]

M. Fauvel, J. A. Benediktsson, J. Chanussot, and J. R. Sveinsson, “Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles,” IEEE Trans. Geosci. Remote Sens. 46, 3804–3814 (2008).
[CrossRef]

2007 (4)

B. Yver and R. Marion, “A theoretical framework for hyperspectral anomaly detection using spectral and spatial a priori information,” IEEE Geosci. Remote Sens. Lett. 4, 436–440 (2007).
[CrossRef]

D. A. Hunter, A. Moradzadeh, E. L. Whitlock, M. J. Brenner, T. A. Myckatyn, C. H. Wei, T. H. H. Tung, and S. E. Mackinnon, “Binary imaging analysis for comprehensive quantitative histomorphometry of peripheral nerve,” J. Neurosci. Methods 166, 116–124 (2007).
[CrossRef]

A. P. D. da Silva, C. E. R. Jordao, and V. P. S. Fazan, “Peripheral nerve morphometry: comparison between manual and semi-automated methods in the analysis of a small nerve,” J. Neurosci. Methods 159, 153–157 (2007).
[CrossRef]

H. Wang, S. D. Babacan, and K. Sayood, “Lossless hyperspectral-image compression using context-based conditional average,” IEEE Trans. Geosci. Remote Sens. 45, 4187–4193 (2007).
[CrossRef]

2006 (3)

L. Zhang, X. Huang, B. Huang, and P. Li, “A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery,” IEEE Trans. Geosci. Remote Sens. 44, 2950–2961 (2006).
[CrossRef]

F. Urso-Baiarda and A. O. Grobbelaar, “Practical nerve morphometry,” J. Neurosci. Methods 156, 333–341 (2006).
[CrossRef]

P. M. Kasili and T. Vo-Dinh, “Hyperspectral imaging system using acousto-optic tunable filter for flow cytometry applications,” Cytom. A 69A, 835–841 (2006).
[CrossRef]

2005 (1)

B. Weyn, M. Van Remoortere, R. Nuydens, T. Meert, and G. van de Wouwer, “A multiparametric assay for quantitative nerve regeneration evaluation,” J. Microsc. 219, 95–101 (2005).
[CrossRef]

2004 (1)

C. Vleggeert-Lankamp, R. J. van den Berg, H. K. P. Feirabend, E. Lakke, M. J. A. Malessy, and R. Thomeer, “Electrophysiology and morphometry of the A alpha- and A beta-fiber populations in the normal and regenerating rat sciatic nerve,” Exp. Neurol. 187, 337–349 (2004).
[CrossRef]

2000 (1)

E. Romero, O. Cuisenaire, J. F. Denef, J. Delbeke, B. Macq, and C. Veraart, “Automatic morphometry of nerve histological sections,” J. Neurosci. Methods 97, 111–122 (2000).
[CrossRef]

1997 (2)

E. S. Wachman, W. H. Niu, and D. L. Farkas, “AOTF microscope for imaging with increased speed and spectral versatility,” Biophys. J. 73, 1215–1222 (1997).
[CrossRef]

R. D. Shonat, E. S. Wachman, W. H. Niu, A. P. Koretsky, and D. L. Farkas, “Near-simultaneous hemoglobin saturation and oxygen tension maps in mouse brain using an AOTF microscope,” Biophys. J. 73, 1223–1231 (1997).
[CrossRef]

1996 (2)

E. S. Wachman, W. H. Niu, and D. L. Farkas, “Imaging acousto-optic tunable filter with 0.35-micrometer spatial resolution,” Appl. Opt. 35, 5220–5226 (1996).
[CrossRef]

F. Ying-Lun, J. C. K. Chan, and R. T. Chin, “Automated analysis of nerve-cell images using active contour models,” IEEE Trans. Med. Imaging 15, 353–368 (1996).
[CrossRef]

1994 (2)

1990 (1)

J. Astola, P. Haavisto, and Y. Neuvo, “Vector median filters,” Proc. IEEE 78, 678–689 (1990).
[CrossRef]

1985 (1)

A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for earth remote sensing,” Science 228, 1147–1153 (1985).
[CrossRef]

Alvarez, D. F.

S. J. Leavesley, N. Annamdevula, J. Boni, S. Stocker, K. Grant, B. Troyanovsky, T. C. Rich, and D. F. Alvarez, “Hyperspectral imaging microscopy for identification and quantitative analysis of fluorescently-labeled cells in highly autofluorescent tissue,” J. Biophotonics 5, 67–84 (2012).
[CrossRef]

Angulo, J.

K. Bernard, Y. Tarabalka, J. Angulo, J. Chanussot, and J. A. Benediktsson, “Spectral-spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach,” IEEE Trans. Image Process. 21, 2008–2021 (2012).
[CrossRef]

Annamdevula, N.

S. J. Leavesley, N. Annamdevula, J. Boni, S. Stocker, K. Grant, B. Troyanovsky, T. C. Rich, and D. F. Alvarez, “Hyperspectral imaging microscopy for identification and quantitative analysis of fluorescently-labeled cells in highly autofluorescent tissue,” J. Biophotonics 5, 67–84 (2012).
[CrossRef]

Astola, J.

J. Astola, P. Haavisto, and Y. Neuvo, “Vector median filters,” Proc. IEEE 78, 678–689 (1990).
[CrossRef]

Babacan, S. D.

H. Wang, S. D. Babacan, and K. Sayood, “Lossless hyperspectral-image compression using context-based conditional average,” IEEE Trans. Geosci. Remote Sens. 45, 4187–4193 (2007).
[CrossRef]

Beg, M. F.

H. L. More, J. Chen, E. Gibson, J. M. Donelan, and M. F. Beg, “A semi-automated method for identifying and measuring myelinated nerve fibers in scanning electron microscope images,” J. Neurosci. Methods 201, 149–158 (2011).
[CrossRef]

Benediktsson, J. A.

K. Bernard, Y. Tarabalka, J. Angulo, J. Chanussot, and J. A. Benediktsson, “Spectral-spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach,” IEEE Trans. Image Process. 21, 2008–2021 (2012).
[CrossRef]

M. Fauvel, J. Chanussot, and J. A. Benediktsson, “A spatial-spectral kernel-based approach for the classification of remote-sensing images,” Pattern Recogn. 45, 381–392 (2012).
[CrossRef]

M. Fauvel, J. A. Benediktsson, J. Chanussot, and J. R. Sveinsson, “Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles,” IEEE Trans. Geosci. Remote Sens. 46, 3804–3814 (2008).
[CrossRef]

M. Fauvel, J. Chanussot, and J. A. Benediktsson, “Evaluation of kernels for multiclass classification of hyperspectral remote sensing data,” in IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 2006), pp. 813–816.

Bernard, K.

K. Bernard, Y. Tarabalka, J. Angulo, J. Chanussot, and J. A. Benediktsson, “Spectral-spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach,” IEEE Trans. Image Process. 21, 2008–2021 (2012).
[CrossRef]

Bioucas-Dias, J. M.

L. Jun, J. M. Bioucas-Dias, and A. Plaza, “Spectral and spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields,” IEEE Trans. Geosci. Remote Sens. 50, 809–823 (2012).
[CrossRef]

Boni, J.

S. J. Leavesley, N. Annamdevula, J. Boni, S. Stocker, K. Grant, B. Troyanovsky, T. C. Rich, and D. F. Alvarez, “Hyperspectral imaging microscopy for identification and quantitative analysis of fluorescently-labeled cells in highly autofluorescent tissue,” J. Biophotonics 5, 67–84 (2012).
[CrossRef]

Brenner, M. J.

D. A. Hunter, A. Moradzadeh, E. L. Whitlock, M. J. Brenner, T. A. Myckatyn, C. H. Wei, T. H. H. Tung, and S. E. Mackinnon, “Binary imaging analysis for comprehensive quantitative histomorphometry of peripheral nerve,” J. Neurosci. Methods 166, 116–124 (2007).
[CrossRef]

Campos, S. A. R.

S. A. R. Campos, L. S. Sanada, K. L. Sato, and V. P. S. Fazan, “Morphometry of saphenous nerve in young rats,” J. Neurosci. Methods 168, 8–14 (2008).
[CrossRef]

Casanova-Molla, J.

G. Lauria, D. Cazzato, C. Porretta-Serapiglia, J. Casanova-Molla, M. Taiana, P. Penza, R. Lombardi, C. G. Faber, and I. S. J. Merkies, “Morphometry of dermal nerve fibers in human skin,” Neurology 77, 242–249 (2011).
[CrossRef]

Cazzato, D.

G. Lauria, D. Cazzato, C. Porretta-Serapiglia, J. Casanova-Molla, M. Taiana, P. Penza, R. Lombardi, C. G. Faber, and I. S. J. Merkies, “Morphometry of dermal nerve fibers in human skin,” Neurology 77, 242–249 (2011).
[CrossRef]

Chan, J. C. K.

F. Ying-Lun, J. C. K. Chan, and R. T. Chin, “Automated analysis of nerve-cell images using active contour models,” IEEE Trans. Med. Imaging 15, 353–368 (1996).
[CrossRef]

Chanussot, J.

M. Fauvel, J. Chanussot, and J. A. Benediktsson, “A spatial-spectral kernel-based approach for the classification of remote-sensing images,” Pattern Recogn. 45, 381–392 (2012).
[CrossRef]

K. Bernard, Y. Tarabalka, J. Angulo, J. Chanussot, and J. A. Benediktsson, “Spectral-spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach,” IEEE Trans. Image Process. 21, 2008–2021 (2012).
[CrossRef]

M. Fauvel, J. A. Benediktsson, J. Chanussot, and J. R. Sveinsson, “Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles,” IEEE Trans. Geosci. Remote Sens. 46, 3804–3814 (2008).
[CrossRef]

M. Fauvel, J. Chanussot, and J. A. Benediktsson, “Evaluation of kernels for multiclass classification of hyperspectral remote sensing data,” in IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 2006), pp. 813–816.

Chen, J.

H. L. More, J. Chen, E. Gibson, J. M. Donelan, and M. F. Beg, “A semi-automated method for identifying and measuring myelinated nerve fibers in scanning electron microscope images,” J. Neurosci. Methods 201, 149–158 (2011).
[CrossRef]

Chen, Z.

Q. Li, Z. Chen, X. He, Y. Wang, H. Liu, and Q. Xu, “Automatic identification and quantitative morphometry of unstained spinal nerve using molecular hyperspectral imaging technology,” Neurochem. Int. 61, 1375–1384 (2012).
[CrossRef]

Chin, R. T.

F. Ying-Lun, J. C. K. Chan, and R. T. Chin, “Automated analysis of nerve-cell images using active contour models,” IEEE Trans. Med. Imaging 15, 353–368 (1996).
[CrossRef]

Cuisenaire, O.

E. Romero, O. Cuisenaire, J. F. Denef, J. Delbeke, B. Macq, and C. Veraart, “Automatic morphometry of nerve histological sections,” J. Neurosci. Methods 97, 111–122 (2000).
[CrossRef]

da Silva, A. P. D.

A. P. D. da Silva, C. E. R. Jordao, and V. P. S. Fazan, “Peripheral nerve morphometry: comparison between manual and semi-automated methods in the analysis of a small nerve,” J. Neurosci. Methods 159, 153–157 (2007).
[CrossRef]

Delbeke, J.

E. Romero, O. Cuisenaire, J. F. Denef, J. Delbeke, B. Macq, and C. Veraart, “Automatic morphometry of nerve histological sections,” J. Neurosci. Methods 97, 111–122 (2000).
[CrossRef]

Denef, J. F.

E. Romero, O. Cuisenaire, J. F. Denef, J. Delbeke, B. Macq, and C. Veraart, “Automatic morphometry of nerve histological sections,” J. Neurosci. Methods 97, 111–122 (2000).
[CrossRef]

Dhalapathy, S.

S. C. Marx, P. Kumar, S. Dhalapathy, K. Prasad, and C. A. Marx, “Microanatomical and immunohistochemical study of the human lateral antebrachial cutaneous nerve of forearm at the antecubital fossa and its clinical implications,” Clin. Anat. 23, 693–701 (2010).
[CrossRef]

Donelan, J. M.

H. L. More, J. Chen, E. Gibson, J. M. Donelan, and M. F. Beg, “A semi-automated method for identifying and measuring myelinated nerve fibers in scanning electron microscope images,” J. Neurosci. Methods 201, 149–158 (2011).
[CrossRef]

Faber, C. G.

G. Lauria, D. Cazzato, C. Porretta-Serapiglia, J. Casanova-Molla, M. Taiana, P. Penza, R. Lombardi, C. G. Faber, and I. S. J. Merkies, “Morphometry of dermal nerve fibers in human skin,” Neurology 77, 242–249 (2011).
[CrossRef]

Farkas, D. L.

R. D. Shonat, E. S. Wachman, W. H. Niu, A. P. Koretsky, and D. L. Farkas, “Near-simultaneous hemoglobin saturation and oxygen tension maps in mouse brain using an AOTF microscope,” Biophys. J. 73, 1223–1231 (1997).
[CrossRef]

E. S. Wachman, W. H. Niu, and D. L. Farkas, “AOTF microscope for imaging with increased speed and spectral versatility,” Biophys. J. 73, 1215–1222 (1997).
[CrossRef]

E. S. Wachman, W. H. Niu, and D. L. Farkas, “Imaging acousto-optic tunable filter with 0.35-micrometer spatial resolution,” Appl. Opt. 35, 5220–5226 (1996).
[CrossRef]

Fauvel, M.

M. Fauvel, J. Chanussot, and J. A. Benediktsson, “A spatial-spectral kernel-based approach for the classification of remote-sensing images,” Pattern Recogn. 45, 381–392 (2012).
[CrossRef]

M. Fauvel, J. A. Benediktsson, J. Chanussot, and J. R. Sveinsson, “Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles,” IEEE Trans. Geosci. Remote Sens. 46, 3804–3814 (2008).
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M. Fauvel, J. Chanussot, and J. A. Benediktsson, “Evaluation of kernels for multiclass classification of hyperspectral remote sensing data,” in IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 2006), pp. 813–816.

Fazan, V. P. S.

S. A. R. Campos, L. S. Sanada, K. L. Sato, and V. P. S. Fazan, “Morphometry of saphenous nerve in young rats,” J. Neurosci. Methods 168, 8–14 (2008).
[CrossRef]

A. P. D. da Silva, C. E. R. Jordao, and V. P. S. Fazan, “Peripheral nerve morphometry: comparison between manual and semi-automated methods in the analysis of a small nerve,” J. Neurosci. Methods 159, 153–157 (2007).
[CrossRef]

Feirabend, H. K. P.

C. Vleggeert-Lankamp, R. J. van den Berg, H. K. P. Feirabend, E. Lakke, M. J. A. Malessy, and R. Thomeer, “Electrophysiology and morphometry of the A alpha- and A beta-fiber populations in the normal and regenerating rat sciatic nerve,” Exp. Neurol. 187, 337–349 (2004).
[CrossRef]

Gibson, E.

H. L. More, J. Chen, E. Gibson, J. M. Donelan, and M. F. Beg, “A semi-automated method for identifying and measuring myelinated nerve fibers in scanning electron microscope images,” J. Neurosci. Methods 201, 149–158 (2011).
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Goetz, A. F. H.

A. F. H. Goetz, “Three decades of hyperspectral remote sensing of the earth: a personal view,” Rem. Sensing Environ. 113, S5–S16 (2009).
[CrossRef]

A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for earth remote sensing,” Science 228, 1147–1153 (1985).
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A. F. H. Goetz and V. Srivastava, “Mineralogical mapping in the Cuprite mining district, Nevada,” in Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop (JPL, 1985), pp. 22–29.

Grant, K.

S. J. Leavesley, N. Annamdevula, J. Boni, S. Stocker, K. Grant, B. Troyanovsky, T. C. Rich, and D. F. Alvarez, “Hyperspectral imaging microscopy for identification and quantitative analysis of fluorescently-labeled cells in highly autofluorescent tissue,” J. Biophotonics 5, 67–84 (2012).
[CrossRef]

Grobbelaar, A. O.

F. Urso-Baiarda and A. O. Grobbelaar, “Practical nerve morphometry,” J. Neurosci. Methods 156, 333–341 (2006).
[CrossRef]

Guan, Y. N.

Y. N. Guan, Q. L. Li, Y. T. Wang, H. Y. Liu, and Z. Q. Zhu, “Pathological leucocyte segmentation algorithm based on hyperspectral imaging technique,” Opt. Eng. 51, 053202 (2012).
[CrossRef]

Haavisto, P.

J. Astola, P. Haavisto, and Y. Neuvo, “Vector median filters,” Proc. IEEE 78, 678–689 (1990).
[CrossRef]

He, X.

Q. Li, Z. Chen, X. He, Y. Wang, H. Liu, and Q. Xu, “Automatic identification and quantitative morphometry of unstained spinal nerve using molecular hyperspectral imaging technology,” Neurochem. Int. 61, 1375–1384 (2012).
[CrossRef]

Hoyt, C. C.

Huang, B.

L. Zhang, X. Huang, B. Huang, and P. Li, “A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery,” IEEE Trans. Geosci. Remote Sens. 44, 2950–2961 (2006).
[CrossRef]

Huang, X.

L. Zhang, X. Huang, B. Huang, and P. Li, “A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery,” IEEE Trans. Geosci. Remote Sens. 44, 2950–2961 (2006).
[CrossRef]

Hunter, D. A.

D. A. Hunter, A. Moradzadeh, E. L. Whitlock, M. J. Brenner, T. A. Myckatyn, C. H. Wei, T. H. H. Tung, and S. E. Mackinnon, “Binary imaging analysis for comprehensive quantitative histomorphometry of peripheral nerve,” J. Neurosci. Methods 166, 116–124 (2007).
[CrossRef]

Jordao, C. E. R.

A. P. D. da Silva, C. E. R. Jordao, and V. P. S. Fazan, “Peripheral nerve morphometry: comparison between manual and semi-automated methods in the analysis of a small nerve,” J. Neurosci. Methods 159, 153–157 (2007).
[CrossRef]

Jun, L.

L. Jun, J. M. Bioucas-Dias, and A. Plaza, “Spectral and spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields,” IEEE Trans. Geosci. Remote Sens. 50, 809–823 (2012).
[CrossRef]

Kasili, P. M.

P. M. Kasili and T. Vo-Dinh, “Hyperspectral imaging system using acousto-optic tunable filter for flow cytometry applications,” Cytom. A 69A, 835–841 (2006).
[CrossRef]

Koretsky, A. P.

R. D. Shonat, E. S. Wachman, W. H. Niu, A. P. Koretsky, and D. L. Farkas, “Near-simultaneous hemoglobin saturation and oxygen tension maps in mouse brain using an AOTF microscope,” Biophys. J. 73, 1223–1231 (1997).
[CrossRef]

Kumar, P.

S. C. Marx, P. Kumar, S. Dhalapathy, K. Prasad, and C. A. Marx, “Microanatomical and immunohistochemical study of the human lateral antebrachial cutaneous nerve of forearm at the antecubital fossa and its clinical implications,” Clin. Anat. 23, 693–701 (2010).
[CrossRef]

Lakke, E.

C. Vleggeert-Lankamp, R. J. van den Berg, H. K. P. Feirabend, E. Lakke, M. J. A. Malessy, and R. Thomeer, “Electrophysiology and morphometry of the A alpha- and A beta-fiber populations in the normal and regenerating rat sciatic nerve,” Exp. Neurol. 187, 337–349 (2004).
[CrossRef]

Lauria, G.

G. Lauria, D. Cazzato, C. Porretta-Serapiglia, J. Casanova-Molla, M. Taiana, P. Penza, R. Lombardi, C. G. Faber, and I. S. J. Merkies, “Morphometry of dermal nerve fibers in human skin,” Neurology 77, 242–249 (2011).
[CrossRef]

Leavesley, S. J.

S. J. Leavesley, N. Annamdevula, J. Boni, S. Stocker, K. Grant, B. Troyanovsky, T. C. Rich, and D. F. Alvarez, “Hyperspectral imaging microscopy for identification and quantitative analysis of fluorescently-labeled cells in highly autofluorescent tissue,” J. Biophotonics 5, 67–84 (2012).
[CrossRef]

Levin, I. W.

Li, P.

L. Zhang, X. Huang, B. Huang, and P. Li, “A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery,” IEEE Trans. Geosci. Remote Sens. 44, 2950–2961 (2006).
[CrossRef]

Li, Q.

Q. Li, Z. Chen, X. He, Y. Wang, H. Liu, and Q. Xu, “Automatic identification and quantitative morphometry of unstained spinal nerve using molecular hyperspectral imaging technology,” Neurochem. Int. 61, 1375–1384 (2012).
[CrossRef]

Q. Li, J. Zhang, Y. Wang, and G. Xu, “Molecular spectral imaging system for quantitative immunohistochemical analysis of early diabetic retinopathy,” Appl. Spectrosc. 63, 1336–1342 (2009).
[CrossRef]

Li, Q. L.

H. Y. Liu, Q. L. Li, L. Xu, Y. T. Wang, J. G. Liu, and Y. Q. Xue, “Evaluation of erythropoietin efficacy on diabetic retinopathy based on molecular hyperspectral imaging (MHSI) system,” J. Infrared Millimeter Waves 31, 248–253 (2012).
[CrossRef]

Y. N. Guan, Q. L. Li, Y. T. Wang, H. Y. Liu, and Z. Q. Zhu, “Pathological leucocyte segmentation algorithm based on hyperspectral imaging technique,” Opt. Eng. 51, 053202 (2012).
[CrossRef]

Liu, H.

Q. Li, Z. Chen, X. He, Y. Wang, H. Liu, and Q. Xu, “Automatic identification and quantitative morphometry of unstained spinal nerve using molecular hyperspectral imaging technology,” Neurochem. Int. 61, 1375–1384 (2012).
[CrossRef]

Liu, H. Y.

Y. N. Guan, Q. L. Li, Y. T. Wang, H. Y. Liu, and Z. Q. Zhu, “Pathological leucocyte segmentation algorithm based on hyperspectral imaging technique,” Opt. Eng. 51, 053202 (2012).
[CrossRef]

H. Y. Liu, Q. L. Li, L. Xu, Y. T. Wang, J. G. Liu, and Y. Q. Xue, “Evaluation of erythropoietin efficacy on diabetic retinopathy based on molecular hyperspectral imaging (MHSI) system,” J. Infrared Millimeter Waves 31, 248–253 (2012).
[CrossRef]

Liu, J. G.

H. Y. Liu, Q. L. Li, L. Xu, Y. T. Wang, J. G. Liu, and Y. Q. Xue, “Evaluation of erythropoietin efficacy on diabetic retinopathy based on molecular hyperspectral imaging (MHSI) system,” J. Infrared Millimeter Waves 31, 248–253 (2012).
[CrossRef]

Lombardi, R.

G. Lauria, D. Cazzato, C. Porretta-Serapiglia, J. Casanova-Molla, M. Taiana, P. Penza, R. Lombardi, C. G. Faber, and I. S. J. Merkies, “Morphometry of dermal nerve fibers in human skin,” Neurology 77, 242–249 (2011).
[CrossRef]

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D. A. Hunter, A. Moradzadeh, E. L. Whitlock, M. J. Brenner, T. A. Myckatyn, C. H. Wei, T. H. H. Tung, and S. E. Mackinnon, “Binary imaging analysis for comprehensive quantitative histomorphometry of peripheral nerve,” J. Neurosci. Methods 166, 116–124 (2007).
[CrossRef]

Macq, B.

E. Romero, O. Cuisenaire, J. F. Denef, J. Delbeke, B. Macq, and C. Veraart, “Automatic morphometry of nerve histological sections,” J. Neurosci. Methods 97, 111–122 (2000).
[CrossRef]

Malessy, M. J. A.

C. Vleggeert-Lankamp, R. J. van den Berg, H. K. P. Feirabend, E. Lakke, M. J. A. Malessy, and R. Thomeer, “Electrophysiology and morphometry of the A alpha- and A beta-fiber populations in the normal and regenerating rat sciatic nerve,” Exp. Neurol. 187, 337–349 (2004).
[CrossRef]

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B. Yver and R. Marion, “A theoretical framework for hyperspectral anomaly detection using spectral and spatial a priori information,” IEEE Geosci. Remote Sens. Lett. 4, 436–440 (2007).
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A. Plaza, P. Martinez, J. Plaza, and R. Perez, “Spatial/spectral analysis of hyperspectral image data,” in 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data (IEEE, 2004), pp. 298–307.

Marx, C. A.

S. C. Marx, P. Kumar, S. Dhalapathy, K. Prasad, and C. A. Marx, “Microanatomical and immunohistochemical study of the human lateral antebrachial cutaneous nerve of forearm at the antecubital fossa and its clinical implications,” Clin. Anat. 23, 693–701 (2010).
[CrossRef]

Marx, S. C.

S. C. Marx, P. Kumar, S. Dhalapathy, K. Prasad, and C. A. Marx, “Microanatomical and immunohistochemical study of the human lateral antebrachial cutaneous nerve of forearm at the antecubital fossa and its clinical implications,” Clin. Anat. 23, 693–701 (2010).
[CrossRef]

Meert, T.

B. Weyn, M. Van Remoortere, R. Nuydens, T. Meert, and G. van de Wouwer, “A multiparametric assay for quantitative nerve regeneration evaluation,” J. Microsc. 219, 95–101 (2005).
[CrossRef]

Merkies, I. S. J.

G. Lauria, D. Cazzato, C. Porretta-Serapiglia, J. Casanova-Molla, M. Taiana, P. Penza, R. Lombardi, C. G. Faber, and I. S. J. Merkies, “Morphometry of dermal nerve fibers in human skin,” Neurology 77, 242–249 (2011).
[CrossRef]

Moradzadeh, A.

D. A. Hunter, A. Moradzadeh, E. L. Whitlock, M. J. Brenner, T. A. Myckatyn, C. H. Wei, T. H. H. Tung, and S. E. Mackinnon, “Binary imaging analysis for comprehensive quantitative histomorphometry of peripheral nerve,” J. Neurosci. Methods 166, 116–124 (2007).
[CrossRef]

More, H. L.

H. L. More, J. Chen, E. Gibson, J. M. Donelan, and M. F. Beg, “A semi-automated method for identifying and measuring myelinated nerve fibers in scanning electron microscope images,” J. Neurosci. Methods 201, 149–158 (2011).
[CrossRef]

Morris, H. R.

Myckatyn, T. A.

D. A. Hunter, A. Moradzadeh, E. L. Whitlock, M. J. Brenner, T. A. Myckatyn, C. H. Wei, T. H. H. Tung, and S. E. Mackinnon, “Binary imaging analysis for comprehensive quantitative histomorphometry of peripheral nerve,” J. Neurosci. Methods 166, 116–124 (2007).
[CrossRef]

Neil Lewis, E.

Neuvo, Y.

J. Astola, P. Haavisto, and Y. Neuvo, “Vector median filters,” Proc. IEEE 78, 678–689 (1990).
[CrossRef]

Niu, W. H.

R. D. Shonat, E. S. Wachman, W. H. Niu, A. P. Koretsky, and D. L. Farkas, “Near-simultaneous hemoglobin saturation and oxygen tension maps in mouse brain using an AOTF microscope,” Biophys. J. 73, 1223–1231 (1997).
[CrossRef]

E. S. Wachman, W. H. Niu, and D. L. Farkas, “AOTF microscope for imaging with increased speed and spectral versatility,” Biophys. J. 73, 1215–1222 (1997).
[CrossRef]

E. S. Wachman, W. H. Niu, and D. L. Farkas, “Imaging acousto-optic tunable filter with 0.35-micrometer spatial resolution,” Appl. Opt. 35, 5220–5226 (1996).
[CrossRef]

Nuydens, R.

B. Weyn, M. Van Remoortere, R. Nuydens, T. Meert, and G. van de Wouwer, “A multiparametric assay for quantitative nerve regeneration evaluation,” J. Microsc. 219, 95–101 (2005).
[CrossRef]

Pan, Z. K.

X. M. Zhao, Z. K. Pan, J. Y. Wu, G. M. Zhou, and Y. J. Zeng, “Automatic identification and morphometry of optic nerve fibers in electron microscopy images,” Comput. Med. Imaging Graph. 34, 179–184 (2010).
[CrossRef]

Penza, P.

G. Lauria, D. Cazzato, C. Porretta-Serapiglia, J. Casanova-Molla, M. Taiana, P. Penza, R. Lombardi, C. G. Faber, and I. S. J. Merkies, “Morphometry of dermal nerve fibers in human skin,” Neurology 77, 242–249 (2011).
[CrossRef]

Perez, R.

A. Plaza, P. Martinez, J. Plaza, and R. Perez, “Spatial/spectral analysis of hyperspectral image data,” in 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data (IEEE, 2004), pp. 298–307.

Plaza, A.

L. Jun, J. M. Bioucas-Dias, and A. Plaza, “Spectral and spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields,” IEEE Trans. Geosci. Remote Sens. 50, 809–823 (2012).
[CrossRef]

A. Plaza, P. Martinez, J. Plaza, and R. Perez, “Spatial/spectral analysis of hyperspectral image data,” in 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data (IEEE, 2004), pp. 298–307.

Plaza, A. J.

A. J. Plaza, “Parallel spatial-spectral processing of hyperspectral images,” in Computational Intelligence for Remote Sensing, M. Grana and R. J. Duro, eds. (Springer-Verlag Berlin Heidelberg, 2008), pp. 163–192.

Plaza, J.

A. Plaza, P. Martinez, J. Plaza, and R. Perez, “Spatial/spectral analysis of hyperspectral image data,” in 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data (IEEE, 2004), pp. 298–307.

Porretta-Serapiglia, C.

G. Lauria, D. Cazzato, C. Porretta-Serapiglia, J. Casanova-Molla, M. Taiana, P. Penza, R. Lombardi, C. G. Faber, and I. S. J. Merkies, “Morphometry of dermal nerve fibers in human skin,” Neurology 77, 242–249 (2011).
[CrossRef]

Prasad, K.

S. C. Marx, P. Kumar, S. Dhalapathy, K. Prasad, and C. A. Marx, “Microanatomical and immunohistochemical study of the human lateral antebrachial cutaneous nerve of forearm at the antecubital fossa and its clinical implications,” Clin. Anat. 23, 693–701 (2010).
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J. Qin, “Hyperspectral imaging instruments,” in Hyperspectral Imaging for Food Quality Analysis and Control, S. Professor Da-Wen, ed. (Academic, 2010), pp. 129–172.

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S. J. Leavesley, N. Annamdevula, J. Boni, S. Stocker, K. Grant, B. Troyanovsky, T. C. Rich, and D. F. Alvarez, “Hyperspectral imaging microscopy for identification and quantitative analysis of fluorescently-labeled cells in highly autofluorescent tissue,” J. Biophotonics 5, 67–84 (2012).
[CrossRef]

Rock, B. N.

A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for earth remote sensing,” Science 228, 1147–1153 (1985).
[CrossRef]

Romero, E.

E. Romero, O. Cuisenaire, J. F. Denef, J. Delbeke, B. Macq, and C. Veraart, “Automatic morphometry of nerve histological sections,” J. Neurosci. Methods 97, 111–122 (2000).
[CrossRef]

Sanada, L. S.

S. A. R. Campos, L. S. Sanada, K. L. Sato, and V. P. S. Fazan, “Morphometry of saphenous nerve in young rats,” J. Neurosci. Methods 168, 8–14 (2008).
[CrossRef]

Sato, K. L.

S. A. R. Campos, L. S. Sanada, K. L. Sato, and V. P. S. Fazan, “Morphometry of saphenous nerve in young rats,” J. Neurosci. Methods 168, 8–14 (2008).
[CrossRef]

Sayood, K.

H. Wang, S. D. Babacan, and K. Sayood, “Lossless hyperspectral-image compression using context-based conditional average,” IEEE Trans. Geosci. Remote Sens. 45, 4187–4193 (2007).
[CrossRef]

Shonat, R. D.

R. D. Shonat, E. S. Wachman, W. H. Niu, A. P. Koretsky, and D. L. Farkas, “Near-simultaneous hemoglobin saturation and oxygen tension maps in mouse brain using an AOTF microscope,” Biophys. J. 73, 1223–1231 (1997).
[CrossRef]

Solomon, J. E.

A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for earth remote sensing,” Science 228, 1147–1153 (1985).
[CrossRef]

Srivastava, V.

A. F. H. Goetz and V. Srivastava, “Mineralogical mapping in the Cuprite mining district, Nevada,” in Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop (JPL, 1985), pp. 22–29.

Stocker, S.

S. J. Leavesley, N. Annamdevula, J. Boni, S. Stocker, K. Grant, B. Troyanovsky, T. C. Rich, and D. F. Alvarez, “Hyperspectral imaging microscopy for identification and quantitative analysis of fluorescently-labeled cells in highly autofluorescent tissue,” J. Biophotonics 5, 67–84 (2012).
[CrossRef]

Sveinsson, J. R.

M. Fauvel, J. A. Benediktsson, J. Chanussot, and J. R. Sveinsson, “Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles,” IEEE Trans. Geosci. Remote Sens. 46, 3804–3814 (2008).
[CrossRef]

Taiana, M.

G. Lauria, D. Cazzato, C. Porretta-Serapiglia, J. Casanova-Molla, M. Taiana, P. Penza, R. Lombardi, C. G. Faber, and I. S. J. Merkies, “Morphometry of dermal nerve fibers in human skin,” Neurology 77, 242–249 (2011).
[CrossRef]

Tarabalka, Y.

K. Bernard, Y. Tarabalka, J. Angulo, J. Chanussot, and J. A. Benediktsson, “Spectral-spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach,” IEEE Trans. Image Process. 21, 2008–2021 (2012).
[CrossRef]

Thomeer, R.

C. Vleggeert-Lankamp, R. J. van den Berg, H. K. P. Feirabend, E. Lakke, M. J. A. Malessy, and R. Thomeer, “Electrophysiology and morphometry of the A alpha- and A beta-fiber populations in the normal and regenerating rat sciatic nerve,” Exp. Neurol. 187, 337–349 (2004).
[CrossRef]

Treado, P. J.

Troyanovsky, B.

S. J. Leavesley, N. Annamdevula, J. Boni, S. Stocker, K. Grant, B. Troyanovsky, T. C. Rich, and D. F. Alvarez, “Hyperspectral imaging microscopy for identification and quantitative analysis of fluorescently-labeled cells in highly autofluorescent tissue,” J. Biophotonics 5, 67–84 (2012).
[CrossRef]

Tung, T. H. H.

D. A. Hunter, A. Moradzadeh, E. L. Whitlock, M. J. Brenner, T. A. Myckatyn, C. H. Wei, T. H. H. Tung, and S. E. Mackinnon, “Binary imaging analysis for comprehensive quantitative histomorphometry of peripheral nerve,” J. Neurosci. Methods 166, 116–124 (2007).
[CrossRef]

Urso-Baiarda, F.

F. Urso-Baiarda and A. O. Grobbelaar, “Practical nerve morphometry,” J. Neurosci. Methods 156, 333–341 (2006).
[CrossRef]

van de Wouwer, G.

B. Weyn, M. Van Remoortere, R. Nuydens, T. Meert, and G. van de Wouwer, “A multiparametric assay for quantitative nerve regeneration evaluation,” J. Microsc. 219, 95–101 (2005).
[CrossRef]

van den Berg, R. J.

C. Vleggeert-Lankamp, R. J. van den Berg, H. K. P. Feirabend, E. Lakke, M. J. A. Malessy, and R. Thomeer, “Electrophysiology and morphometry of the A alpha- and A beta-fiber populations in the normal and regenerating rat sciatic nerve,” Exp. Neurol. 187, 337–349 (2004).
[CrossRef]

Van Remoortere, M.

B. Weyn, M. Van Remoortere, R. Nuydens, T. Meert, and G. van de Wouwer, “A multiparametric assay for quantitative nerve regeneration evaluation,” J. Microsc. 219, 95–101 (2005).
[CrossRef]

Vane, G.

A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for earth remote sensing,” Science 228, 1147–1153 (1985).
[CrossRef]

Veraart, C.

E. Romero, O. Cuisenaire, J. F. Denef, J. Delbeke, B. Macq, and C. Veraart, “Automatic morphometry of nerve histological sections,” J. Neurosci. Methods 97, 111–122 (2000).
[CrossRef]

Vleggeert-Lankamp, C.

C. Vleggeert-Lankamp, R. J. van den Berg, H. K. P. Feirabend, E. Lakke, M. J. A. Malessy, and R. Thomeer, “Electrophysiology and morphometry of the A alpha- and A beta-fiber populations in the normal and regenerating rat sciatic nerve,” Exp. Neurol. 187, 337–349 (2004).
[CrossRef]

Vo-Dinh, T.

P. M. Kasili and T. Vo-Dinh, “Hyperspectral imaging system using acousto-optic tunable filter for flow cytometry applications,” Cytom. A 69A, 835–841 (2006).
[CrossRef]

Wachman, E. S.

E. S. Wachman, W. H. Niu, and D. L. Farkas, “AOTF microscope for imaging with increased speed and spectral versatility,” Biophys. J. 73, 1215–1222 (1997).
[CrossRef]

R. D. Shonat, E. S. Wachman, W. H. Niu, A. P. Koretsky, and D. L. Farkas, “Near-simultaneous hemoglobin saturation and oxygen tension maps in mouse brain using an AOTF microscope,” Biophys. J. 73, 1223–1231 (1997).
[CrossRef]

E. S. Wachman, W. H. Niu, and D. L. Farkas, “Imaging acousto-optic tunable filter with 0.35-micrometer spatial resolution,” Appl. Opt. 35, 5220–5226 (1996).
[CrossRef]

Wang, H.

H. Wang, S. D. Babacan, and K. Sayood, “Lossless hyperspectral-image compression using context-based conditional average,” IEEE Trans. Geosci. Remote Sens. 45, 4187–4193 (2007).
[CrossRef]

Wang, Y.

Q. Li, Z. Chen, X. He, Y. Wang, H. Liu, and Q. Xu, “Automatic identification and quantitative morphometry of unstained spinal nerve using molecular hyperspectral imaging technology,” Neurochem. Int. 61, 1375–1384 (2012).
[CrossRef]

Q. Li, J. Zhang, Y. Wang, and G. Xu, “Molecular spectral imaging system for quantitative immunohistochemical analysis of early diabetic retinopathy,” Appl. Spectrosc. 63, 1336–1342 (2009).
[CrossRef]

Wang, Y. T.

H. Y. Liu, Q. L. Li, L. Xu, Y. T. Wang, J. G. Liu, and Y. Q. Xue, “Evaluation of erythropoietin efficacy on diabetic retinopathy based on molecular hyperspectral imaging (MHSI) system,” J. Infrared Millimeter Waves 31, 248–253 (2012).
[CrossRef]

Y. N. Guan, Q. L. Li, Y. T. Wang, H. Y. Liu, and Z. Q. Zhu, “Pathological leucocyte segmentation algorithm based on hyperspectral imaging technique,” Opt. Eng. 51, 053202 (2012).
[CrossRef]

Wei, C. H.

D. A. Hunter, A. Moradzadeh, E. L. Whitlock, M. J. Brenner, T. A. Myckatyn, C. H. Wei, T. H. H. Tung, and S. E. Mackinnon, “Binary imaging analysis for comprehensive quantitative histomorphometry of peripheral nerve,” J. Neurosci. Methods 166, 116–124 (2007).
[CrossRef]

Weyn, B.

B. Weyn, M. Van Remoortere, R. Nuydens, T. Meert, and G. van de Wouwer, “A multiparametric assay for quantitative nerve regeneration evaluation,” J. Microsc. 219, 95–101 (2005).
[CrossRef]

Whitlock, E. L.

D. A. Hunter, A. Moradzadeh, E. L. Whitlock, M. J. Brenner, T. A. Myckatyn, C. H. Wei, T. H. H. Tung, and S. E. Mackinnon, “Binary imaging analysis for comprehensive quantitative histomorphometry of peripheral nerve,” J. Neurosci. Methods 166, 116–124 (2007).
[CrossRef]

Wu, J. Y.

X. M. Zhao, Z. K. Pan, J. Y. Wu, G. M. Zhou, and Y. J. Zeng, “Automatic identification and morphometry of optic nerve fibers in electron microscopy images,” Comput. Med. Imaging Graph. 34, 179–184 (2010).
[CrossRef]

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Figures (9)

Fig. 1.
Fig. 1.

Schematic diagram of the MHSI system and molecular hyperspectral data cube.

Fig. 2.
Fig. 2.

(a) Microscope graticule and (b) single band image of graticules at wavelength 640 nm.

Fig. 3.
Fig. 3.

Spectral response of the MHSI system.

Fig. 4.
Fig. 4.

Molecular hyperspectral image processing flowchart.

Fig. 5.
Fig. 5.

Images of nerve sections: (a) and (d) RGB color images of stained nerve sections captured by light microscopy with a 20× and 40× objective lens; (b) and (c) false color images of the same section as (a) before staining; (e) and (f) false color images of the same section as (b) before staining; (b) and (e) false color images synthesized by the 554, 627, and 707 nm single-band images as the R, G, and B channels; and (c) and (f) false color images synthesized by the 554, 758, and 644 nm single-band images as the R, G, and B channels.

Fig. 6.
Fig. 6.

Single band image at wavelength 628 nm (a) before and (b) after preprocessing.

Fig. 7.
Fig. 7.

Spectra before and after preprocessing: (a) spectrum at pixel (218, 238) before and (b) after preprocessing; (c) spectrum at pixel (40, 112) before and (d) after preprocessing.

Fig. 8.
Fig. 8.

Segmentation results of different algorithms: (a), (e), and (i) single band images at wavelength 628 nm of unstained nerve sections captured by the MHSI system; (b), (f), and (j) segmentation results of the ISAM algorithm; (c), (g), and (k) segmentation results of the spatial–spectral kernel-based algorithm; and (d), (h), and (l) ground truth labeled manually.

Fig. 9.
Fig. 9.

(a) Typical nerve fiber image and (b) schematic diagram of fiber diameter (Di) and axon diameter (di).

Tables (2)

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Table 1. Performance Comparisons of the ISAM and the Proposed Algorithm for Nerve Fiber Segmentation

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Table 2. Morphometry Parameters of Nerve Fibers

Equations (7)

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

Tn(λ)=DNn(λ)DNd(λ)DNs(λ)DNd(λ),
k=μ1kspectral+μ2kspatial,
kspectral(x,z)=exp(xz22σ2)(Rn×Rn[0,1]),
kspatial(x,z)=exp(γxγz22σ2)(Rn×Rn[0,1]),
f(z)=i=1Nyiαik(xi,z)+b.
TPR=TP/CGT,
FPR=FP/BGT,

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