Abstract

We applied a polarimetric analysis to retinal imaging, to examine the potential improvement in characterizing blood vessels. To minimize the reflection artifact of the superficial wall of the blood vessel, we computed depolarized light images by removing the polarization retaining light reaching the instrument. These depolarized light images were compared to images from the average of all the light. Michelson contrast was computed for the vessel profiles across arteries and veins, and was higher for the depolarized light images. Depolarized light images provide one step towards improving the characterization of retinal blood vessels.

© 2004 Optical Society of America

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References

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  1. B. B. Boycott, J. W. Dowling, �??Organization of the primate retina: Light microscopy,�?? Philosophical Transactions of the Royal Society of London B 255: 109-184 (1969)
    [CrossRef]
  2. A. E. Elsner, S. A. Burns, J. J. Weiter, F. C. Delori, �??Infrared imaging of subretinal structures in the human ocular fundus,�?? Vis Res 36, 191-205 (1996)
    [CrossRef] [PubMed]
  3. M. Miura, A. E. Elsner, �??Three dimensional imaging in age-related macular degeneration,�?? Opt Express 9, 436-443 (2001)
    [CrossRef] [PubMed]
  4. A. E. Elsner, M. Miura, J. B. Stewart, M. B. M. Kairala, S.A. Burns, �?? Novel Algorithm for polarization imaging resulting in improved quantification of retinal blood vessels,�?? Medicine Meets Virtual Reality 11, 59-61 (2003)
  5. S. A. Burns, A. E. Elsner, M. B. Mellem-Kairala, R. B. Simmons, �??Improved contrast of subretinal structures using polarization analysis,�?? Invest. Ophthalmol. Vis. Sci. 44, 4061-8 (2003)
    [CrossRef] [PubMed]
  6. T.S. Leeson, C.R. Leeson, A.A. Paparo, �??The circulatory system�?? in Text/Atlas of Histology, T.S. Leeson, C.R. Leeson, A.A. Paparo , eds. (Saunders, Philadelphia, PA 1988), pp. 309-327
  7. A.E. Elsner, S.A. Burns, F.C. Delori, R.H. Webb, "Quantitative Reflectometry with the SLO," in Laser Scanning Ophthalmoscopy and Tomography, J. E. Nasemann and R.O.W. Burk, eds. (Quintessenz-Verlag, Muenchen, 1990), pp. 109-121
  8. R.H. Webb and F.C. Delori, �??How we see the retina.�?? in Laser Technology in Ophthalmology, J. Marshall ed., (Kugler & Ghedini Pub. 1988), pp 3-14.
  9. S.A. Burns, S. Marcos, A.E. Elsner, S. Barra, �??Contrast Improvement for Confocal Retinal Imaging Using Phase Correcting Plates,�?? Optics Letters. 27, 400-402 (2002)
    [CrossRef]
  10. N. Chapman, N.Witt, X. Gao, A.A. Bharath, A.V. Stanton, S.A. Thom, A.D. Hughes, �??Computer algorithms for the automated measurements of retinal arteriolar diameters,�?? Br. J. Ophthalmol. 85,74-79 (2001).
    [CrossRef] [PubMed]
  11. M. H. Smith, K. R. Denninghoff, A. Lompado, Hillman LW, �??Effect of multiple light paths on retinal vessel oximetry", Appl. Opt. 3, 1183-1193 (2000)
    [CrossRef]
  12. N. T. Choplin, D. C. Lundy, A. W. Dreher, �?? Differentiating patients with glaucoma from glaucoma suspects and normal subjects by nerve fiber layer assessment with scanning laser polarimetry,�?? Ophthalmology 105, 2068-2076 (1998)
    [CrossRef] [PubMed]
  13. L. S. Lasdon, R. L. Fox, M. W. Ratner, �??Nonlinear optimization using the generalized reduced gradient method,�?? RAIRO 3, 73-104 (1974)
  14. K. R. Denninghoff, M. H. Smith, M. H. Lompado, L.W. Hillman, �??Retinal venous oxygen saturation and cardiac output during controlled hemorrhage and resuscitation,�?? J. Appl. Physiol. 94, 891-896 (2003)
    [PubMed]
  15. R. Klein, B. E. K. Klein, S. C. Tomany, T. Y. Wong, �??The relation of retinal microvascular characteristics to age-related eye disease: the Beaver Dam Eye Study,�?? Am. J. Ophthalmol, 137 435-444 (2004)
    [CrossRef] [PubMed]
  16. Greenfield, R.W. Knighton, W.J. Feuer, J.C. Schiffman, L. Zangwill and R.N. Weinreb, �??Correction for corneal polarization axis improves the discriminating power of scanning laser polarimetry,�?? Am. J. Ophthalmol. 134, 27-33 (2002)
    [CrossRef] [PubMed]
  17. M. Miura, M.Osako, A. E. Elsner, H. Kajizuka, K. Uamada, M. Usua, �??Birefringence of intraocular lenses,�?? J Cataract Refract Surg. 30, 1549-55 (2004)
    [CrossRef] [PubMed]
  18. R.A. Chipman �??Polarimetry�?? in The Handbook of Optics, M. Bass, E.W. van Stryland, D.R.Williams, W.I.Wolfe, eds. (McGraw Hill, New York, 1994) pp. 1-27
  19. J. M. Bueno, M.C.W. Campbell, �??Confocal scanning laser ophthalmoscopy improvement by use of Muellermatrix polarimetry,�?? Opt. Lett. 27, 830-832 (2002)
    [CrossRef]
  20. J. M. Bueno, �??Polarimetry in the human eye using an imaging linear polariscope,�?? J. Opt. A-Pure Appl. Opt. 4, 553-561 (2002)
    [CrossRef]

Am. J. Ophthalmol. (2)

R. Klein, B. E. K. Klein, S. C. Tomany, T. Y. Wong, �??The relation of retinal microvascular characteristics to age-related eye disease: the Beaver Dam Eye Study,�?? Am. J. Ophthalmol, 137 435-444 (2004)
[CrossRef] [PubMed]

Greenfield, R.W. Knighton, W.J. Feuer, J.C. Schiffman, L. Zangwill and R.N. Weinreb, �??Correction for corneal polarization axis improves the discriminating power of scanning laser polarimetry,�?? Am. J. Ophthalmol. 134, 27-33 (2002)
[CrossRef] [PubMed]

Appl. Opt. (1)

M. H. Smith, K. R. Denninghoff, A. Lompado, Hillman LW, �??Effect of multiple light paths on retinal vessel oximetry", Appl. Opt. 3, 1183-1193 (2000)
[CrossRef]

Br. J. Ophthalmol. (1)

N. Chapman, N.Witt, X. Gao, A.A. Bharath, A.V. Stanton, S.A. Thom, A.D. Hughes, �??Computer algorithms for the automated measurements of retinal arteriolar diameters,�?? Br. J. Ophthalmol. 85,74-79 (2001).
[CrossRef] [PubMed]

Invest. Ophthalmol. Vis. Sci. (1)

S. A. Burns, A. E. Elsner, M. B. Mellem-Kairala, R. B. Simmons, �??Improved contrast of subretinal structures using polarization analysis,�?? Invest. Ophthalmol. Vis. Sci. 44, 4061-8 (2003)
[CrossRef] [PubMed]

J Cataract Refract Surg. (1)

M. Miura, M.Osako, A. E. Elsner, H. Kajizuka, K. Uamada, M. Usua, �??Birefringence of intraocular lenses,�?? J Cataract Refract Surg. 30, 1549-55 (2004)
[CrossRef] [PubMed]

J. Appl. Physiol. (1)

K. R. Denninghoff, M. H. Smith, M. H. Lompado, L.W. Hillman, �??Retinal venous oxygen saturation and cardiac output during controlled hemorrhage and resuscitation,�?? J. Appl. Physiol. 94, 891-896 (2003)
[PubMed]

J. Opt. A-Pure Appl. Opt. (1)

J. M. Bueno, �??Polarimetry in the human eye using an imaging linear polariscope,�?? J. Opt. A-Pure Appl. Opt. 4, 553-561 (2002)
[CrossRef]

Medicine Meets Virtual Reality (1)

A. E. Elsner, M. Miura, J. B. Stewart, M. B. M. Kairala, S.A. Burns, �?? Novel Algorithm for polarization imaging resulting in improved quantification of retinal blood vessels,�?? Medicine Meets Virtual Reality 11, 59-61 (2003)

Ophthalmology (1)

N. T. Choplin, D. C. Lundy, A. W. Dreher, �?? Differentiating patients with glaucoma from glaucoma suspects and normal subjects by nerve fiber layer assessment with scanning laser polarimetry,�?? Ophthalmology 105, 2068-2076 (1998)
[CrossRef] [PubMed]

Opt Express (1)

M. Miura, A. E. Elsner, �??Three dimensional imaging in age-related macular degeneration,�?? Opt Express 9, 436-443 (2001)
[CrossRef] [PubMed]

Opt. Lett. (1)

Optics Letters. (1)

S.A. Burns, S. Marcos, A.E. Elsner, S. Barra, �??Contrast Improvement for Confocal Retinal Imaging Using Phase Correcting Plates,�?? Optics Letters. 27, 400-402 (2002)
[CrossRef]

Philosophical Transactions of the Royal (1)

B. B. Boycott, J. W. Dowling, �??Organization of the primate retina: Light microscopy,�?? Philosophical Transactions of the Royal Society of London B 255: 109-184 (1969)
[CrossRef]

RAIRO (1)

L. S. Lasdon, R. L. Fox, M. W. Ratner, �??Nonlinear optimization using the generalized reduced gradient method,�?? RAIRO 3, 73-104 (1974)

Vis Res (1)

A. E. Elsner, S. A. Burns, J. J. Weiter, F. C. Delori, �??Infrared imaging of subretinal structures in the human ocular fundus,�?? Vis Res 36, 191-205 (1996)
[CrossRef] [PubMed]

Other (4)

T.S. Leeson, C.R. Leeson, A.A. Paparo, �??The circulatory system�?? in Text/Atlas of Histology, T.S. Leeson, C.R. Leeson, A.A. Paparo , eds. (Saunders, Philadelphia, PA 1988), pp. 309-327

A.E. Elsner, S.A. Burns, F.C. Delori, R.H. Webb, "Quantitative Reflectometry with the SLO," in Laser Scanning Ophthalmoscopy and Tomography, J. E. Nasemann and R.O.W. Burk, eds. (Quintessenz-Verlag, Muenchen, 1990), pp. 109-121

R.H. Webb and F.C. Delori, �??How we see the retina.�?? in Laser Technology in Ophthalmology, J. Marshall ed., (Kugler & Ghedini Pub. 1988), pp 3-14.

R.A. Chipman �??Polarimetry�?? in The Handbook of Optics, M. Bass, E.W. van Stryland, D.R.Williams, W.I.Wolfe, eds. (McGraw Hill, New York, 1994) pp. 1-27

Supplementary Material (1)

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Figures (8)

Fig. 1.
Fig. 1.

(479 kB) Movie of raw GDx data from the right eye of a 45 year old, female glaucoma suspect. The retina was illuminated with linearly polarized light, at 20 different polarization angles over range of 90 deg in equal steps. For each polarization angle, two images were acquired: one from a detector configured to collect light with a polarization angle perpendicular (crossed) to the illumination light (left image), the other from a detector configured to collect returning light with a polarization angle parallel to the illumination light (right image). Images have been scaled for easier viewing, since the unscaled image for the crossed detector is considerably darker than its corresponding image from the parallel detector. Scaling parameters were determined from the first frame of the movie, separately for each detector. Parameters were chosen so that the full 256 level grayscale range mapped to the mean plus or minus three standard deviations of the data from the first image. Subsequent frames used the same mapping, so that all frames in the movie were scaled with the same parameters. Note the changes over successive frames in the appearance of the large vessels in the crossed image (left).

Fig. 2.
Fig. 2.

Diagram describing the polarimetry algorithm that is used to compute images with different polarization content. Top left- images from the parallel detector, outlined in blue. Bottom left- images from the crossed detector, outlined in red. Right- Intensity variation of a single pixel. The data points associated with the red and blue curves show the intensity variation of a single pixel as it changes over the 90 deg range of input polarization. The grayscale for one pixel is calculated for each of the 20 polarization angles, which is sampled in less than one second. The data are low pass filtered in the frequency domain using a Fast Fourier Transform (FFT) to reduce noise, as shown by comparison of the red and blue curves with the individual data points. The blue curve at the upper right shows the modulation of the light returning to the parallel detector, smoothed by FFT. The red curve at the lower right shows the smoothed curve for the crossed detector. The minimum value of light that returns to the crossed detector is taken from the lowest value of the FFT and used to compute the depolarized image. We assume that light that does not modulate with polarization angle has become randomly polarized due to multiple scattering. The thick gray bar in the middle right indicates that the average light returning from both detectors can be used to compute an average image.

Fig. 3.
Fig. 3.

Retinal images centered on the optic nerve head and corresponding retinal vessel profile data for one subject. A- Depolarized light image. Color bars indicate sampled region. Red bars are for artery profiles and blue for vein profiles. The image is scaled for better visibility, as in Fig. 1, since there is less light return in this image type than in the average images. B- Average image. Red and blue bars indicate sampled region. C- Color fundus image, slightly higher magnification and from a clinical fundus camera. Note central reflections on the largest vein and artery, indicated by black arrows. D- Vessel profiles across one vessel with 11 bisector lines. Left panel- Bisector lines in color on a magnified section of the image in 3A. These lines are selected by a computer algorithm that finds adjacent parallel lines that are perpendicular to the vessel and in the region of interest defined by selecting a pixel on either side of the vessel. Right panel- grayscale of each bisector line plotted as a function of location in the profile. E- Computed data for blood vessel profiles across the retinal vein, plotted as grayscale values as a function of location in the profile. Filled symbols indicate data from veins, open symbols from arteries. Squares- profile from the depolarized image, scaled to match the mean of the average image. Circles- profile from the average image, F- Right column, open symbols- profile across an artery from the same eye as in the left column.

Fig. 4.
Fig. 4.

Computed data for blood vessel profiles across retinal vessels from 9 patients (one vein and one artery from each), plotted as grayscale values as a function of pixels along the cross-sections. Circles- profile from the average image, without scaling, with the grayscale values plotted from 0 – 140 out of a total possible range of 0 – 255. Squares- profile from the depolarized image, scaled to match the mean of the average image. Left column, closed symbols- profile across a vein. Right column, open symbols- profile across an artery from the same eye as in the left column. Note that while the overall curve shapes are similar, there is an elevated portion in the middle of the average image profiles, shown as the circles being elevated above the squares.

Fig. 5.
Fig. 5.

Michelson contrast for vessel profiles for arteries and veins, comparing depolarized and average images. Michelson contrast for the nine individuals, using the midpoint. Symbols: squares- depolarized image, circles- average image, filled symbols- veins, open symbols-arteries. Note that the contrast is typically higher for veins than for arteries and for depolarized light image profiles compared with average image profiles.

Fig. 6.
Fig. 6.

Michelson contrast for vessel profiles for artery and veins, comparing contrast using the midpoint of the vessel profile to that by manually selecting the darkest point. Small triangles-Michelson contrast computed from the baseline retinal grayscale and the midpoint of vessel. Large triangles- maximum possible Michelson contrast, computed from the baseline retinal grayscale and the darkest point on the vessel.

Fig. 7.
Fig. 7.

RMS fitting errors of a single Gaussian curve to each blood vessel profile, showing improved Goodness of Fit for the depolarized light images. The ratio (RMS error for the average images/ RMS error for the depolarized light image) is plotted as a function of the mean vessel diameter measurement from the Gaussian fitting for average and depolarized light images. Filled symbols indicate veins, open symbols arteries. Ratios greater than 1 indicate a poor fit of the Gaussian profiles to average image data compared with depolarized light image data.

Fig. 8.
Fig. 8.

A comparison of the diameters from the half height method to the Gaussian fit half area method. A- Average image. B- Depolarized image. Closed symbols- vein. Open symbols-artery. Note that there is general agreement between the two methods, but fewer outliers with the depolarized light images.

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