Abstract

A novel image processing algorithm based on a modified Bayesian residual transform (MBRT) was developed for the enhancement of morphological and vascular features in optical coherence tomography (OCT) and OCT angiography (OCTA) images. The MBRT algorithm decomposes the original OCT image into multiple residual images, where each image presents information at a unique scale. Scale selective residual adaptation is used subsequently to enhance morphological features of interest, such as blood vessels and tissue layers, and to suppress irrelevant image features such as noise and motion artefacts. The performance of the proposed MBRT algorithm was tested on a series of cross-sectional and enface OCT and OCTA images of retina and brain tissue that were acquired in-vivo. Results show that the MBRT reduces speckle noise and motion-related imaging artefacts locally, thus improving significantly the contrast and visibility of morphological features in the OCT and OCTA images.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (1)

2017 (7)

J. M. B. de Barros Garcia, D. L. C. Isaac, and M. Avila, “Diabetic retinopathy and OCT angiography: clinical findings and future perspectives,” Int. J. Retina Vitreous 3(1), 14 (2017).
[Crossref] [PubMed]

Z. Liu, K. Kurokawa, F. Zhang, J. J. Lee, and D. T. Miller, “Imaging and quantifying ganglion cells and other transparent neurons in the living human retina,” Proc. Natl. Acad. Sci. U.S.A. 114(48), 12803–12808 (2017).
[Crossref] [PubMed]

J. Polans, D. Cunefare, E. Cole, B. Keller, P. S. Mettu, S. W. Cousins, M. J. Allingham, J. A. Izatt, and S. Farsiu, “Enhanced visualization of peripheral retinal vasculature with wavefront sensorless adaptive optics optical coherence tomography angiography in diabetic patients,” Opt. Lett. 42(1), 17–20 (2017).
[Crossref] [PubMed]

O. Liba, M. D. Lew, E. D. Sorelle, R. Dutta, D. Sen, D. M. Moshfeghi, S. Chu, and A. De La Zerda, “Speckle-modulating optical coherence tomography in living mice and humans,” Nat. Commun. 8(May), 1–12 (2017).
[PubMed]

A. Li, J. You, C. Du, and Y. Pan, “Automated segmentation and quantification of OCT angiography for tracking angiogenesis progression,” Biomed. Opt. Express 8(12), 5604–5616 (2017).
[Crossref] [PubMed]

B. Tan, E. Mason, B. MacLellan, and K. K. Bizheva, “Correlation of Visually Evoked Functional and Blood Flow Changes in the Rat Retina Measured With a Combined OCT+ERG System,” Invest. Ophthalmol. Vis. Sci. 58(3), 1673–1681 (2017).
[Crossref] [PubMed]

K. Bizheva, B. Tan, B. MacLelan, O. Kralj, M. Hajialamdari, D. Hileeto, and L. Sorbara, “Sub-micrometer axial resolution OCT for in-vivo imaging of the cellular structure of healthy and keratoconic human corneas,” Biomed. Opt. Express 8(2), 800–812 (2017).
[Crossref] [PubMed]

2016 (4)

Y. Jian, S. Lee, M. J. Ju, M. Heisler, W. Ding, R. J. Zawadzki, S. Bonora, and M. V. Sarunic, “Lens-based wavefront sensorless adaptive optics swept source OCT,” Sci. Rep. 6(May), 27620 (2016).
[Crossref] [PubMed]

N. K. Scripsema, P. M. Garcia, R. D. Bavier, T. Y. P. Chui, B. D. Krawitz, S. Mo, S. A. Agemy, L. Xu, Y. B. Lin, J. F. Panarelli, P. A. Sidoti, J. C. Tsai, and R. B. Rosen, “Optical Coherence Tomography Angiography Analysis of Perfused Peripapillary Capillaries in Primary Open-Angle Glaucoma and Normal-Tension Glaucoma,” Invest. Ophthalmol. Vis. Sci. 57(9), OCT611 (2016).
[Crossref] [PubMed]

C. W. Merkle, C. Leahy, and V. J. Srinivasan, “Dynamic contrast optical coherence tomography images transit time and quantifies microvascular plasma volume and flow in the retina and choriocapillaris,” Biomed. Opt. Express 7(10), 4289–4312 (2016).
[Crossref] [PubMed]

O. Liba, E. D. SoRelle, D. Sen, and A. de la Zerda, “Contrast-enhanced optical coherence tomography with picomolar sensitivity for functional in vivo imaging,” Sci. Rep. 6, 23337 (2016).
[Crossref] [PubMed]

2015 (3)

T. E. de Carlo, A. Romano, N. K. Waheed, and J. S. Duker, “A review of optical coherence tomography angiography (OCTA),” Int. J. Retina Vitreous 1(1), 5 (2015).
[Crossref] [PubMed]

Q. Zhang, Y. Huang, T. Zhang, S. Kubach, L. An, M. Laron, U. Sharma, and R. K. Wang, “Wide-field imaging of retinal vasculature using optical coherence tomography-based microangiography provided by motion tracking,” J. Biomed. Opt. 20(6), 066008 (2015).
[Crossref] [PubMed]

A. Wong and X. Y. U. Wang, “A Bayesian Residual Transform for Signal Processing,” IEEE Access 3(1109), 709–717 (2015).
[Crossref]

2014 (1)

Y. Pan, J. You, N. D. Volkow, K. Park, and C. Du, “Ultrasensitive detection of 3D cerebral microvascular network dynamics in vivo,” Neuroimage 103, 492–501 (2014).
[Crossref] [PubMed]

2013 (6)

2012 (7)

D. Xu, N. Vaswani, Y. Huang, and J. U. Kang, “Modified compressive sensing optical coherence tomography with noise reduction,” Opt. Lett. 37(20), 4209–4211 (2012).
[Crossref] [PubMed]

L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, and S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express 3(5), 927–942 (2012).
[Crossref] [PubMed]

M. A. Mayer, A. Borsdorf, M. Wagner, J. Hornegger, C. Y. Mardin, and R. P. Tornow, “Wavelet denoising of multiframe optical coherence tomography data,” Biomed. Opt. Express 3(3), 572–589 (2012).
[Crossref] [PubMed]

G. Liu, Z. Zhi, and R. K. Wang, “Digital focusing of OCT images based on scalar diffraction theory and information entropy,” Biomed. Opt. Express 3(11), 2774–2783 (2012).
[Crossref] [PubMed]

P. A. Keane, P. J. Patel, S. Liakopoulos, F. M. Heussen, S. R. Sadda, and A. Tufail, “Evaluation of Age-related Macular Degeneration With Optical Coherence Tomography,” Surv. Ophthalmol. 57(5), 389–414 (2012).
[Crossref] [PubMed]

B. J. Vakoc, D. Fukumura, R. K. Jain, and B. E. Bouma, “Cancer imaging by optical coherence tomography: preclinical progress and clinical potential,” Nat. Rev. Cancer 12(5), 363–368 (2012).
[Crossref] [PubMed]

Y. Jia, O. Tan, J. Tokayer, B. Potsaid, Y. Wang, J. J. Liu, M. F. Kraus, H. Subhash, J. G. Fujimoto, J. Hornegger, and D. Huang, “Split-spectrum amplitude-decorrelation angiography with optical coherence tomography,” Opt. Express 20(4), 4710–4725 (2012).
[Crossref] [PubMed]

2010 (1)

2009 (2)

2006 (1)

2005 (1)

T. S. Ralston, D. L. Marks, F. Kamalabadi, and S. A. Boppart, “Deconvolution methods for mitigation of transverse blurring in optical coherence tomography,” IEEE Trans. Image Process. 14(9), 1254–1264 (2005).
[Crossref] [PubMed]

2004 (1)

2002 (1)

Y. Yu, S. T. Acton, and S. Member, “Speckle Reducing Anisotropic Diffusion,” IEEE Trans. Image Process. 11(11), 1260–1270 (2002).
[Crossref] [PubMed]

1999 (1)

Y. Yasuno, J. Sugisaka, Y. Sando, Y. Nakamura, S. Makita, M. Itoh, T. Yatagai, D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, J. G. Fujimoto, B. Povazay, K. Bizheva, A. Unterhuber, B. Hermann, H. Sattmann, A. F. Fercher, W. Drexler, A. Apolonski, W. J. Wadsworth, J. C. Knight, P. J. St Russell, M. Vetterlein, and E. Scherzer, “Non-iterative numerical method for laterally superresolving Fourier domain optical coherence tomography,” Opt. Express 24(3), 1221–1223 (1999).
[PubMed]

1998 (1)

W. J. N. K. L. V. M. A. V. Ro and F. Frangi, “Multiscale vessel enhancement filtering,” Med. Image Comput. Comput. Assist. Interv. 1496, 130–137 (1998).

1993 (1)

A. Lopes, E. Nezry, R. Touzi, and H. Laur, “Structure detection and statistical adaptive speckle filtering in SAR images,” Int. J. Remote Sens. 14(9), 1735–1758 (1993).
[Crossref]

1989 (2)

S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989).
[Crossref]

J. J. Hopfield and J. L. Mcclelland, “An Adaptive Weighted Median Filter for Speckle Suppression in Medical Ultrasonic Images,” IEEE Trans. Circ. Syst. 36(1), 129–135 (1989).
[Crossref]

1982 (1)

V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. C. Holtzman, “A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise,” IEEE Trans. Pattern Anal. Mach. Intell. 4(2), 157–166 (1982).
[Crossref] [PubMed]

1979 (1)

N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979).
[Crossref]

1964 (2)

E. A. Nadaraya, “On Estimating Regression,” Theory Probab. Appl. 9(1), 141–142 (1964).
[Crossref]

G. S. Watson, “Smooth regression analysis,” Indian J. Stat. 26(4), 359–372 (1964).

Acton, S. T.

Y. Yu, S. T. Acton, and S. Member, “Speckle Reducing Anisotropic Diffusion,” IEEE Trans. Image Process. 11(11), 1260–1270 (2002).
[Crossref] [PubMed]

Adler, D. C.

Agemy, S. A.

N. K. Scripsema, P. M. Garcia, R. D. Bavier, T. Y. P. Chui, B. D. Krawitz, S. Mo, S. A. Agemy, L. Xu, Y. B. Lin, J. F. Panarelli, P. A. Sidoti, J. C. Tsai, and R. B. Rosen, “Optical Coherence Tomography Angiography Analysis of Perfused Peripapillary Capillaries in Primary Open-Angle Glaucoma and Normal-Tension Glaucoma,” Invest. Ophthalmol. Vis. Sci. 57(9), OCT611 (2016).
[Crossref] [PubMed]

Allingham, M. J.

An, L.

Q. Zhang, Y. Huang, T. Zhang, S. Kubach, L. An, M. Laron, U. Sharma, and R. K. Wang, “Wide-field imaging of retinal vasculature using optical coherence tomography-based microangiography provided by motion tracking,” J. Biomed. Opt. 20(6), 066008 (2015).
[Crossref] [PubMed]

Apolonski, A.

Y. Yasuno, J. Sugisaka, Y. Sando, Y. Nakamura, S. Makita, M. Itoh, T. Yatagai, D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, J. G. Fujimoto, B. Povazay, K. Bizheva, A. Unterhuber, B. Hermann, H. Sattmann, A. F. Fercher, W. Drexler, A. Apolonski, W. J. Wadsworth, J. C. Knight, P. J. St Russell, M. Vetterlein, and E. Scherzer, “Non-iterative numerical method for laterally superresolving Fourier domain optical coherence tomography,” Opt. Express 24(3), 1221–1223 (1999).
[PubMed]

Avanaki, M. R. N.

Avila, M.

J. M. B. de Barros Garcia, D. L. C. Isaac, and M. Avila, “Diabetic retinopathy and OCT angiography: clinical findings and future perspectives,” Int. J. Retina Vitreous 3(1), 14 (2017).
[Crossref] [PubMed]

Bavier, R. D.

N. K. Scripsema, P. M. Garcia, R. D. Bavier, T. Y. P. Chui, B. D. Krawitz, S. Mo, S. A. Agemy, L. Xu, Y. B. Lin, J. F. Panarelli, P. A. Sidoti, J. C. Tsai, and R. B. Rosen, “Optical Coherence Tomography Angiography Analysis of Perfused Peripapillary Capillaries in Primary Open-Angle Glaucoma and Normal-Tension Glaucoma,” Invest. Ophthalmol. Vis. Sci. 57(9), OCT611 (2016).
[Crossref] [PubMed]

Bernucci, M. T.

Bizheva, K.

K. Bizheva, B. Tan, B. MacLelan, O. Kralj, M. Hajialamdari, D. Hileeto, and L. Sorbara, “Sub-micrometer axial resolution OCT for in-vivo imaging of the cellular structure of healthy and keratoconic human corneas,” Biomed. Opt. Express 8(2), 800–812 (2017).
[Crossref] [PubMed]

A. Boroomand, A. Wong, E. Li, D. S. Cho, B. Ni, and K. Bizheva, “Multi-penalty conditional random field approach to super-resolved reconstruction of optical coherence tomography images,” Biomed. Opt. Express 4(10), 2032–2050 (2013).
[Crossref] [PubMed]

A. Cameron, D. Lui, A. Boroomand, J. Glaister, A. Wong, and K. Bizheva, “Stochastic speckle noise compensation in optical coherence tomography using non-stationary spline-based speckle noise modelling,” Biomed. Opt. Express 4(9), 1769–1785 (2013).
[Crossref] [PubMed]

A. Wong, A. Mishra, K. Bizheva, and D. A. Clausi, “General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery,” Opt. Express 18(8), 8338–8352 (2010).
[Crossref] [PubMed]

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Sorelle, E. D.

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St Russell, P. J.

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Y. Yasuno, J. Sugisaka, Y. Sando, Y. Nakamura, S. Makita, M. Itoh, T. Yatagai, D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, J. G. Fujimoto, B. Povazay, K. Bizheva, A. Unterhuber, B. Hermann, H. Sattmann, A. F. Fercher, W. Drexler, A. Apolonski, W. J. Wadsworth, J. C. Knight, P. J. St Russell, M. Vetterlein, and E. Scherzer, “Non-iterative numerical method for laterally superresolving Fourier domain optical coherence tomography,” Opt. Express 24(3), 1221–1223 (1999).
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Sugisaka, J.

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B. Tan, E. Mason, B. MacLellan, and K. K. Bizheva, “Correlation of Visually Evoked Functional and Blood Flow Changes in the Rat Retina Measured With a Combined OCT+ERG System,” Invest. Ophthalmol. Vis. Sci. 58(3), 1673–1681 (2017).
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Tsai, J. C.

N. K. Scripsema, P. M. Garcia, R. D. Bavier, T. Y. P. Chui, B. D. Krawitz, S. Mo, S. A. Agemy, L. Xu, Y. B. Lin, J. F. Panarelli, P. A. Sidoti, J. C. Tsai, and R. B. Rosen, “Optical Coherence Tomography Angiography Analysis of Perfused Peripapillary Capillaries in Primary Open-Angle Glaucoma and Normal-Tension Glaucoma,” Invest. Ophthalmol. Vis. Sci. 57(9), OCT611 (2016).
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Tufail, A.

P. A. Keane, P. J. Patel, S. Liakopoulos, F. M. Heussen, S. R. Sadda, and A. Tufail, “Evaluation of Age-related Macular Degeneration With Optical Coherence Tomography,” Surv. Ophthalmol. 57(5), 389–414 (2012).
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Y. Yasuno, J. Sugisaka, Y. Sando, Y. Nakamura, S. Makita, M. Itoh, T. Yatagai, D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, J. G. Fujimoto, B. Povazay, K. Bizheva, A. Unterhuber, B. Hermann, H. Sattmann, A. F. Fercher, W. Drexler, A. Apolonski, W. J. Wadsworth, J. C. Knight, P. J. St Russell, M. Vetterlein, and E. Scherzer, “Non-iterative numerical method for laterally superresolving Fourier domain optical coherence tomography,” Opt. Express 24(3), 1221–1223 (1999).
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Y. Pan, J. You, N. D. Volkow, K. Park, and C. Du, “Ultrasensitive detection of 3D cerebral microvascular network dynamics in vivo,” Neuroimage 103, 492–501 (2014).
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Y. Yasuno, J. Sugisaka, Y. Sando, Y. Nakamura, S. Makita, M. Itoh, T. Yatagai, D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, J. G. Fujimoto, B. Povazay, K. Bizheva, A. Unterhuber, B. Hermann, H. Sattmann, A. F. Fercher, W. Drexler, A. Apolonski, W. J. Wadsworth, J. C. Knight, P. J. St Russell, M. Vetterlein, and E. Scherzer, “Non-iterative numerical method for laterally superresolving Fourier domain optical coherence tomography,” Opt. Express 24(3), 1221–1223 (1999).
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Waheed, N. K.

T. E. de Carlo, A. Romano, N. K. Waheed, and J. S. Duker, “A review of optical coherence tomography angiography (OCTA),” Int. J. Retina Vitreous 1(1), 5 (2015).
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Wang, R. K.

Q. Zhang, Y. Huang, T. Zhang, S. Kubach, L. An, M. Laron, U. Sharma, and R. K. Wang, “Wide-field imaging of retinal vasculature using optical coherence tomography-based microangiography provided by motion tracking,” J. Biomed. Opt. 20(6), 066008 (2015).
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Figures (7)

Fig. 1
Fig. 1 Illustration of structure enhancement using modified Bayesian Residual Transform
Fig. 2
Fig. 2 Example of BRT based multi-scale image decomposition and reconstruction on a Newton’s ring phantom image. (A) Original phantom Image. (B) Noise contaminated phantom image with zero-mean Gaussian noise. (C-G) BRT-based image decompositions into different scales, from the finest scale to the coarsest scale. (H) Structure enhanced image with the visibility of graded rings largely improved.
Fig. 3
Fig. 3 Example of BRT based multi-scale image decomposition and reconstruction on rat’s OCTA image from retinal GCL + NFL layer. (A) Original Image. (B-F) BRT-based image decompositions into different scales, from the finest scale to the coarsest scale. Structural characteristics at different scales are well extracted. (G) Structure enhanced image using scale selective residual adaption. (H) Computational (Comp.) time for image decomposition and reconstruction as a function of decomposed residual image numbers. Range from 1.2 s to 2.6 s with 4-11 decomposed residual images.
Fig. 4
Fig. 4 Application to OCTA image on rat’s retinal OPL layer. (A-D) Original image and BRT-based image reconstruction. The view of interested is labelled in yellow rectangles and magnified to (E-H).
Fig. 5
Fig. 5 Application of the MBRT to OCTA image of rat tumorous brain. (A-D) Original image, Frangi filtered, Gabor filtered and proposed structure enhanced images. The view of interested is labelled in yellow and red rectangles and magnified to (E-H) and (I-L) respectively. Blue curved line labels the boundary of a cyst region.
Fig. 6
Fig. 6 Enhancement of the vasculature in OCT images of the human retina using MBRT, acquired with a commercial SS-OCT system. (A,C) Extracted original images of healthy human macula with sizes 3mm × 3mm and 6mm × 6mm. (B,D) MBRT enhanced images. Note that the original OCT images were already pre-processed with built-in filters into the commercial SS-OCT system.
Fig. 7
Fig. 7 Application to cross-sectional OCT images of rat’s retina and human cornea. (A-B) Original image and the reconstructed image using MBRT approach that represent the layered retinal structure near the ONH, shown as the yellow line in (C). The magnified view of the blood vessel region shows the unambiguous recognition of a blood vessel after image reconstruction using MBRT approach. (D-E) original and structure enhanced image using modified MBRT. Layered structure is improved so that the layer segmentation can be easily implemented afterward in (F). It’s note that the basal cell layer is much more prominent after the structure enhancement and can be confidently isolated from the rest of the epithelium layer.

Tables (1)

Tables Icon

Table 1 DSC and vascular connectivity of three methods and manual segmentation (N = 9)

Equations (9)

Equations on this page are rendered with MathJax. Learn more.

I( A ¯ )=  I 1 ( A ¯ )=  i=1 N µ i ( A ¯ ) µ 1 ( A ¯ ) µ 2 ( A ¯ ) µ N ( A ¯ )
µ 1 ( A ¯ )= i=1 N µ i ( A ¯ ) i=2 N µ i ( A ¯ )= I 1 ( A ¯ ) I 2 ( A ¯ )
I ' 1 ( A ¯ )=E( I 2 ( A ¯ )| I 1 ( A ¯ ))
I ' k+1 ( A ¯ )=E( I k+1 ( A ¯ )|I ' k ( A ¯ ))
G k ( x )= 1 2π α k exp( x 2 2 α k 2 )
I ' k ( A ¯ )=(G*(I ' k ( A ¯ )I ' k ( A ¯ i ))=  1 2π α k exp( ( I ' k ( A ¯ ) I ' k ( A ¯ i )) 2 2 α k 2 )
µ i '( A ¯ )=   η 1 µ i ( A ¯ )
S( A ¯ )=  i=1 N µ i ( A ¯ )
Dice  Similarity  Coefficient  (DSC)= 2TP 2TP+FP+FN

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