Abstract

We apply a novel computational technique known as the map-seeking circuit algorithm to estimate the motion of the retina of eye from a sequence of frames of data from a scanning laser ophthalmoscope. We also present a scheme to dewarp and co-add frames of retinal image data, given the estimated motion. The motion estimation and dewarping techniques are applied to data collected from an adaptive optics scanning laser ophthalmoscopy.

© 2006 Optical Society of America

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References

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  1. A. Roorda, F Romero-Borja, W.J. Donnelly, T.J. Hebert, H. Queener, and M.C.W. Campbell, “Adaptive Optics Scanning Laser Ophthalmoscopy,” Opt. Express 10, 405–412 (2002).
    [PubMed]
  2. J. Liang, D. R. Williams, and D. Miller, “Supernormal vision and high-resolution retinal imaging through adaptive optics,” J. Opt. Soc. Am. A 14, 2884–2892 (1997).
    [CrossRef]
  3. R. H. Webb, G. W. Hughes, and F. C. Delori, “Confocal scanning laser ophthalmoscope,” Appl. Opt. 26, 1492–1499 (1987).
    [CrossRef] [PubMed]
  4. J.B. Mulligan, “Recovery of motion parameters from distortions in scanned images,” Proceedings of the NASA Image Registration Workshop (IRW97), NASA Goddard Space Flight Center, MD, 1997.
  5. S.B. Stevenson and A. Roorda, “Correcting for miniature eye movements in high resolution scanning laser ophthalmoscopy,” in Ophthalmic Technologies XV, edited by Fabrice Manns, Per Soderberg, Arthur Ho, Proceedings of SPIE Vol.  5688A (SPIE, Bellingham, WA, 2005), pp. 145–151.
    [CrossRef]
  6. D. P. Wornson and G. W. Hughes, et al., “Fundus tracking with the scanning laser ophthalmoscope,” Appl. Opt. 26, 1500–1504 (1987).
    [CrossRef] [PubMed]
  7. N. J. O’Connor and D. U. Bartsch, et al, “Fluorescent infrared scanning-laser ophthalmoscope for three-dimensional visualization: automatic random-eye-motion correction and deconvolution,” Appl. Opt. 37, 2021–2033 (1998).
    [CrossRef]
  8. E. Decastro and G. Cristini, et al, “Compensation of random eye motion in television ophthalmoscopy—preliminary results,” IEEE Transactions on Medical Imaging 6, 74–81 (1987).
    [CrossRef]
  9. A. V. Cideciyan, “Registration of ocular fundus images—an algorithm using cross-correlation of triple invariant image descriptors,” IEEE Engineering in Medicine and Biology Magazine 14, 52–58 (1995).
    [CrossRef]
  10. J. Modersitzki, Numerical Methods for Image Registration, (Oxford University Press, 2004).
  11. D.W. Arathorn, Map-Seeking Circuits in Visual Cognition: A Computational Mechanism for Biological and Machine Vision, (Stanford University Press, 2002).
  12. D. W. ARATHORN, “Computation in higher visual cortices: Map-seeking circuit theory and application to machine vision,” Proceedings of IEEE Applied Imagery Pattern Recognition Workshop73–78 (2004).
  13. D. W. ARATHORN, “From wolves hunting elk to Rubik’s cubes: Are the cortices compositional/decompositional engines?” Proceedings of AAAI Symposium on Compositional Connectionism (2004), pp. 1–5.
  14. D. W. ARATHORN, “Memory-driven visual attention: An emergent behavior of map-seeking circuits,” in Neurobiology of Attention, Eds. L. Itti, G. Rees, and J. Tsotsos, (Academic Press/Elsevier, 2005) pp. 605–609.
  15. D. W. ARATHORN, A cortically plausible inverse problem solving method applied to recognizing static and kinematic 3-D objects, proceedings of Neural Information Processing Systems (NIPS) Workshop, (in press).
  16. D. W. Arathorn and T. Gedeon, “Convergence in map finding circuits,” preprint, 2004.
  17. S.A. Harker, T. Gedeon, and C.R. Vogel, “A multilinear optimization problem associated with correspondence maximization,” preprint, 2005.
  18. http://www.math.montana.edu/~vogel/Vision/graphics/
  19. J. A. Martin and A. Roorda, “Direct and n on-invasive assessment of parafoveal capillary leukocyte velocity,” Ophthalmology (in press).
  20. T.N. Cornsweet and H.D. Crane, “Accurate two-dimensional eye tracker using first and fourth Purkinje images,” J. Opt. Soc. Am. 63, 921–928 (1973).
    [CrossRef] [PubMed]

2005 (2)

S.B. Stevenson and A. Roorda, “Correcting for miniature eye movements in high resolution scanning laser ophthalmoscopy,” in Ophthalmic Technologies XV, edited by Fabrice Manns, Per Soderberg, Arthur Ho, Proceedings of SPIE Vol.  5688A (SPIE, Bellingham, WA, 2005), pp. 145–151.
[CrossRef]

D. W. ARATHORN, “Memory-driven visual attention: An emergent behavior of map-seeking circuits,” in Neurobiology of Attention, Eds. L. Itti, G. Rees, and J. Tsotsos, (Academic Press/Elsevier, 2005) pp. 605–609.

2004 (2)

D. W. ARATHORN, “Computation in higher visual cortices: Map-seeking circuit theory and application to machine vision,” Proceedings of IEEE Applied Imagery Pattern Recognition Workshop73–78 (2004).

D. W. ARATHORN, “From wolves hunting elk to Rubik’s cubes: Are the cortices compositional/decompositional engines?” Proceedings of AAAI Symposium on Compositional Connectionism (2004), pp. 1–5.

2002 (1)

1998 (1)

1997 (1)

1995 (1)

A. V. Cideciyan, “Registration of ocular fundus images—an algorithm using cross-correlation of triple invariant image descriptors,” IEEE Engineering in Medicine and Biology Magazine 14, 52–58 (1995).
[CrossRef]

1987 (3)

1973 (1)

ARATHORN, D. W.

D. W. ARATHORN, “Memory-driven visual attention: An emergent behavior of map-seeking circuits,” in Neurobiology of Attention, Eds. L. Itti, G. Rees, and J. Tsotsos, (Academic Press/Elsevier, 2005) pp. 605–609.

D. W. ARATHORN, “Computation in higher visual cortices: Map-seeking circuit theory and application to machine vision,” Proceedings of IEEE Applied Imagery Pattern Recognition Workshop73–78 (2004).

D. W. ARATHORN, “From wolves hunting elk to Rubik’s cubes: Are the cortices compositional/decompositional engines?” Proceedings of AAAI Symposium on Compositional Connectionism (2004), pp. 1–5.

D. W. ARATHORN, A cortically plausible inverse problem solving method applied to recognizing static and kinematic 3-D objects, proceedings of Neural Information Processing Systems (NIPS) Workshop, (in press).

D. W. Arathorn and T. Gedeon, “Convergence in map finding circuits,” preprint, 2004.

Arathorn, D.W.

D.W. Arathorn, Map-Seeking Circuits in Visual Cognition: A Computational Mechanism for Biological and Machine Vision, (Stanford University Press, 2002).

Bartsch, D. U.

Campbell, M.C.W.

Cideciyan, A. V.

A. V. Cideciyan, “Registration of ocular fundus images—an algorithm using cross-correlation of triple invariant image descriptors,” IEEE Engineering in Medicine and Biology Magazine 14, 52–58 (1995).
[CrossRef]

Cornsweet, T.N.

Crane, H.D.

Cristini, G.

E. Decastro and G. Cristini, et al, “Compensation of random eye motion in television ophthalmoscopy—preliminary results,” IEEE Transactions on Medical Imaging 6, 74–81 (1987).
[CrossRef]

Decastro, E.

E. Decastro and G. Cristini, et al, “Compensation of random eye motion in television ophthalmoscopy—preliminary results,” IEEE Transactions on Medical Imaging 6, 74–81 (1987).
[CrossRef]

Delori, F. C.

Donnelly, W.J.

Gedeon, T.

D. W. Arathorn and T. Gedeon, “Convergence in map finding circuits,” preprint, 2004.

S.A. Harker, T. Gedeon, and C.R. Vogel, “A multilinear optimization problem associated with correspondence maximization,” preprint, 2005.

Harker, S.A.

S.A. Harker, T. Gedeon, and C.R. Vogel, “A multilinear optimization problem associated with correspondence maximization,” preprint, 2005.

Hebert, T.J.

Hughes, G. W.

Liang, J.

Martin, J. A.

J. A. Martin and A. Roorda, “Direct and n on-invasive assessment of parafoveal capillary leukocyte velocity,” Ophthalmology (in press).

Miller, D.

Modersitzki, J.

J. Modersitzki, Numerical Methods for Image Registration, (Oxford University Press, 2004).

Mulligan, J.B.

J.B. Mulligan, “Recovery of motion parameters from distortions in scanned images,” Proceedings of the NASA Image Registration Workshop (IRW97), NASA Goddard Space Flight Center, MD, 1997.

O’Connor, N. J.

Queener, H.

Romero-Borja, F

Roorda, A.

S.B. Stevenson and A. Roorda, “Correcting for miniature eye movements in high resolution scanning laser ophthalmoscopy,” in Ophthalmic Technologies XV, edited by Fabrice Manns, Per Soderberg, Arthur Ho, Proceedings of SPIE Vol.  5688A (SPIE, Bellingham, WA, 2005), pp. 145–151.
[CrossRef]

A. Roorda, F Romero-Borja, W.J. Donnelly, T.J. Hebert, H. Queener, and M.C.W. Campbell, “Adaptive Optics Scanning Laser Ophthalmoscopy,” Opt. Express 10, 405–412 (2002).
[PubMed]

J. A. Martin and A. Roorda, “Direct and n on-invasive assessment of parafoveal capillary leukocyte velocity,” Ophthalmology (in press).

Stevenson, S.B.

S.B. Stevenson and A. Roorda, “Correcting for miniature eye movements in high resolution scanning laser ophthalmoscopy,” in Ophthalmic Technologies XV, edited by Fabrice Manns, Per Soderberg, Arthur Ho, Proceedings of SPIE Vol.  5688A (SPIE, Bellingham, WA, 2005), pp. 145–151.
[CrossRef]

Vogel, C.R.

S.A. Harker, T. Gedeon, and C.R. Vogel, “A multilinear optimization problem associated with correspondence maximization,” preprint, 2005.

Webb, R. H.

Williams, D. R.

Wornson, D. P.

Appl. Opt. (3)

IEEE Engineering in Medicine and Biology Magazine (1)

A. V. Cideciyan, “Registration of ocular fundus images—an algorithm using cross-correlation of triple invariant image descriptors,” IEEE Engineering in Medicine and Biology Magazine 14, 52–58 (1995).
[CrossRef]

IEEE Transactions on Medical Imaging (1)

E. Decastro and G. Cristini, et al, “Compensation of random eye motion in television ophthalmoscopy—preliminary results,” IEEE Transactions on Medical Imaging 6, 74–81 (1987).
[CrossRef]

J. Opt. Soc. Am. (1)

J. Opt. Soc. Am. A (1)

Opt. Express (1)

Proceedings of AAAI Symposium on Compositional Connectionism (1)

D. W. ARATHORN, “From wolves hunting elk to Rubik’s cubes: Are the cortices compositional/decompositional engines?” Proceedings of AAAI Symposium on Compositional Connectionism (2004), pp. 1–5.

Proceedings of IEEE Applied Imagery Pattern Recognition Workshop (1)

D. W. ARATHORN, “Computation in higher visual cortices: Map-seeking circuit theory and application to machine vision,” Proceedings of IEEE Applied Imagery Pattern Recognition Workshop73–78 (2004).

Proceedings of SPIE (1)

S.B. Stevenson and A. Roorda, “Correcting for miniature eye movements in high resolution scanning laser ophthalmoscopy,” in Ophthalmic Technologies XV, edited by Fabrice Manns, Per Soderberg, Arthur Ho, Proceedings of SPIE Vol.  5688A (SPIE, Bellingham, WA, 2005), pp. 145–151.
[CrossRef]

Other (9)

D. W. ARATHORN, “Memory-driven visual attention: An emergent behavior of map-seeking circuits,” in Neurobiology of Attention, Eds. L. Itti, G. Rees, and J. Tsotsos, (Academic Press/Elsevier, 2005) pp. 605–609.

D. W. ARATHORN, A cortically plausible inverse problem solving method applied to recognizing static and kinematic 3-D objects, proceedings of Neural Information Processing Systems (NIPS) Workshop, (in press).

D. W. Arathorn and T. Gedeon, “Convergence in map finding circuits,” preprint, 2004.

S.A. Harker, T. Gedeon, and C.R. Vogel, “A multilinear optimization problem associated with correspondence maximization,” preprint, 2005.

http://www.math.montana.edu/~vogel/Vision/graphics/

J. A. Martin and A. Roorda, “Direct and n on-invasive assessment of parafoveal capillary leukocyte velocity,” Ophthalmology (in press).

J.B. Mulligan, “Recovery of motion parameters from distortions in scanned images,” Proceedings of the NASA Image Registration Workshop (IRW97), NASA Goddard Space Flight Center, MD, 1997.

J. Modersitzki, Numerical Methods for Image Registration, (Oxford University Press, 2004).

D.W. Arathorn, Map-Seeking Circuits in Visual Cognition: A Computational Mechanism for Biological and Machine Vision, (Stanford University Press, 2002).

Supplementary Material (2)

» Media 1: AVI (3011 KB)     
» Media 2: AVI (3424 KB)     

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Figures (5)

Fig. 1.
Fig. 1.

Illustration of transformation effects. Under the transformation T, straight lines in the rectangular grid on the left map to curved lines on the right. Under the inverse transformation T -1, equispaced grid points on the right (blue dots) map back to non equispaced points on the left (red dots).

Fig. 2.
Fig. 2.

Sample frame from the raw video clip SLD-AR.avi. This clip consists of 24 image frames and the file size is 3.1 MB. The image size is 350 × 350 pixels, or 1.02 × 1.02 degrees, or 300 × 300 microns. The fovea is located 400 microns up and to the left of the frame. [Media 1]

Fig. 3.
Fig. 3.

Horizontal and vertical motion estimates obtained from AOSLO data. One pixel corresponds to .17 minutes or arc, or .88 microns of planar distance across the retina. The .8 second duration of the motion corresponds to 24 frames of AOSLO data.

Fig. 4.
Fig. 4.

Sample frame from dewarped video clip SLD-AR-dewarp.avi. This clip consists of 24 image frames and the file size is 3.5 MB. The image statistics are the same as in Fig. 2. [Media 2]

Fig. 5.
Fig. 5.

Raw image (top) and co-added image (bottom) obtained from AOSLO data. Image statistics are the same as in Fig. 2. Note the honeycomb structure known as a cone mosaic in the co-added image.

Equations (13)

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d ( t ) = E ( r ( t ) + X ( t ) ) .
d i = E ( r ( t i ) + X ( t i ) ) + η i ,
X ( t ) = X ( t 0 ) + ( t t 0 ) v ,
E ( x ( t i + τ f ) ) = E ( x ( t i ) + τ f v ) + noise .
corr ( E , E ) k , = i j E ( i + k , j + ) E ( i , j ) .
T k , E ( i , j ) = E ( i + k , j + ) .
T k , = T ( 2 ) T k ( 1 ) .
E , E = i j E ( i , j ) E ( i , j ) ,
corr ( k , ) = T ( 2 ) T k ( 1 ) E , E .
corr ( g ( 1 ) , g ( 2 ) ) = ( g ( 2 ) T ( 2 ) ) ( k g k ( 1 ) T k ( 1 ) ) E , E .
E ( x ) = E ( x ) , where x = T 1 x .
1 T 0 T X true ( t ) dt ,
1 N n = 1 N X ( t + n τ s ) X bias ( t )

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