Abstract

We present a new signal processing method that extracts the reference spectrum information from an acquired optical coherence tomography (OCT) image without a separate calibration step of reference spectrum measurement. The reference spectrum is used to remove the fixed-pattern noise that is a characteristic artifact of Fourier-domain OCT schemes. It was found that the conventional approach based on an averaged spectrum, or mean spectrum, is prone to be influenced by the high-amplitude data points whose statistical distribution is hardly randomized. Thus, the conventional mean-spectrum subtraction method cannot completely eliminate the artifact but may leave residual horizontal lines in the final image. This problem was avoided by utilizing an advanced statistical analysis tool of the median A-line. The reference A-line was obtained by taking a complex median of each horizontal-line data. As an optional method of high-speed calculation, we also propose a minimum-variance mean A-line that can be calculated from an image by a collection of mean A-line values taken from a horizontal segment whose complex variance of the data points is the minimum. By comparing the images processed by those methods, it was found that our new processing schemes of the median-line subtraction and the minimum-variance mean-line subtraction successfully suppressed the fixed-pattern noise. The inverse Fourier transform of the obtained reference A-line well matched the reference spectrum obtained by a physical measurement as well.

© 2010 OSA

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References

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2004 (2)

2003 (3)

2002 (1)

Bouma, B.

Bouma, B. E.

Cense, B.

Chen, T.

de Boer, J.

de Boer, J. F.

Fercher, A. F.

Hitzenberger, C. K.

Iftimia, N.

Leitgeb, R.

Nassif, N.

Nelson, J. S.

Park, B.

Park, B. H.

Pierce, M.

Pierce, M. C.

Podoleanu, A. G.

Rosa, C. C.

Tearney, G.

Tearney, G. J.

Tripathi, R.

Yun, S.

Appl. Opt. (1)

Opt. Express (3)

Opt. Lett. (2)

Other (3)

R. Langley, Practical Statistics (Dover Publications, 1971).

J. F. de Boer, “Spectral/Fourier domain optical coherence tomography,” in Optical Coherence Tomography, Technology and Applications, Wolfgang Drexler, and James G. Fujimoto, eds. (Springer, 2008), pp. 147–175.

R. A. Leitgeb, and M. Wojtkowski, “Complex and coherent noise free Fourier domain optical coherence tomography,” in Optical Coherence Tomography, Technology and Applications, Wolfgang Drexler, and James G. Fujimoto, eds. (Springer, 2008), pp. 177–207.

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Figures (5)

Fig. 1
Fig. 1

Raw OCT image (a), image processed by the physically measured reference (b), and the OCT image processed by the conventional mean-spectrum subtraction method (c) for the same OCT data of a human finger tip.

Fig. 2
Fig. 2

Complex-plane representation of the A-line data (a), their histogram on the real axis (b), their histogram on the imaginary axis (c), and the magnified version ( × 20) of the complex plane for the central area (d). The data are from the 162nd line of the raw OCT image in Fig. 1 (a).

Fig. 3
Fig. 3

OCT image obtained by the physical reference spectrum measurement (a), image processed by the median-line subtraction (b), and the image processed by the conventional mean-spectrum subtraction (c), displayed together for comparison.

Fig. 4
Fig. 4

Reference spectra obtained by a physical calibration (blue dots), transform of a median line (black solid line), and the difference between the two spectra.

Fig. 5
Fig. 5

OCT image processed by mean-spectrum subtraction (a), image obtained by median-line subtraction (b), and the image processed by minimum-variance mean-line subtraction (c) for an IR detection card.

Equations (7)

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G ( k ) = | E r | 2 + n | E n | 2 + n 2 | E r E n | + n m 2 | E n E m | ,
G 1 N l = 1 N G l ( k ) | E r | 2 + n | E n | 2 l ,
F { 1 N l G l ( k ) } = 1 N l F { G l ( k ) } ,
g 1 N l = 1 N g l ( z ) 1 N l = 1 N F { G l ( k ) } ,
g ( z ) = med { Re { g l } } l + i med { Im { g l } } l ,
g Ω ( z ) = 1 L l Ω g l ( z ) ,
v Ω = 1 L l Ω ( Re { g l } Re { g Ω } ) 2 + 1 L l Ω ( Im { g l } Im { g Ω } ) 2 ,

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