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Retinal motion estimation in adaptive optics scanning laser ophthalmoscopy

Curtis R. Vogel, David W. Arathorn, Austin Roorda, and Albert Parker

Open Access Open Access

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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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).

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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]

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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.

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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]

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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.

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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 , ℓ = ∑ i ∑ j E ( i + k , j + ℓ ) E ′ ( i , j ) .
T k , ℓ E ( i , j ) = E ( i + k , j + ℓ ) .
T k , ℓ = T ℓ ( 2 ) T k ( 1 ) .
〈 〈 E , E ′ 〉 〉 = ∑ i ∑ j E ( i , j ) E ′ ( i , j ) ,
corr ( k , ℓ ) = 〈 〈 T ℓ ( 2 ) T k ( 1 ) E , E ′ 〉 〉 .
corr ( g ( 1 ) , g ( 2 ) ) = 〈 〈 ( ∑ ℓ g ℓ ( 2 ) T ℓ ( 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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