Abstract

Optical coherence tomography (OCT) has the potential for skin tissue characterization due to its high axial and transverse resolution and its acceptable depth penetration. In practice, OCT cannot reach the theoretical resolutions due to imperfections of some of the components used. One way to improve the quality of the images is to estimate the point spread function (PSF) of the OCT system and deconvolve it from the output images. In this paper, we investigate the use of solid phantoms to estimate the PSF of the imaging system. We then utilize iterative Lucy–Richardson deconvolution algorithm to improve the quality of the images. The performance of the proposed algorithm is demonstrated on OCT images acquired from a variety of samples, such as epoxy-resin phantoms, fingertip skin and basaloid larynx and eyelid tissues.

© 2013 Optical Society of America

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  8. T. S. Ralston, D. L. Marks, F. Kamalabadi, and S. A. Boppart, “Deconvolution methods for mitigation of transversal blurring in optical coherence tomography,” IEEE Trans. Image Process. 14, 1254–1264 (2005).
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  25. A. Hojjatoleslami and M. R. Nasiriavanaki, “OCT skin image enhancement through attenuation compensation,” Appl. Opt. 51, 4927–4935 (2012).
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  27. M. R. N. Avanaki, M. Sira, S. A. Hojjatoleslami, A. Aber, J. B. Schofield, C. Jones, and A. Gh. Podoleanu, “Improved imaging of basal cell carcinoma using dynamic focus optical coherence tomography,” J. Invest. Dermatol. 131, S38 (2011).

2013

M. R. N. Avanaki, A. Bradu, I. Trifanov, A. B. Lobo Ribeiro, A. Hojjatoleslami, and A. G. Podoleanu, “Algorithm for excitation optimization of Fabry–Perot filters used in swept sources,” IEEE Photon. Technol. Lett. 25, 472–475 (2013).
[CrossRef]

M. R. N. Avanaki, R. Cernat, P. J. Tadrous, T. Tatla, A. Gh. Podoleanu, and S. A. Hojjatoleslami, “Spatial compounding algorithm for speckle reduction of dynamic focus OCT images,” IEEE Photon. Technol. Lett. 25, 1439–1442 (2013).

M. R. N. Avanaki, A. Gh. Podoleanu, J. B. Schofield, C. Jones, M. Sira, Y. Liu, and A. Hojjat, “Quantitative evaluation of scattering in optical coherence tomography skin images using the extended Huygens–Fresnel theorem,” Appl. Opt. 52, 1574–1580 (2013).
[CrossRef]

M. Avanaki, P. P. Laissue, T. J. Eom, A. Gh. Podoleanu, and A. Hojjatoleslami, “Speckle reduction using an artificial neural network algorithm,” Appl. Opt. 52, 5050–5057 (2013).
[CrossRef]

2012

2011

M. Laasmaa, M. Vendelin, and P. Peterson, “Application of regularized Richardson-Lucy algorithm for deconvolution of confocal microscopy images,” J. Microsc. 243, 124–140 (2011).
[CrossRef]

M. R. N. Avanaki, M. Sira, S. A. Hojjatoleslami, A. Aber, J. B. Schofield, C. Jones, and A. Gh. Podoleanu, “Improved imaging of basal cell carcinoma using dynamic focus optical coherence tomography,” J. Invest. Dermatol. 131, S38 (2011).

2009

M. Hughes and A. G. Podoleanu, “Simplified dynamic focus method for time domain OCT,” Electron. Lett. 45, 623–624 (2009).
[CrossRef]

M. Avanaki, S. Hojjatoleslami, and A. Podoleanu, “Investigation of computer-based skin cancer detection using optical coherence tomography,” J. Mod. Opt. 56, 1536–1544 (2009).
[CrossRef]

Y. Liu, Y. Liang, G. Mu, and X. Zhu, “Deconvolution methods for image deblurring in optical coherence tomography,” J. Opt. Soc. Am. A 26, 72–77 (2009).
[CrossRef]

2007

A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. Am. A 24, 1901–1910 (2007).
[CrossRef]

Y. Liu, Y. Liang, Z. Tong, X. Zhu, and G. Mu, “Contrast enhancement of optical coherence tomography images using least squares fitting and histogram matching,” Opt. Commun. 279, 23–26 (2007).
[CrossRef]

2005

A. G. Podoleanu, “Optical coherence tomography,” Br. J. Radiol. 78, 976–988 (2005).
[CrossRef]

T. S. Ralston, D. L. Marks, F. Kamalabadi, and S. A. Boppart, “Deconvolution methods for mitigation of transversal blurring in optical coherence tomography,” IEEE Trans. Image Process. 14, 1254–1264 (2005).
[CrossRef]

2002

J. Starck, E. Pantin, and F. Murtagh, “Deconvolution in astronomy: a review,” Publ. Astron. Soc. Pac. 114, 1051–1069 (2002).
[CrossRef]

1999

J. Schmitt, “Optical coherence tomography (OCT): a review,” IEEE J. Sel. Top. Quantum Electron. 5, 1205–1215 (1999).
[CrossRef]

1997

M. D. Kulkarni, J. A. Izatt, and M. V. Sivak, “Image enhancement in optical coherence tomography using deconvolution,” Electron. Lett. 33, 1365–1367 (1997).
[CrossRef]

J. M. Schmitt and Z. Liang, “Deconvolution and enhancement of optical coherence tomograms,” Proc. SPIE 2981, 46–57 (1997).
[CrossRef]

1995

1985

1982

L. A. Shepp and Y. Vardi, “Maximum likelihood reconstruction for emission tomography,” IEEE Trans. Med. Imaging 1, 113–122 (1982).
[CrossRef]

1974

L. B. Lucy, “An iterative technique for the rectification of observed distributions,” Astron. J. 79, 745–754 (1974).
[CrossRef]

1972

Aber, A.

M. R. N. Avanaki, M. Sira, S. A. Hojjatoleslami, A. Aber, J. B. Schofield, C. Jones, and A. Gh. Podoleanu, “Improved imaging of basal cell carcinoma using dynamic focus optical coherence tomography,” J. Invest. Dermatol. 131, S38 (2011).

Agrawal, A.

Avanaki, M.

M. Avanaki, P. P. Laissue, T. J. Eom, A. Gh. Podoleanu, and A. Hojjatoleslami, “Speckle reduction using an artificial neural network algorithm,” Appl. Opt. 52, 5050–5057 (2013).
[CrossRef]

M. Avanaki, S. Hojjatoleslami, and A. Podoleanu, “Investigation of computer-based skin cancer detection using optical coherence tomography,” J. Mod. Opt. 56, 1536–1544 (2009).
[CrossRef]

Avanaki, M. R. N.

M. R. N. Avanaki, A. Gh. Podoleanu, J. B. Schofield, C. Jones, M. Sira, Y. Liu, and A. Hojjat, “Quantitative evaluation of scattering in optical coherence tomography skin images using the extended Huygens–Fresnel theorem,” Appl. Opt. 52, 1574–1580 (2013).
[CrossRef]

M. R. N. Avanaki, R. Cernat, P. J. Tadrous, T. Tatla, A. Gh. Podoleanu, and S. A. Hojjatoleslami, “Spatial compounding algorithm for speckle reduction of dynamic focus OCT images,” IEEE Photon. Technol. Lett. 25, 1439–1442 (2013).

M. R. N. Avanaki, A. Bradu, I. Trifanov, A. B. Lobo Ribeiro, A. Hojjatoleslami, and A. G. Podoleanu, “Algorithm for excitation optimization of Fabry–Perot filters used in swept sources,” IEEE Photon. Technol. Lett. 25, 472–475 (2013).
[CrossRef]

M. R. N. Avanaki, M. Sira, S. A. Hojjatoleslami, A. Aber, J. B. Schofield, C. Jones, and A. Gh. Podoleanu, “Improved imaging of basal cell carcinoma using dynamic focus optical coherence tomography,” J. Invest. Dermatol. 131, S38 (2011).

Barton, D.

Beylin, A.

Bilenca, A.

Bonner, R.

Boppart, S. A.

T. S. Ralston, D. L. Marks, F. Kamalabadi, and S. A. Boppart, “Deconvolution methods for mitigation of transversal blurring in optical coherence tomography,” IEEE Trans. Image Process. 14, 1254–1264 (2005).
[CrossRef]

Bouma, B. E.

Bradu, A.

M. R. N. Avanaki, A. Bradu, I. Trifanov, A. B. Lobo Ribeiro, A. Hojjatoleslami, and A. G. Podoleanu, “Algorithm for excitation optimization of Fabry–Perot filters used in swept sources,” IEEE Photon. Technol. Lett. 25, 472–475 (2013).
[CrossRef]

Brinicombe, A.

Cernat, R.

M. R. N. Avanaki, R. Cernat, P. J. Tadrous, T. Tatla, A. Gh. Podoleanu, and S. A. Hojjatoleslami, “Spatial compounding algorithm for speckle reduction of dynamic focus OCT images,” IEEE Photon. Technol. Lett. 25, 1439–1442 (2013).

Chen, Y.

Connors, M.

Desjardins, A. E.

Dhawan, A. P.

Drezek, R. A.

Eddins, S. L.

R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB (Pearson Education India, 2004).

Eom, T. J.

Fish, D.

Gonzalez, R. C.

R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB (Pearson Education India, 2004).

Gordon, R.

Hojjat, A.

Hojjatoleslami, A.

M. Avanaki, P. P. Laissue, T. J. Eom, A. Gh. Podoleanu, and A. Hojjatoleslami, “Speckle reduction using an artificial neural network algorithm,” Appl. Opt. 52, 5050–5057 (2013).
[CrossRef]

M. R. N. Avanaki, A. Bradu, I. Trifanov, A. B. Lobo Ribeiro, A. Hojjatoleslami, and A. G. Podoleanu, “Algorithm for excitation optimization of Fabry–Perot filters used in swept sources,” IEEE Photon. Technol. Lett. 25, 472–475 (2013).
[CrossRef]

A. Hojjatoleslami and M. R. Nasiriavanaki, “OCT skin image enhancement through attenuation compensation,” Appl. Opt. 51, 4927–4935 (2012).
[CrossRef]

Hojjatoleslami, S.

M. Avanaki, S. Hojjatoleslami, and A. Podoleanu, “Investigation of computer-based skin cancer detection using optical coherence tomography,” J. Mod. Opt. 56, 1536–1544 (2009).
[CrossRef]

Hojjatoleslami, S. A.

M. R. N. Avanaki, R. Cernat, P. J. Tadrous, T. Tatla, A. Gh. Podoleanu, and S. A. Hojjatoleslami, “Spatial compounding algorithm for speckle reduction of dynamic focus OCT images,” IEEE Photon. Technol. Lett. 25, 1439–1442 (2013).

M. R. N. Avanaki, M. Sira, S. A. Hojjatoleslami, A. Aber, J. B. Schofield, C. Jones, and A. Gh. Podoleanu, “Improved imaging of basal cell carcinoma using dynamic focus optical coherence tomography,” J. Invest. Dermatol. 131, S38 (2011).

Hughes, M.

M. Hughes and A. G. Podoleanu, “Simplified dynamic focus method for time domain OCT,” Electron. Lett. 45, 623–624 (2009).
[CrossRef]

Izatt, J. A.

M. D. Kulkarni, J. A. Izatt, and M. V. Sivak, “Image enhancement in optical coherence tomography using deconvolution,” Electron. Lett. 33, 1365–1367 (1997).
[CrossRef]

Jones, C.

M. R. N. Avanaki, A. Gh. Podoleanu, J. B. Schofield, C. Jones, M. Sira, Y. Liu, and A. Hojjat, “Quantitative evaluation of scattering in optical coherence tomography skin images using the extended Huygens–Fresnel theorem,” Appl. Opt. 52, 1574–1580 (2013).
[CrossRef]

M. R. N. Avanaki, M. Sira, S. A. Hojjatoleslami, A. Aber, J. B. Schofield, C. Jones, and A. Gh. Podoleanu, “Improved imaging of basal cell carcinoma using dynamic focus optical coherence tomography,” J. Invest. Dermatol. 131, S38 (2011).

Kamalabadi, F.

T. S. Ralston, D. L. Marks, F. Kamalabadi, and S. A. Boppart, “Deconvolution methods for mitigation of transversal blurring in optical coherence tomography,” IEEE Trans. Image Process. 14, 1254–1264 (2005).
[CrossRef]

Kulkarni, M. D.

M. D. Kulkarni, J. A. Izatt, and M. V. Sivak, “Image enhancement in optical coherence tomography using deconvolution,” Electron. Lett. 33, 1365–1367 (1997).
[CrossRef]

Laasmaa, M.

M. Laasmaa, M. Vendelin, and P. Peterson, “Application of regularized Richardson-Lucy algorithm for deconvolution of confocal microscopy images,” J. Microsc. 243, 124–140 (2011).
[CrossRef]

Laissue, P. P.

Liang, C.-P.

Liang, Y.

Y. Liu, Y. Liang, G. Mu, and X. Zhu, “Deconvolution methods for image deblurring in optical coherence tomography,” J. Opt. Soc. Am. A 26, 72–77 (2009).
[CrossRef]

Y. Liu, Y. Liang, Z. Tong, X. Zhu, and G. Mu, “Contrast enhancement of optical coherence tomography images using least squares fitting and histogram matching,” Opt. Commun. 279, 23–26 (2007).
[CrossRef]

Liang, Z.

J. M. Schmitt and Z. Liang, “Deconvolution and enhancement of optical coherence tomograms,” Proc. SPIE 2981, 46–57 (1997).
[CrossRef]

Liu, Y.

Lobo Ribeiro, A. B.

M. R. N. Avanaki, A. Bradu, I. Trifanov, A. B. Lobo Ribeiro, A. Hojjatoleslami, and A. G. Podoleanu, “Algorithm for excitation optimization of Fabry–Perot filters used in swept sources,” IEEE Photon. Technol. Lett. 25, 472–475 (2013).
[CrossRef]

Lucy, L. B.

L. B. Lucy, “An iterative technique for the rectification of observed distributions,” Astron. J. 79, 745–754 (1974).
[CrossRef]

Marks, D. L.

T. S. Ralston, D. L. Marks, F. Kamalabadi, and S. A. Boppart, “Deconvolution methods for mitigation of transversal blurring in optical coherence tomography,” IEEE Trans. Image Process. 14, 1254–1264 (2005).
[CrossRef]

Mu, G.

Y. Liu, Y. Liang, G. Mu, and X. Zhu, “Deconvolution methods for image deblurring in optical coherence tomography,” J. Opt. Soc. Am. A 26, 72–77 (2009).
[CrossRef]

Y. Liu, Y. Liang, Z. Tong, X. Zhu, and G. Mu, “Contrast enhancement of optical coherence tomography images using least squares fitting and histogram matching,” Opt. Commun. 279, 23–26 (2007).
[CrossRef]

Murtagh, F.

J. Starck, E. Pantin, and F. Murtagh, “Deconvolution in astronomy: a review,” Publ. Astron. Soc. Pac. 114, 1051–1069 (2002).
[CrossRef]

Nasiriavanaki, M. R.

Ozcan, A.

Pantin, E.

J. Starck, E. Pantin, and F. Murtagh, “Deconvolution in astronomy: a review,” Publ. Astron. Soc. Pac. 114, 1051–1069 (2002).
[CrossRef]

Peterson, P.

M. Laasmaa, M. Vendelin, and P. Peterson, “Application of regularized Richardson-Lucy algorithm for deconvolution of confocal microscopy images,” J. Microsc. 243, 124–140 (2011).
[CrossRef]

Pfefer, T. J.

Pike, E.

Podoleanu, A.

M. Avanaki, S. Hojjatoleslami, and A. Podoleanu, “Investigation of computer-based skin cancer detection using optical coherence tomography,” J. Mod. Opt. 56, 1536–1544 (2009).
[CrossRef]

Podoleanu, A. G.

M. R. N. Avanaki, A. Bradu, I. Trifanov, A. B. Lobo Ribeiro, A. Hojjatoleslami, and A. G. Podoleanu, “Algorithm for excitation optimization of Fabry–Perot filters used in swept sources,” IEEE Photon. Technol. Lett. 25, 472–475 (2013).
[CrossRef]

M. Hughes and A. G. Podoleanu, “Simplified dynamic focus method for time domain OCT,” Electron. Lett. 45, 623–624 (2009).
[CrossRef]

A. G. Podoleanu, “Optical coherence tomography,” Br. J. Radiol. 78, 976–988 (2005).
[CrossRef]

Podoleanu, A. Gh.

M. R. N. Avanaki, A. Gh. Podoleanu, J. B. Schofield, C. Jones, M. Sira, Y. Liu, and A. Hojjat, “Quantitative evaluation of scattering in optical coherence tomography skin images using the extended Huygens–Fresnel theorem,” Appl. Opt. 52, 1574–1580 (2013).
[CrossRef]

M. R. N. Avanaki, R. Cernat, P. J. Tadrous, T. Tatla, A. Gh. Podoleanu, and S. A. Hojjatoleslami, “Spatial compounding algorithm for speckle reduction of dynamic focus OCT images,” IEEE Photon. Technol. Lett. 25, 1439–1442 (2013).

M. Avanaki, P. P. Laissue, T. J. Eom, A. Gh. Podoleanu, and A. Hojjatoleslami, “Speckle reduction using an artificial neural network algorithm,” Appl. Opt. 52, 5050–5057 (2013).
[CrossRef]

M. R. N. Avanaki, M. Sira, S. A. Hojjatoleslami, A. Aber, J. B. Schofield, C. Jones, and A. Gh. Podoleanu, “Improved imaging of basal cell carcinoma using dynamic focus optical coherence tomography,” J. Invest. Dermatol. 131, S38 (2011).

Ralston, T. S.

T. S. Ralston, D. L. Marks, F. Kamalabadi, and S. A. Boppart, “Deconvolution methods for mitigation of transversal blurring in optical coherence tomography,” IEEE Trans. Image Process. 14, 1254–1264 (2005).
[CrossRef]

Rangayyan, R. M.

Richardson, W. H.

Schmitt, J.

J. Schmitt, “Optical coherence tomography (OCT): a review,” IEEE J. Sel. Top. Quantum Electron. 5, 1205–1215 (1999).
[CrossRef]

M. Yadlowsky, J. Schmitt, and R. Bonner, “Multiple scattering in optical coherence microscopy,” Appl. Opt. 34, 5699–5707 (1995).
[CrossRef]

Schmitt, J. M.

J. M. Schmitt and Z. Liang, “Deconvolution and enhancement of optical coherence tomograms,” Proc. SPIE 2981, 46–57 (1997).
[CrossRef]

Schofield, J. B.

M. R. N. Avanaki, A. Gh. Podoleanu, J. B. Schofield, C. Jones, M. Sira, Y. Liu, and A. Hojjat, “Quantitative evaluation of scattering in optical coherence tomography skin images using the extended Huygens–Fresnel theorem,” Appl. Opt. 52, 1574–1580 (2013).
[CrossRef]

M. R. N. Avanaki, M. Sira, S. A. Hojjatoleslami, A. Aber, J. B. Schofield, C. Jones, and A. Gh. Podoleanu, “Improved imaging of basal cell carcinoma using dynamic focus optical coherence tomography,” J. Invest. Dermatol. 131, S38 (2011).

Shepp, L. A.

L. A. Shepp and Y. Vardi, “Maximum likelihood reconstruction for emission tomography,” IEEE Trans. Med. Imaging 1, 113–122 (1982).
[CrossRef]

Sira, M.

M. R. N. Avanaki, A. Gh. Podoleanu, J. B. Schofield, C. Jones, M. Sira, Y. Liu, and A. Hojjat, “Quantitative evaluation of scattering in optical coherence tomography skin images using the extended Huygens–Fresnel theorem,” Appl. Opt. 52, 1574–1580 (2013).
[CrossRef]

M. R. N. Avanaki, M. Sira, S. A. Hojjatoleslami, A. Aber, J. B. Schofield, C. Jones, and A. Gh. Podoleanu, “Improved imaging of basal cell carcinoma using dynamic focus optical coherence tomography,” J. Invest. Dermatol. 131, S38 (2011).

Sivak, M. V.

M. D. Kulkarni, J. A. Izatt, and M. V. Sivak, “Image enhancement in optical coherence tomography using deconvolution,” Electron. Lett. 33, 1365–1367 (1997).
[CrossRef]

Starck, J.

J. Starck, E. Pantin, and F. Murtagh, “Deconvolution in astronomy: a review,” Publ. Astron. Soc. Pac. 114, 1051–1069 (2002).
[CrossRef]

Tadrous, P. J.

M. R. N. Avanaki, R. Cernat, P. J. Tadrous, T. Tatla, A. Gh. Podoleanu, and S. A. Hojjatoleslami, “Spatial compounding algorithm for speckle reduction of dynamic focus OCT images,” IEEE Photon. Technol. Lett. 25, 1439–1442 (2013).

Tatla, T.

M. R. N. Avanaki, R. Cernat, P. J. Tadrous, T. Tatla, A. Gh. Podoleanu, and S. A. Hojjatoleslami, “Spatial compounding algorithm for speckle reduction of dynamic focus OCT images,” IEEE Photon. Technol. Lett. 25, 1439–1442 (2013).

Tearney, G. J.

Tong, Z.

Y. Liu, Y. Liang, Z. Tong, X. Zhu, and G. Mu, “Contrast enhancement of optical coherence tomography images using least squares fitting and histogram matching,” Opt. Commun. 279, 23–26 (2007).
[CrossRef]

Trifanov, I.

M. R. N. Avanaki, A. Bradu, I. Trifanov, A. B. Lobo Ribeiro, A. Hojjatoleslami, and A. G. Podoleanu, “Algorithm for excitation optimization of Fabry–Perot filters used in swept sources,” IEEE Photon. Technol. Lett. 25, 472–475 (2013).
[CrossRef]

Vardi, Y.

L. A. Shepp and Y. Vardi, “Maximum likelihood reconstruction for emission tomography,” IEEE Trans. Med. Imaging 1, 113–122 (1982).
[CrossRef]

Vendelin, M.

M. Laasmaa, M. Vendelin, and P. Peterson, “Application of regularized Richardson-Lucy algorithm for deconvolution of confocal microscopy images,” J. Microsc. 243, 124–140 (2011).
[CrossRef]

Walker, J.

Woods, R. E.

R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB (Pearson Education India, 2004).

Yadlowsky, M.

Zhu, X.

Y. Liu, Y. Liang, G. Mu, and X. Zhu, “Deconvolution methods for image deblurring in optical coherence tomography,” J. Opt. Soc. Am. A 26, 72–77 (2009).
[CrossRef]

Y. Liu, Y. Liang, Z. Tong, X. Zhu, and G. Mu, “Contrast enhancement of optical coherence tomography images using least squares fitting and histogram matching,” Opt. Commun. 279, 23–26 (2007).
[CrossRef]

Appl. Opt.

Astron. J.

L. B. Lucy, “An iterative technique for the rectification of observed distributions,” Astron. J. 79, 745–754 (1974).
[CrossRef]

Biomed. Opt. Express

Br. J. Radiol.

A. G. Podoleanu, “Optical coherence tomography,” Br. J. Radiol. 78, 976–988 (2005).
[CrossRef]

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Figures (10)

Fig. 1.
Fig. 1.

Dynamic focus time domain OCT optical setup. SLD, super luminescent laser diode; BD, balance detection receiver, equipped with two photodetectors and a differential amplifier; C1 and 2, 2 × 2 couplers; CL1, 2, and 3, collimator lenses; MPC, mirror positioning controller; PC1 and 2, polarization controllers; TS, translation stage; OF, optical fiber.

Fig. 2.
Fig. 2.

Flowchart of the proposed image quality improvement method.

Fig. 3.
Fig. 3.

Averaged OCT C-scan image obtained from the phantom described in Section 2.B, acquired from a depth of 100 μm. The red squares surrounding the spots are chosen to calculate the averaged PSF image (reliable spots). Inset: the enlarged view of one of the reliable spots. The size of the zoomed inset is 60 μm × 60 μm ( 30 × 30 pixels).

Fig. 4.
Fig. 4.

PSF of the OCT system. 3D presentation of (a) averaged PSF image, (b) processed averaged PSF image, and (c) modeled PSF image.

Fig. 5.
Fig. 5.

OCT images of skin before and after applying the deconvolution algorithm with 25 iterations of the Lucy–Richardson algorithm. (a) Averaged C-scan OCT image acquired at a depth of 100 μm from the dorsal skin of a finger of a 28-year old Asian male (type II), (b) the improved image using the processed averaged PSF image, and (c) the improved image using the modeled PSF image. The orange arrows indicate the regions where the details of the structure were improved.

Fig. 6.
Fig. 6.

OCT images of human tissues before and after the deconvolution algorithm. The left image in each row is the original image and the right image is the improved image. The images acquired are from (a) the BCC-affected eyelid of a white 78-year old male (skin type II) (after 28 iterations), (b) the skin of fingertip of a healthy white 28-year old male (skin type III) (after 25 iterations), and (c) and (d) BCC affected larynx tissue (after 26 and 30 iterations, respectively). The images are improved using the processed averaged PSF image.

Fig. 7.
Fig. 7.

Comparison of transverse resolution between (a) original C-scan image and (b) improved image after applying the Lucy–Richardson algorithm, 27 iterations. The dotted box in the images includes a reliable spot for the transverse resolution assessment. The images are improved using the processed averaged PSF image.

Fig. 8.
Fig. 8.

Comparison between the FWHM of the intensity profiles of a line in the original and improved images in Fig. 7. The transverse resolution for the original and the improved images has been measured from the intensity profile of the line scan indicated in Fig. 7 [23].

Fig. 9.
Fig. 9.

SNR of the original and improved images. The images are improved using Lucy–Richardson algorithm and two PSF images (processed averaged PSF and modeled PSF). The images were taken from depths of 10 to 300 μm of the dorsal skin of a finger of a healthy individual of age 45 with skin type (II) (images were acquired at 10 μm axial intervals measured in air).

Fig. 10.
Fig. 10.

CNR of the original and improved images. The images are improved using the Lucy–Richardson algorithm and two PSF images (processed averaged PSF and modeled PSF). The images were taken from depths of 10 to 300 μm of the dorsal skin of a finger of a healthy individual of age 45 with skin type (II) (images were acquired at 10 μm axial intervals measured in air).

Tables (1)

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Table 1. Statistical Properties Estimated from the Processed Averaged PSF Image in Fig. 4(b)

Equations (6)

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f ( x , y ) h ( x , y ) = g ( x , y ) ,
F 1 ( F ( k x , k y ) ) = F 1 ( g ( k x , k y ) ) / H ( k x , k y ) ,
f m + 1 ( x , y ) = f m ( x , y ) [ h ( x , y ) g ( x , y ) h ( x , y ) f m ( x , y ) ] ,
PSF ( i , j ) = A * exp ( ( i c x ) 2 2 σ x 2 + ( j c y ) 2 2 σ y 2 ) ,
SNR = 10 log 10 ( max { B lin 2 } / σ lin 2 ) .
CNR = 1 R ( r = 1 R ( μ r μ b ) σ r 2 + σ b 2 ) ,

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