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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 3, Iss. 1 — Jan. 1, 2012
  • pp: 48–54
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Snapshot hyperspectral retinal camera with the Image Mapping Spectrometer (IMS)

Liang Gao, R. Theodore Smith, and Tomasz S. Tkaczyk  »View Author Affiliations


Biomedical Optics Express, Vol. 3, Issue 1, pp. 48-54 (2012)
http://dx.doi.org/10.1364/BOE.3.000048


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Abstract

We present a snapshot hyperspectral retinal camera with the Image Mapping Spectrometer (IMS) for eye imaging applications. The resulting system is capable of simultaneously acquiring 48 spectral channel images in the range 470 nm–650 nm with frame rate at 5.2 fps. The spatial sampling of each measured spectral scene is 350 × 350 pixels. The advantages of this snapshot device are elimination of the eye motion artifacts and pixel misregistration problems in traditional scanning-based hyperspectral retinal cameras, and real-time imaging of oxygen saturation dynamics with sub-second temporal resolution. The spectral imaging performance is demonstrated in a human retinal imaging experiment in vivo. The absorption spectral signatures of oxy-hemoglobin and macular pigments were successfully acquired by using this device.

© 2011 OSA

1. Introduction

Hyperspectral retinal imaging is a novel technique for non-invasive ocular diagnosis [1

1. B. Khoobehi, J. M. Beach, and H. Kawano, “Hyperspectral imaging for measurement of oxygen saturation in the optic nerve head,” Invest. Ophthalmol. Vis. Sci. 45(5), 1464–1472 (2004). [CrossRef] [PubMed]

,2

2. B. Khoobehi and J. Beach, “Hyperspectral image analysis for oxygen saturation automated localization of the eye,” in Computational Analysis of the Human Eye with Applications, S. Dua, U. R. Acharya, and E. Y. K. Ng, eds. (World Scientific, 2011), pp. 123–185.

]. Instead of only providing intensity images, the hyperspectral retinal camera is capable of acquiring both spatial and spectral retinal information and constructing an (x, y, λ) 3D datacube for multivariable data analysis [3

3. V. Diaconu, “Multichannel spectroreflectometry: a noninvasive method for assessment of on-line hemoglobin derivatives,” Appl. Opt. 48(10), D52–D61 (2009). [CrossRef] [PubMed]

,4

4. V. Vucea, P. J. Bernard, P. Sauvageau, and V. Diaconu, “Blood oxygenation measurements by multichannel reflectometry on the venous and arterial structures of the retina,” Appl. Opt. 50(26), 5185–5191 (2011). [CrossRef] [PubMed]

]. Since light reflected from retinal structures (e. g., the macula) often have unique spectral signatures which are commonly considered to be associated with specific ocular diseases [5

5. S. Beatty, M. Boulton, D. Henson, H. H. Koh, and I. J. Murray, “Macular pigment and age related macular degeneration,” Br. J. Ophthalmol. 83(7), 867–877 (1999). [CrossRef] [PubMed]

], the capability of identifying these spectral signatures in-vivo will be of high value in clinical diagnosis and treatment. However, most traditional hyperspectral retinal cameras are scanning-based systems—they either scan in the spatial domain, e.g. push-broom slit scanning spectrometers [1

1. B. Khoobehi, J. M. Beach, and H. Kawano, “Hyperspectral imaging for measurement of oxygen saturation in the optic nerve head,” Invest. Ophthalmol. Vis. Sci. 45(5), 1464–1472 (2004). [CrossRef] [PubMed]

], or scan in the spectral domain, e.g., liquid-crystal-tunable-filters [6

6. Y. Hirohara, T. Yamaguchi, H. Aoki, Y. Takahashi, N. Nakazawa, T. Mihashi, S. Sato, T. Morimoto, and T. Fujikado, “Development of fundus camera for spectral imaging using liquid crystal tunable filter,” Invest. Ophthalmol. Vis. Sci. 45, U935 (2004).

] and sequential illuminated multicolor LEDs [7

7. N. L. Everdell, I. B. Styles, A. Calcagni, J. Gibson, J. Hebden, and E. Claridge, “Multispectral imaging of the ocular fundus using light emitting diode illumination,” Rev. Sci. Instrum. 81(9), 093706 (2010). [CrossRef] [PubMed]

]. Incorporation of scanning-based hyperspectral imagers in retinal imaging causes severe motion artifacts and pixel misregistration problems because the human eye is constantly moving. To overcome this limitation, recently a snapshot hyperspectral retinal camera which utilizes a computed tomographic imaging spectrometer (CTIS) has been developed for fundus imaging [8

8. G. Bearman, W. R. Johnson, D. W. Wilson, W. Fink, and M. Humayun, “Snapshot hyperspectral imaging in ophthalmology,” J. Biomed. Opt. 12(1), 014036 (2007).

]. Although this CTIS-based hyperspectral retinal camera can acquire multiple spectral scenes within a single integration event, the CTIS technique itself requires extensive computational cost and is limited by resolution constraints [9

9. N. Hagen and E. L. Dereniak, “Analysis of computed tomographic imaging spectrometers. I. Spatial and spectral resolution,” Appl. Opt. 47(28), F85–F95 (2008). [CrossRef] [PubMed]

].

In this article, we present a novel snapshot hyperspectral retinal camera which uses an Image Mapping Spectrometer (IMS) [10

10. L. Gao, R. T. Kester, and T. S. Tkaczyk, “Compact Image Slicing Spectrometer (ISS) for hyperspectral fluorescence microscopy,” Opt. Express 17(15), 12293–12308 (2009). [CrossRef] [PubMed]

12

12. R. T. Kester, N. Bedard, L. Gao, and T. S. Tkaczyk, “Real-time snapshot hyperspectral imaging endoscope,” J. Biomed. Opt. 16(5), 056005 (2011). [CrossRef] [PubMed]

] as its spectrum detector. The IMS is a parallel acquisition hyperspectral imager that can capture hyperspectral datacubes without scanning. It also allows full light throughput across the whole spectral collection range due to its snapshot operating format. By integrating the IMS with a traditional retinal camera (Topcon TRC50X, Topcon Inc, Tokyo, Japan), we have achieved simultaneous 48 spectral channel imaging of the human retina in vivo. The reflectance and absorption spectra from vessels and the macula were measured, from which the known spectral absorption signatures of oxy-hemoglobin and macular pigment were successfully identified. The previously unknown spectral reflectance of an optic disc druse was also measured.

2. System description

The system setup of the hyperspectral retinal camera is shown in Fig. 1 (a)
Fig. 1 Snapshot hyperspectral retinal camera with the IMS. The IMS is coupled to the back image port of a traditional retinal camera. The optical layout inside the IMS is detailed in [12].
. The IMS is coupled to the back image port of the Topcon TRC 50X retinal camera to acquire (x, y, λ) datacubes. The optical components of the retinal camera and the IMS are shown in Fig. 1 (b). The illumination is provided by the tungsten light of retinal camera. After being collimated, the illumination light is reflected by an annular mirror and then focused on to the eye’s pupil, creating a uniformly illuminated field on the retina. The reflected light from the fundus is collected by retinal camera’s front lens and forms an intermediate image at its back image port, where the entrance port of the IMS is co-located.

The operation of the IMS is based on the image mapping principle, which has been detailed elsewhere [10

10. L. Gao, R. T. Kester, and T. S. Tkaczyk, “Compact Image Slicing Spectrometer (ISS) for hyperspectral fluorescence microscopy,” Opt. Express 17(15), 12293–12308 (2009). [CrossRef] [PubMed]

,11

11. L. Gao, R. T. Kester, N. Hagen, and T. S. Tkaczyk, “Snapshot Image Mapping Spectrometer (IMS) with high sampling density for hyperspectral microscopy,” Opt. Express 18(14), 14330–14344 (2010). [CrossRef] [PubMed]

]. Briefly, the intermediate image at the entrance port is first reimaged onto a custom-fabricated component—the image mapper. The image mapper consists of hundreds of mirror facets, and each mirror facet is around 70 µm wide and has a two-dimensional tilt [13

13. R. T. Kester, L. Gao, and T. S. Tkaczyk, “Development of image mappers for hyperspectral biomedical imaging applications,” Appl. Opt. 49(10), 1886–1899 (2010). [CrossRef] [PubMed]

]. The image mapper cuts the intermediate image into strips and reflects them to different locations of a CCD camera. Due to tilt angle differences of mirror facets on the image mapper, blank regions are created between adjacent image strips at the detector plane. The strips of reflected light from the image mapper are then dispersed by a prism array and reimaged onto their associated blank regions by an array of reimaging lenses. In this way, each pixel on the CCD camera is encoded with unique spatial and spectral information from the sample. By applying a simple image remapping algorithm [11

11. L. Gao, R. T. Kester, N. Hagen, and T. S. Tkaczyk, “Snapshot Image Mapping Spectrometer (IMS) with high sampling density for hyperspectral microscopy,” Opt. Express 18(14), 14330–14344 (2010). [CrossRef] [PubMed]

], a hyperspectral (x, y, λ) datacube is acquired. Since no scanning is employed, the IMS features high optical throughput and datacube acquisition rate (currently up to 7.2 fps [12

12. R. T. Kester, N. Bedard, L. Gao, and T. S. Tkaczyk, “Real-time snapshot hyperspectral imaging endoscope,” J. Biomed. Opt. 16(5), 056005 (2011). [CrossRef] [PubMed]

]). The spatial sampling of each acquired spectral scene is 350 × 350 pixels, and the measured spectral range is from 470 nm to 650 nm with ~4 nm bandwidth.

3. Hyperspectral imaging of the retina in vivo

To demonstrate the spectral imaging performance of the IMS-based hyperspectral retinal camera, the retina of a 25 year old healthy female volunteer with a known ocular diagnosis of optic disc drusen [14

14. P. L. Davis and W. M. Jay, “Optic nerve head drusen,” Semin. Ophthalmol. 18(4), 222–242 (2003). [CrossRef] [PubMed]

] was imaged in vivo. The subject’s pupil was dilated with mydriatic (Mydriacyl, 1%) 15 minutes before the experiment. The hyperspectral retinal camera was working in the reflectance imaging mode, in which the internal light source of the retinal camera provided constant illumination of the structures. The retinal images were taken at the 50 degree viewing angle of the camera, of which an approximately 20 degree retinal image was captured by the optics of the IMS. The IMS was operated at 5.2 fps with ~180 ms integration time for each frame. The acquired (x, y, λ) datacube is displayed as a panchromatic image in Fig. 2 (a)
Fig. 2 Hyperspectral imaging of the retina centered at the optic disc in vivo. An optic disc drusen is seen at about the one o’clock position at the edge of the disc. (a) Panchromatic image display of acquired (x, y, λ) datacube. The coloration of each pixel is converted from corresponding spectral data. (b) Selected images from a total of 48 spectral channels. A scan of all acquired wavelengths is shown in Media 1. Note the greater spectral reflectance of the optic disc druse in the 530 nm to 580 nm wavelengths. There is also an atypical retinal vessel branching pattern, which often accompanies optic disc drusen. (c) Baseline reference image captured without the IMS attached.
. The coloration of each pixel is converted from corresponding spectral data with a specific algorithm [15

15. H. R. Kang, Computational Color Technology (SPIE Press, Bellingham, Wash., 2006).

]. Selected images from a total of 48 spectral channels are shown in Fig. 2 (b) (see a scan of all acquired wavelengths in Media 1). Note that vertical stripes show up in Media 1. These stripes are image artifacts and are caused by the mirror facets’ reflectivity variations in the current IMS. In order to provide a baseline reference, a retinal camera image without the IMS attached is also captured and shown in Fig. 2(c). The image resolution of the standard fundus photo is about the same as the hyperspectral composite.

The ability to visualize oxygen delivery into the eye in vivo is important in clinical studies because it would aid our understanding of the way that oxygen is provided and used in the eye, both in healthy and diseased conditions. Since the oxygenation can be estimated from blood oxy-hemoglobin concentration [1

1. B. Khoobehi, J. M. Beach, and H. Kawano, “Hyperspectral imaging for measurement of oxygen saturation in the optic nerve head,” Invest. Ophthalmol. Vis. Sci. 45(5), 1464–1472 (2004). [CrossRef] [PubMed]

], we first measured the absorption spectra from a retinal arteriole on the optic nerve (see Fig. 3
Fig. 3 Measured (b) reflectance spectrum and (c) absorption spectrum of oxy-hemoglobin in a retinal arteriole on the optic nerve; measured (d) reflectance spectra of druse and normal disc substance and (e) their reflectance ratio vs. wavelength
) to test the feasibility of the proposed system in recovering oxy-hemoglobin’s absorption spectral signature. The IMS was spectrally calibrated with respect to a standard light source (Ocean optics, PN: LS-1-CAL-INT) before the experiment. The spectral range from 510 nm to 586 nm was chosen for analysis because within this range the blood absorption has a major contribution to the overall spectral reflection while ocular media absorption and scattering by erythrocytes are minimal [4

4. V. Vucea, P. J. Bernard, P. Sauvageau, and V. Diaconu, “Blood oxygenation measurements by multichannel reflectometry on the venous and arterial structures of the retina,” Appl. Opt. 50(26), 5185–5191 (2011). [CrossRef] [PubMed]

,16

16. D. Schweitzer, M. Hammer, J. Kraft, E. Thamm, E. Königsdörffer, and J. Strobel, “In vivo measurement of the oxygen saturation of retinal vessels in healthy volunteers,” IEEE Trans. Biomed. Eng. 46(12), 1454–1465 (1999). [CrossRef] [PubMed]

]. The reflectance spectrum Sr (Fig. 3(b)) at the vessel was acquired by averaging over pixels’ spectra in the circled area. In order to acquire the absorption spectrum in the same area, the lamp’s illumination spectrum Si was first measured by placing a white paper in front of the retinal camera’s front lens and averaging over pixels’ reflectance spectra in the field of view. Then the absorption spectrum Sa (Fig. 3(c)) was calculated by subtracting the normalized reflectance spectrum Sr from the normalized lamp’s illumination spectrum Si in the spectral range 510 nm to 586 nm, e.g.

Sa510586nm=Si510586nm/max(Si510586nm)Sr510586nm/max(Sr510586nm)
(1)

A time-lapsed video of oxygen saturation dynamics was then recorded with a frame rate of 5.2 fps. The oxygen saturation map (see Fig. 4
Fig. 4 Oxygen saturation dynamics near the optic nerve (Media 2)
) was calculated at each scene with the algorithm detailed in Refs. [1

1. B. Khoobehi, J. M. Beach, and H. Kawano, “Hyperspectral imaging for measurement of oxygen saturation in the optic nerve head,” Invest. Ophthalmol. Vis. Sci. 45(5), 1464–1472 (2004). [CrossRef] [PubMed]

,18

18. J. Beach, J. F. Ning, and B. Khoobehi, “Oxygen saturation in optic nerve head structures by hyperspectral image analysis,” Curr. Eye Res. 32(2), 161–170 (2007). [CrossRef] [PubMed]

]. The oxygen saturation dynamics at one arteriole (pointer, Fig. 4) is shown in Fig. 5
Fig. 5 Relative saturation index vs. time at an arteriole on the optic nerve
. These results show that the relative saturation index value reaches its maximum at time t = 720 ms and t = 2340 ms, an interval of 1.6 sec. Interestingly, the subject’s pulse of 72 bpm would suggest that the O2 saturation should peak about every 0.84 sec, or about twice as often as measured herein. A possible explanation may lie in the anomalous circulation on the optic disc in subjects with optic disc drusen [14

14. P. L. Davis and W. M. Jay, “Optic nerve head drusen,” Semin. Ophthalmol. 18(4), 222–242 (2003). [CrossRef] [PubMed]

,19

19. C. Auw-Haedrich, M. Mathieu, and L. L. Hansen, “Complete circumvention of central retinal artery and venous cilioretinal shunts in optic disc drusen,” Arch. Ophthalmol. 114(10), 1285–1287 (1996). [CrossRef] [PubMed]

], and in fact the central retinal artery can be bypassed completely [19

19. C. Auw-Haedrich, M. Mathieu, and L. L. Hansen, “Complete circumvention of central retinal artery and venous cilioretinal shunts in optic disc drusen,” Arch. Ophthalmol. 114(10), 1285–1287 (1996). [CrossRef] [PubMed]

], but this of course is speculation. Note that during the acquisition period, the eye was constantly moving (Media 2). Since current scanning-based hyperspectral retinal cameras normally require an exposure time > 5 secs long to acquire an (x, y, λ) datacube [20

20. J. M. Beach, K. J. Schwenzer, S. Srinivas, D. Kim, and J. S. Tiedeman, “Oximetry of retinal vessels by dual-wavelength imaging: calibration and influence of pigmentation,” J. Appl. Physiol. 86(2), 748–758 (1999). [PubMed]

], all in vivo retinal data from such systems would be compromised by motion artifacts. In particular, a dynamic study such as seen in Figs. 4 and 5 would not even be possible.

4. Conclusions

Acknowledgments

This work is supported by the National Institutes of Health under Grants No. R21EB009186, R21EB011598 and R01 EY021470.

References and links

1.

B. Khoobehi, J. M. Beach, and H. Kawano, “Hyperspectral imaging for measurement of oxygen saturation in the optic nerve head,” Invest. Ophthalmol. Vis. Sci. 45(5), 1464–1472 (2004). [CrossRef] [PubMed]

2.

B. Khoobehi and J. Beach, “Hyperspectral image analysis for oxygen saturation automated localization of the eye,” in Computational Analysis of the Human Eye with Applications, S. Dua, U. R. Acharya, and E. Y. K. Ng, eds. (World Scientific, 2011), pp. 123–185.

3.

V. Diaconu, “Multichannel spectroreflectometry: a noninvasive method for assessment of on-line hemoglobin derivatives,” Appl. Opt. 48(10), D52–D61 (2009). [CrossRef] [PubMed]

4.

V. Vucea, P. J. Bernard, P. Sauvageau, and V. Diaconu, “Blood oxygenation measurements by multichannel reflectometry on the venous and arterial structures of the retina,” Appl. Opt. 50(26), 5185–5191 (2011). [CrossRef] [PubMed]

5.

S. Beatty, M. Boulton, D. Henson, H. H. Koh, and I. J. Murray, “Macular pigment and age related macular degeneration,” Br. J. Ophthalmol. 83(7), 867–877 (1999). [CrossRef] [PubMed]

6.

Y. Hirohara, T. Yamaguchi, H. Aoki, Y. Takahashi, N. Nakazawa, T. Mihashi, S. Sato, T. Morimoto, and T. Fujikado, “Development of fundus camera for spectral imaging using liquid crystal tunable filter,” Invest. Ophthalmol. Vis. Sci. 45, U935 (2004).

7.

N. L. Everdell, I. B. Styles, A. Calcagni, J. Gibson, J. Hebden, and E. Claridge, “Multispectral imaging of the ocular fundus using light emitting diode illumination,” Rev. Sci. Instrum. 81(9), 093706 (2010). [CrossRef] [PubMed]

8.

G. Bearman, W. R. Johnson, D. W. Wilson, W. Fink, and M. Humayun, “Snapshot hyperspectral imaging in ophthalmology,” J. Biomed. Opt. 12(1), 014036 (2007).

9.

N. Hagen and E. L. Dereniak, “Analysis of computed tomographic imaging spectrometers. I. Spatial and spectral resolution,” Appl. Opt. 47(28), F85–F95 (2008). [CrossRef] [PubMed]

10.

L. Gao, R. T. Kester, and T. S. Tkaczyk, “Compact Image Slicing Spectrometer (ISS) for hyperspectral fluorescence microscopy,” Opt. Express 17(15), 12293–12308 (2009). [CrossRef] [PubMed]

11.

L. Gao, R. T. Kester, N. Hagen, and T. S. Tkaczyk, “Snapshot Image Mapping Spectrometer (IMS) with high sampling density for hyperspectral microscopy,” Opt. Express 18(14), 14330–14344 (2010). [CrossRef] [PubMed]

12.

R. T. Kester, N. Bedard, L. Gao, and T. S. Tkaczyk, “Real-time snapshot hyperspectral imaging endoscope,” J. Biomed. Opt. 16(5), 056005 (2011). [CrossRef] [PubMed]

13.

R. T. Kester, L. Gao, and T. S. Tkaczyk, “Development of image mappers for hyperspectral biomedical imaging applications,” Appl. Opt. 49(10), 1886–1899 (2010). [CrossRef] [PubMed]

14.

P. L. Davis and W. M. Jay, “Optic nerve head drusen,” Semin. Ophthalmol. 18(4), 222–242 (2003). [CrossRef] [PubMed]

15.

H. R. Kang, Computational Color Technology (SPIE Press, Bellingham, Wash., 2006).

16.

D. Schweitzer, M. Hammer, J. Kraft, E. Thamm, E. Königsdörffer, and J. Strobel, “In vivo measurement of the oxygen saturation of retinal vessels in healthy volunteers,” IEEE Trans. Biomed. Eng. 46(12), 1454–1465 (1999). [CrossRef] [PubMed]

17.

N. Lee, J. Wielaard, A. A. Fawzi, P. Sajda, A. F. Laine, G. Martin, M. S. Humayun, and R. T. Smith, “In vivo snapshot hyperspectral image analysis of age-related macular degeneration,” in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2010), pp. 5363–5366.

18.

J. Beach, J. F. Ning, and B. Khoobehi, “Oxygen saturation in optic nerve head structures by hyperspectral image analysis,” Curr. Eye Res. 32(2), 161–170 (2007). [CrossRef] [PubMed]

19.

C. Auw-Haedrich, M. Mathieu, and L. L. Hansen, “Complete circumvention of central retinal artery and venous cilioretinal shunts in optic disc drusen,” Arch. Ophthalmol. 114(10), 1285–1287 (1996). [CrossRef] [PubMed]

20.

J. M. Beach, K. J. Schwenzer, S. Srinivas, D. Kim, and J. S. Tiedeman, “Oximetry of retinal vessels by dual-wavelength imaging: calibration and influence of pigmentation,” J. Appl. Physiol. 86(2), 748–758 (1999). [PubMed]

21.

A. A. Fawzi, N. Lee, J. H. Acton, A. F. Laine, and R. T. Smith, “Recovery of macular pigment spectrum in vivo using hyperspectral image analysis,” J. Biomed. Opt. 16(10), 106008 (2011). [CrossRef] [PubMed]

22.

B. Davis, S. Russell, M. Abramoff, S. C. Nemeth, E. S. Barriga, and P. Soliz, “Identification of spectral phenojours in age-related macular degeneration patients,” Proc. SPIE 6426, 64261I, 64261I-11 (2007). [CrossRef]

23.

R. A. Bone, B. Brener, and J. C. Gibert, “Macular pigment, photopigments, and melanin: distributions in young subjects determined by four-wavelength reflectometry,” Vision Res. 47(26), 3259–3268 (2007). [CrossRef] [PubMed]

OCIS Codes
(170.6510) Medical optics and biotechnology : Spectroscopy, tissue diagnostics
(330.4460) Vision, color, and visual optics : Ophthalmic optics and devices
(110.4234) Imaging systems : Multispectral and hyperspectral imaging

ToC Category:
Molecular Imaging and Probe Development

History
Original Manuscript: October 13, 2011
Revised Manuscript: November 30, 2011
Manuscript Accepted: December 2, 2011
Published: December 7, 2011

Citation
Liang Gao, R. Theodore Smith, and Tomasz S. Tkaczyk, "Snapshot hyperspectral retinal camera with the Image Mapping Spectrometer (IMS)," Biomed. Opt. Express 3, 48-54 (2012)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-3-1-48


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References

  1. B. Khoobehi, J. M. Beach, and H. Kawano, “Hyperspectral imaging for measurement of oxygen saturation in the optic nerve head,” Invest. Ophthalmol. Vis. Sci.45(5), 1464–1472 (2004). [CrossRef] [PubMed]
  2. B. Khoobehi and J. Beach, “Hyperspectral image analysis for oxygen saturation automated localization of the eye,” in Computational Analysis of the Human Eye with Applications, S. Dua, U. R. Acharya, and E. Y. K. Ng, eds. (World Scientific, 2011), pp. 123–185.
  3. V. Diaconu, “Multichannel spectroreflectometry: a noninvasive method for assessment of on-line hemoglobin derivatives,” Appl. Opt.48(10), D52–D61 (2009). [CrossRef] [PubMed]
  4. V. Vucea, P. J. Bernard, P. Sauvageau, and V. Diaconu, “Blood oxygenation measurements by multichannel reflectometry on the venous and arterial structures of the retina,” Appl. Opt.50(26), 5185–5191 (2011). [CrossRef] [PubMed]
  5. S. Beatty, M. Boulton, D. Henson, H. H. Koh, and I. J. Murray, “Macular pigment and age related macular degeneration,” Br. J. Ophthalmol.83(7), 867–877 (1999). [CrossRef] [PubMed]
  6. Y. Hirohara, T. Yamaguchi, H. Aoki, Y. Takahashi, N. Nakazawa, T. Mihashi, S. Sato, T. Morimoto, and T. Fujikado, “Development of fundus camera for spectral imaging using liquid crystal tunable filter,” Invest. Ophthalmol. Vis. Sci.45, U935 (2004).
  7. N. L. Everdell, I. B. Styles, A. Calcagni, J. Gibson, J. Hebden, and E. Claridge, “Multispectral imaging of the ocular fundus using light emitting diode illumination,” Rev. Sci. Instrum.81(9), 093706 (2010). [CrossRef] [PubMed]
  8. G. Bearman, W. R. Johnson, D. W. Wilson, W. Fink, and M. Humayun, “Snapshot hyperspectral imaging in ophthalmology,” J. Biomed. Opt.12(1), 014036 (2007).
  9. N. Hagen and E. L. Dereniak, “Analysis of computed tomographic imaging spectrometers. I. Spatial and spectral resolution,” Appl. Opt.47(28), F85–F95 (2008). [CrossRef] [PubMed]
  10. L. Gao, R. T. Kester, and T. S. Tkaczyk, “Compact Image Slicing Spectrometer (ISS) for hyperspectral fluorescence microscopy,” Opt. Express17(15), 12293–12308 (2009). [CrossRef] [PubMed]
  11. L. Gao, R. T. Kester, N. Hagen, and T. S. Tkaczyk, “Snapshot Image Mapping Spectrometer (IMS) with high sampling density for hyperspectral microscopy,” Opt. Express18(14), 14330–14344 (2010). [CrossRef] [PubMed]
  12. R. T. Kester, N. Bedard, L. Gao, and T. S. Tkaczyk, “Real-time snapshot hyperspectral imaging endoscope,” J. Biomed. Opt.16(5), 056005 (2011). [CrossRef] [PubMed]
  13. R. T. Kester, L. Gao, and T. S. Tkaczyk, “Development of image mappers for hyperspectral biomedical imaging applications,” Appl. Opt.49(10), 1886–1899 (2010). [CrossRef] [PubMed]
  14. P. L. Davis and W. M. Jay, “Optic nerve head drusen,” Semin. Ophthalmol.18(4), 222–242 (2003). [CrossRef] [PubMed]
  15. H. R. Kang, Computational Color Technology (SPIE Press, Bellingham, Wash., 2006).
  16. D. Schweitzer, M. Hammer, J. Kraft, E. Thamm, E. Königsdörffer, and J. Strobel, “In vivo measurement of the oxygen saturation of retinal vessels in healthy volunteers,” IEEE Trans. Biomed. Eng.46(12), 1454–1465 (1999). [CrossRef] [PubMed]
  17. N. Lee, J. Wielaard, A. A. Fawzi, P. Sajda, A. F. Laine, G. Martin, M. S. Humayun, and R. T. Smith, “In vivo snapshot hyperspectral image analysis of age-related macular degeneration,” in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2010), pp. 5363–5366.
  18. J. Beach, J. F. Ning, and B. Khoobehi, “Oxygen saturation in optic nerve head structures by hyperspectral image analysis,” Curr. Eye Res.32(2), 161–170 (2007). [CrossRef] [PubMed]
  19. C. Auw-Haedrich, M. Mathieu, and L. L. Hansen, “Complete circumvention of central retinal artery and venous cilioretinal shunts in optic disc drusen,” Arch. Ophthalmol.114(10), 1285–1287 (1996). [CrossRef] [PubMed]
  20. J. M. Beach, K. J. Schwenzer, S. Srinivas, D. Kim, and J. S. Tiedeman, “Oximetry of retinal vessels by dual-wavelength imaging: calibration and influence of pigmentation,” J. Appl. Physiol.86(2), 748–758 (1999). [PubMed]
  21. A. A. Fawzi, N. Lee, J. H. Acton, A. F. Laine, and R. T. Smith, “Recovery of macular pigment spectrum in vivo using hyperspectral image analysis,” J. Biomed. Opt.16(10), 106008 (2011). [CrossRef] [PubMed]
  22. B. Davis, S. Russell, M. Abramoff, S. C. Nemeth, E. S. Barriga, and P. Soliz, “Identification of spectral phenojours in age-related macular degeneration patients,” Proc. SPIE6426, 64261I, 64261I-11 (2007). [CrossRef]
  23. R. A. Bone, B. Brener, and J. C. Gibert, “Macular pigment, photopigments, and melanin: distributions in young subjects determined by four-wavelength reflectometry,” Vision Res.47(26), 3259–3268 (2007). [CrossRef] [PubMed]

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