Generation and optimization of superpixels as image processing kernels for Jones matrix optical coherence tomography

Arata Miyazawa; Young-Joo Hong; Shuichi Makita; Deepa Kasaragod; Yoshiaki Yasuno

doi:10.1364/BOE.8.004396

Biomedical Optics Express
Vol. 8,
Issue 10,
pp. 4396-4418
(2017)
•https://doi.org/10.1364/BOE.8.004396

Generation and optimization of superpixels as image processing kernels for Jones matrix optical coherence tomography

Arata Miyazawa, Young-Joo Hong, Shuichi Makita, Deepa Kasaragod, and Yoshiaki Yasuno

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Arata Miyazawa, Young-Joo Hong, Shuichi Makita, Deepa Kasaragod, and Yoshiaki Yasuno, "Generation and optimization of superpixels as image processing kernels for Jones matrix optical coherence tomography," Biomed. Opt. Express 8, 4396-4418 (2017)

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Related Topics
Table of Contents Category
- Optical Coherence Tomography
Optics & Photonics Topics
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The topics in this list come from the Optics and Photonics Topics applied to this article.
Previously assigned OCIS codes
- Image analysis (100.2960)
- Optical coherence tomography (110.4500)
- Ophthalmology (170.4470)
- Optical coherence tomography (170.4500)

About this Article
History
- Original Manuscript: August 9, 2017
- Revised Manuscript: August 31, 2017
- Manuscript Accepted: September 1, 2017
- Published: September 8, 2017

Abstract

Jones matrix-based polarization sensitive optical coherence tomography (JM-OCT) simultaneously measures optical intensity, birefringence, degree of polarization uniformity, and OCT angiography. The statistics of the optical features in a local region, such as the local mean of the OCT intensity, are frequently used for image processing and the quantitative analysis of JM-OCT. Conventionally, local statistics have been computed with fixed-size rectangular kernels. However, this results in a trade-off between image sharpness and statistical accuracy. We introduce a superpixel method to JM-OCT for generating the flexible kernels of local statistics. A superpixel is a cluster of image pixels that is formed by the pixels’ spatial and signal value proximities. An algorithm for superpixel generation specialized for JM-OCT and its optimization methods are presented in this paper. The spatial proximity is in two-dimensional cross-sectional space and the signal values are the four optical features. Hence, the superpixel method is a six-dimensional clustering technique for JM-OCT pixels. The performance of the JM-OCT superpixels and its optimization methods are evaluated in detail using JM-OCT datasets of posterior eyes. The superpixels were found to well preserve tissue structures, such as layer structures, sclera, vessels, and retinal pigment epithelium. And hence, they are more suitable for local statistics kernels than conventional uniform rectangular kernels.

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Year
Author
Publication

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, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]
E. A. Swanson and J. G. Fujimoto, “The ecosystem that powered the translation of OCT from fundamental research to clinical and commercial impact [Invited],” Biomed. Opt. Express 8, 1638–1664 (2017).
[Crossref] [PubMed]
C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 102, 217–229 (1995).
[Crossref] [PubMed]
M. Pircher, C. K. Hitzenberger, and U. Schmidt-Erfurth, “Polarization sensitive optical coherence tomography in the human eye,” Prog. Retin. Eye Res. 30, 431–451 (2011).
[Crossref] [PubMed]
J. F. d. Boer, C. K. Hitzenberger, and Y. Yasuno, “Polarization sensitive optical coherence tomography – a review [Invited],” Biomed. Opt. Express 8, 1838–1873 (2017).
[Crossref] [PubMed]
S. Guo, J. Zhang, L. Wang, J. S. Nelson, and Z. Chen, “Depth-resolved birefringence and differential optical axis orientation measurements with fiber-based polarization-sensitive optical coherence tomography,” Opt. Lett. 29, 2025–2027 (2004).
[Crossref] [PubMed]
S. Makita, M. Yamanari, and Y. Yasuno, “Generalized Jones matrix optical coherence tomography: performance and local birefringence imaging,” Opt. Express 18, 854–876 (2010).
[Crossref] [PubMed]
D. Kasaragod, S. Makita, S. Fukuda, S. Beheregaray, T. Oshika, and Y. Yasuno, “Bayesian maximum likelihood estimator of phase retardation for quantitative polarization-sensitive optical coherence tomography,” Opt. Express 22, 16472–16492 (2014).
[Crossref] [PubMed]
M. Yamanari, S. Tsuda, T. Kokubun, Y. Shiga, K. Omodaka, Y. Yokoyama, N. Himori, M. Ryu, S. Kunimatsu-Sanuki, H. Takahashi, K. Maruyama, H. Kunikata, and T. Nakazawa, “Fiber-based polarization-sensitive OCT for birefringence imaging of the anterior eye segment,” Biomed. Opt. Express 6, 369–389 (2015).
[Crossref] [PubMed]
S. Sugiyama, Y.-J. Hong, D. Kasaragod, S. Makita, S. Uematsu, Y. Ikuno, M. Miura, and Y. Yasuno, “Birefringence imaging of posterior eye by multi-functional Jones matrix optical coherence tomography,” Biomed. Opt. Express 6, 4951–4974 (2015).
[Crossref] [PubMed]
W. C. Y. Lo, M. Villiger, A. Golberg, G. F. Broelsch, S. Khan, C. G. Lian, W. G. Austen, M. Yarmush, and B. E. Bouma, “Longitudinal, 3d Imaging of Collagen Remodeling in Murine Hypertrophic Scars In Vivo using Polarization-sensitive Optical Frequency Domain Imaging,” J. Invest. Dermatol. 136, 84–92 (2016).
[Crossref] [PubMed]
E. Li, S. Makita, Y.-J. Hong, D. Kasaragod, and Y. Yasuno, “Three-dimensional multi-contrast imaging of in vivo human skin by Jones matrix optical coherence tomography,” Biomed. Opt. Express 8, 1290–1305 (2017).
[Crossref] [PubMed]
D. Kasaragod, S. Makita, Y.-J. Hong, and Y. Yasuno, “Noise stochastic corrected maximum a posteriori estimator for birefringence imaging using polarization-sensitive optical coherence tomography,” Biomed. Opt. Express 8, 653–669 (2017).
[Crossref] [PubMed]
E. Götzinger, M. Pircher, W. Geitzenauer, C. Ahlers, B. Baumann, S. Michels, U. Schmidt-Erfurth, and C. K. Hitzenberger, “Retinal pigment epithelium segmentation by polarization sensitive optical coherence tomography,” Opt. Express 16, 16410–16422 (2008).
[Crossref] [PubMed]
C. Ahlers, E. Götzinger, M. Pircher, I. Golbaz, F. Prager, C. Schütze, B. Baumann, C. K. Hitzenberger, and U. Schmidt-Erfurth, “Imaging of the retinal pigment epithelium in age-related macular degeneration using polarization-sensitive optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 51, 2149–2157 (2010).
[Crossref]
S. Makita, Y.-J. Hong, M. Miura, and Y. Yasuno, “Degree of polarization uniformity with high noise immunity using polarization-sensitive optical coherence tomography,” Opt. Lett. 39, 6783–6786 (2014).
[Crossref] [PubMed]
B. Baumann, S. Zotter, M. Pircher, E. Götzinger, S. Rauscher, M. Glösmann, J. Lammer, U. Schmidt-Erfurth, M. Gröger, and C. K. Hitzenberger, “Spectral degree of polarization uniformity for polarization-sensitive OCT,” J. Mod. Opt. 62, 1758–1763 (2015).
[Crossref] [PubMed]
B. Baumann, S. O. Baumann, T. Konegger, M. Pircher, E. Götzinger, F. Schlanitz, C. Schütze, H. Sattmann, M. Litschauer, U. Schmidt-Erfurth, and C. K. Hitzenberger, “Polarization sensitive optical coherence tomography of melanin provides intrinsic contrast based on depolarization,” Biomed. Opt. Express 3, 1670–1683 (2012).
[Crossref] [PubMed]
S. Makita, Y. Hong, M. Yamanari, T. Yatagai, and Y. Yasuno, “Optical coherence angiography,” Opt. Express 14, 7821–7840 (2006).
[Crossref] [PubMed]
L. An and R. K. Wang, “In vivo volumetric imaging of vascular perfusion within human retina and choroids with optical micro-angiography,” Opt. Express 16, 11438–11452 (2008).
[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, 4710–4725 (2012).
[Crossref] [PubMed]
S. Makita, K. Kurokawa, Y.-J. Hong, M. Miura, and Y. Yasuno, “Noise-immune complex correlation for optical coherence angiography based on standard and Jones matrix optical coherence tomography,” Biomed. Opt. Express 7, 1525–1548 (2016).
[Crossref] [PubMed]
Y. Lim, Y.-J. Hong, L. Duan, M. Yamanari, and Y. Yasuno, “Passive component based multifunctional Jones matrix swept source optical coherence tomography for Doppler and polarization imaging,” Opt. Lett. 37, 1958–1960 (2012).
[Crossref] [PubMed]
M. J. Ju, Y.-J. Hong, S. Makita, Y. Lim, K. Kurokawa, L. Duan, M. Miura, S. Tang, and Y. Yasuno, “Advanced multi-contrast Jones matrix optical coherence tomography for Doppler and polarization sensitive imaging,” Opt. Express 21, 19412–19436 (2013).
[Crossref] [PubMed]
S. Kwon, Y. Yoon, B. Kim, W. H. Jang, B. Oh, K. Y. Chung, and K. H. Kim, “Dermoscopy guided dark-field multi-functional optical coherence tomography,” Biomed. Opt. Express 8, 1372–1381 (2017).
[Crossref] [PubMed]
X. Ren and J. Malik, “Learning a classification model for segmentation,” Proc. Ninth IEEE Int. Conf. Comp. Vis. pp. 10–17 vol.1 (2003).
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC Superpixels,” EPFL Technical Report 149300 (2010).
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods,” IEEE Trans. Pattern Anal. Mach. Intell. 34, 2274–2282 (2012).
[Crossref] [PubMed]
American National Standard Institute, American National Standard for the Safe Use of Lasers ANSI Z 136.1-2014 (American National Standards Institute, New York, 2014).
J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in Optical Coherence Tomography,” Journal of Biomedical Optics 4, 95–105 (1999).
[Crossref] [PubMed]
J. J. Weiter, F. C. Delori, G. L. Wing, and K. A. Fitch, “Retinal pigment epithelial lipofuscin and melanin and choroidal melanin in human eyes,” Investigative Ophthalmology & Visual Science 27, 145–152 (1986).
U. Bhaskar, Y.-J. Hong, M. Miura, and Y. Yasuno, “Five-dimensional analysis of multi-contrast Jones matrix tomography of posterior eye,” Proceedings of SPIE 8930, 893008 (2014).
[Crossref]
A. Miyazawa, Y.-J. Hong, S. Makita, D. Kasaragod, and Y. Yasuno, “Accurate measurement of retinal birefringence by jones matrix oct with maximum a posteriori estimator and superpixel kernel,” ARVO Imaging in the Eye Conference (2017).
E. Götzinger, M. Pircher, B. Baumann, T. Schmoll, H. Sattmann, R. A. Leitgeb, and C. K. Hitzenberger, “Speckle noise reduction in high speed polarization sensitive spectral domain optical coherence tomography,” Opt. Express 19, 14568–14584 (2011).
[Crossref] [PubMed]
Y. Lim, M. Yamanari, S. Fukuda, Y. Kaji, T. Kiuchi, M. Miura, T. Oshika, and Y. Yasuno, “Birefringence measurement of cornea and anterior segment by office-based polarization-sensitive optical coherence tomography,” Biomedical Optics Express 2, 2392–2402 (2011).
[Crossref] [PubMed]
M. Yamanari, S. Tsuda, T. Kokubun, Y. Shiga, K. Omodaka, N. Aizawa, Y. Yokoyama, N. Himori, S. Kunimatsu-Sanuki, K. Maruyama, H. Kunikata, and T. Nakazawa, “Estimation of Jones matrix, birefringence and entropy using Cloude-Pottier decomposition in polarization-sensitive optical coherence tomography,” Biomedical Optics Express 7, 3551 (2016).
[Crossref] [PubMed]

2017 (5)

E. A. Swanson and J. G. Fujimoto, “The ecosystem that powered the translation of OCT from fundamental research to clinical and commercial impact [Invited],” Biomed. Opt. Express 8, 1638–1664 (2017).
[Crossref] [PubMed]

J. F. d. Boer, C. K. Hitzenberger, and Y. Yasuno, “Polarization sensitive optical coherence tomography – a review [Invited],” Biomed. Opt. Express 8, 1838–1873 (2017).
[Crossref] [PubMed]

E. Li, S. Makita, Y.-J. Hong, D. Kasaragod, and Y. Yasuno, “Three-dimensional multi-contrast imaging of in vivo human skin by Jones matrix optical coherence tomography,” Biomed. Opt. Express 8, 1290–1305 (2017).
[Crossref] [PubMed]

D. Kasaragod, S. Makita, Y.-J. Hong, and Y. Yasuno, “Noise stochastic corrected maximum a posteriori estimator for birefringence imaging using polarization-sensitive optical coherence tomography,” Biomed. Opt. Express 8, 653–669 (2017).
[Crossref] [PubMed]

S. Kwon, Y. Yoon, B. Kim, W. H. Jang, B. Oh, K. Y. Chung, and K. H. Kim, “Dermoscopy guided dark-field multi-functional optical coherence tomography,” Biomed. Opt. Express 8, 1372–1381 (2017).
[Crossref] [PubMed]

2016 (3)

S. Makita, K. Kurokawa, Y.-J. Hong, M. Miura, and Y. Yasuno, “Noise-immune complex correlation for optical coherence angiography based on standard and Jones matrix optical coherence tomography,” Biomed. Opt. Express 7, 1525–1548 (2016).
[Crossref] [PubMed]

M. Yamanari, S. Tsuda, T. Kokubun, Y. Shiga, K. Omodaka, N. Aizawa, Y. Yokoyama, N. Himori, S. Kunimatsu-Sanuki, K. Maruyama, H. Kunikata, and T. Nakazawa, “Estimation of Jones matrix, birefringence and entropy using Cloude-Pottier decomposition in polarization-sensitive optical coherence tomography,” Biomedical Optics Express 7, 3551 (2016).
[Crossref] [PubMed]

W. C. Y. Lo, M. Villiger, A. Golberg, G. F. Broelsch, S. Khan, C. G. Lian, W. G. Austen, M. Yarmush, and B. E. Bouma, “Longitudinal, 3d Imaging of Collagen Remodeling in Murine Hypertrophic Scars In Vivo using Polarization-sensitive Optical Frequency Domain Imaging,” J. Invest. Dermatol. 136, 84–92 (2016).
[Crossref] [PubMed]

2015 (3)

B. Baumann, S. Zotter, M. Pircher, E. Götzinger, S. Rauscher, M. Glösmann, J. Lammer, U. Schmidt-Erfurth, M. Gröger, and C. K. Hitzenberger, “Spectral degree of polarization uniformity for polarization-sensitive OCT,” J. Mod. Opt. 62, 1758–1763 (2015).
[Crossref] [PubMed]

M. Yamanari, S. Tsuda, T. Kokubun, Y. Shiga, K. Omodaka, Y. Yokoyama, N. Himori, M. Ryu, S. Kunimatsu-Sanuki, H. Takahashi, K. Maruyama, H. Kunikata, and T. Nakazawa, “Fiber-based polarization-sensitive OCT for birefringence imaging of the anterior eye segment,” Biomed. Opt. Express 6, 369–389 (2015).
[Crossref] [PubMed]

S. Sugiyama, Y.-J. Hong, D. Kasaragod, S. Makita, S. Uematsu, Y. Ikuno, M. Miura, and Y. Yasuno, “Birefringence imaging of posterior eye by multi-functional Jones matrix optical coherence tomography,” Biomed. Opt. Express 6, 4951–4974 (2015).
[Crossref] [PubMed]

2014 (3)

D. Kasaragod, S. Makita, S. Fukuda, S. Beheregaray, T. Oshika, and Y. Yasuno, “Bayesian maximum likelihood estimator of phase retardation for quantitative polarization-sensitive optical coherence tomography,” Opt. Express 22, 16472–16492 (2014).
[Crossref] [PubMed]

U. Bhaskar, Y.-J. Hong, M. Miura, and Y. Yasuno, “Five-dimensional analysis of multi-contrast Jones matrix tomography of posterior eye,” Proceedings of SPIE 8930, 893008 (2014).
[Crossref]

S. Makita, Y.-J. Hong, M. Miura, and Y. Yasuno, “Degree of polarization uniformity with high noise immunity using polarization-sensitive optical coherence tomography,” Opt. Lett. 39, 6783–6786 (2014).
[Crossref] [PubMed]

2013 (1)

M. J. Ju, Y.-J. Hong, S. Makita, Y. Lim, K. Kurokawa, L. Duan, M. Miura, S. Tang, and Y. Yasuno, “Advanced multi-contrast Jones matrix optical coherence tomography for Doppler and polarization sensitive imaging,” Opt. Express 21, 19412–19436 (2013).
[Crossref] [PubMed]

2012 (4)

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, 4710–4725 (2012).
[Crossref] [PubMed]

Y. Lim, Y.-J. Hong, L. Duan, M. Yamanari, and Y. Yasuno, “Passive component based multifunctional Jones matrix swept source optical coherence tomography for Doppler and polarization imaging,” Opt. Lett. 37, 1958–1960 (2012).
[Crossref] [PubMed]

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods,” IEEE Trans. Pattern Anal. Mach. Intell. 34, 2274–2282 (2012).
[Crossref] [PubMed]

B. Baumann, S. O. Baumann, T. Konegger, M. Pircher, E. Götzinger, F. Schlanitz, C. Schütze, H. Sattmann, M. Litschauer, U. Schmidt-Erfurth, and C. K. Hitzenberger, “Polarization sensitive optical coherence tomography of melanin provides intrinsic contrast based on depolarization,” Biomed. Opt. Express 3, 1670–1683 (2012).
[Crossref] [PubMed]

2011 (3)

M. Pircher, C. K. Hitzenberger, and U. Schmidt-Erfurth, “Polarization sensitive optical coherence tomography in the human eye,” Prog. Retin. Eye Res. 30, 431–451 (2011).
[Crossref] [PubMed]

E. Götzinger, M. Pircher, B. Baumann, T. Schmoll, H. Sattmann, R. A. Leitgeb, and C. K. Hitzenberger, “Speckle noise reduction in high speed polarization sensitive spectral domain optical coherence tomography,” Opt. Express 19, 14568–14584 (2011).
[Crossref] [PubMed]

Y. Lim, M. Yamanari, S. Fukuda, Y. Kaji, T. Kiuchi, M. Miura, T. Oshika, and Y. Yasuno, “Birefringence measurement of cornea and anterior segment by office-based polarization-sensitive optical coherence tomography,” Biomedical Optics Express 2, 2392–2402 (2011).
[Crossref] [PubMed]

2010 (2)

S. Makita, M. Yamanari, and Y. Yasuno, “Generalized Jones matrix optical coherence tomography: performance and local birefringence imaging,” Opt. Express 18, 854–876 (2010).
[Crossref] [PubMed]

C. Ahlers, E. Götzinger, M. Pircher, I. Golbaz, F. Prager, C. Schütze, B. Baumann, C. K. Hitzenberger, and U. Schmidt-Erfurth, “Imaging of the retinal pigment epithelium in age-related macular degeneration using polarization-sensitive optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 51, 2149–2157 (2010).
[Crossref]

1999 (1)

J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in Optical Coherence Tomography,” Journal of Biomedical Optics 4, 95–105 (1999).
[Crossref] [PubMed]

1995 (1)

C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 102, 217–229 (1995).
[Crossref] [PubMed]

1991 (1)

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, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

1986 (1)

J. J. Weiter, F. C. Delori, G. L. Wing, and K. A. Fitch, “Retinal pigment epithelial lipofuscin and melanin and choroidal melanin in human eyes,” Investigative Ophthalmology & Visual Science 27, 145–152 (1986).

Achanta, R.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC Superpixels,” EPFL Technical Report 149300 (2010).

Ahlers, C.

Aizawa, N.

An, L.

Austen, W. G.

Baumann, B.

Baumann, S. O.

Beheregaray, S.

Bhaskar, U.

U. Bhaskar, Y.-J. Hong, M. Miura, and Y. Yasuno, “Five-dimensional analysis of multi-contrast Jones matrix tomography of posterior eye,” Proceedings of SPIE 8930, 893008 (2014).
[Crossref]

Boer, J. F. d.

J. F. d. Boer, C. K. Hitzenberger, and Y. Yasuno, “Polarization sensitive optical coherence tomography – a review [Invited],” Biomed. Opt. Express 8, 1838–1873 (2017).
[Crossref] [PubMed]

Bouma, B. E.

Broelsch, G. F.

Chang, W.

Chen, Z.

Chung, K. Y.

Delori, F. C.

Duan, L.

Duker, J. S.

Fitch, K. A.

Flotte, T.

Fua, P.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC Superpixels,” EPFL Technical Report 149300 (2010).

Fujimoto, J. G.

Fukuda, S.

Geitzenauer, W.

Glösmann, M.

Golbaz, I.

Golberg, A.

Götzinger, E.

Gregory, K.

Gröger, M.

Guo, S.

Hee, M. R.

Himori, N.

Hitzenberger, C. K.

J. F. d. Boer, C. K. Hitzenberger, and Y. Yasuno, “Polarization sensitive optical coherence tomography – a review [Invited],” Biomed. Opt. Express 8, 1838–1873 (2017).
[Crossref] [PubMed]

M. Pircher, C. K. Hitzenberger, and U. Schmidt-Erfurth, “Polarization sensitive optical coherence tomography in the human eye,” Prog. Retin. Eye Res. 30, 431–451 (2011).
[Crossref] [PubMed]

Hong, Y.

S. Makita, Y. Hong, M. Yamanari, T. Yatagai, and Y. Yasuno, “Optical coherence angiography,” Opt. Express 14, 7821–7840 (2006).
[Crossref] [PubMed]

Hong, Y.-J.

U. Bhaskar, Y.-J. Hong, M. Miura, and Y. Yasuno, “Five-dimensional analysis of multi-contrast Jones matrix tomography of posterior eye,” Proceedings of SPIE 8930, 893008 (2014).
[Crossref]

A. Miyazawa, Y.-J. Hong, S. Makita, D. Kasaragod, and Y. Yasuno, “Accurate measurement of retinal birefringence by jones matrix oct with maximum a posteriori estimator and superpixel kernel,” ARVO Imaging in the Eye Conference (2017).

Hornegger, J.

Huang, D.

Ikuno, Y.

Izatt, J. A.

Jang, W. H.

Jia, Y.

Ju, M. J.

Kaji, Y.

Kasaragod, D.

Khan, S.

Kim, B.

Kim, K. H.

Kiuchi, T.

Kokubun, T.

Konegger, T.

Kraus, M. F.

Kunikata, H.

Kunimatsu-Sanuki, S.

Kurokawa, K.

Kwon, S.

Lammer, J.

Leitgeb, R. A.

Li, E.

Lian, C. G.

Lim, Y.

Lin, C. P.

Litschauer, M.

Liu, J. J.

Lo, W. C. Y.

Lucchi, A.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC Superpixels,” EPFL Technical Report 149300 (2010).

Makita, S.

S. Makita, M. Yamanari, and Y. Yasuno, “Generalized Jones matrix optical coherence tomography: performance and local birefringence imaging,” Opt. Express 18, 854–876 (2010).
[Crossref] [PubMed]

S. Makita, Y. Hong, M. Yamanari, T. Yatagai, and Y. Yasuno, “Optical coherence angiography,” Opt. Express 14, 7821–7840 (2006).
[Crossref] [PubMed]

Malik, J.

X. Ren and J. Malik, “Learning a classification model for segmentation,” Proc. Ninth IEEE Int. Conf. Comp. Vis. pp. 10–17 vol.1 (2003).

Maruyama, K.

Michels, S.

Miura, M.

U. Bhaskar, Y.-J. Hong, M. Miura, and Y. Yasuno, “Five-dimensional analysis of multi-contrast Jones matrix tomography of posterior eye,” Proceedings of SPIE 8930, 893008 (2014).
[Crossref]

Miyazawa, A.

Nakazawa, T.

Nelson, J. S.

Oh, B.

Omodaka, K.

Oshika, T.

Pircher, M.

M. Pircher, C. K. Hitzenberger, and U. Schmidt-Erfurth, “Polarization sensitive optical coherence tomography in the human eye,” Prog. Retin. Eye Res. 30, 431–451 (2011).
[Crossref] [PubMed]

Potsaid, B.

Prager, F.

Puliafito, C. A.

Rauscher, S.

Reichel, E.

Ren, X.

X. Ren and J. Malik, “Learning a classification model for segmentation,” Proc. Ninth IEEE Int. Conf. Comp. Vis. pp. 10–17 vol.1 (2003).

Ryu, M.

Sattmann, H.

Schlanitz, F.

Schmidt-Erfurth, U.

M. Pircher, C. K. Hitzenberger, and U. Schmidt-Erfurth, “Polarization sensitive optical coherence tomography in the human eye,” Prog. Retin. Eye Res. 30, 431–451 (2011).
[Crossref] [PubMed]

Schmitt, J. M.

J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in Optical Coherence Tomography,” Journal of Biomedical Optics 4, 95–105 (1999).
[Crossref] [PubMed]

Schmoll, T.

Schuman, J. S.

Schütze, C.

Shaji, A.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC Superpixels,” EPFL Technical Report 149300 (2010).

Shiga, Y.

Smith, K.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC Superpixels,” EPFL Technical Report 149300 (2010).

Stinson, W. G.

Subhash, H.

Sugiyama, S.

Süsstrunk, S.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC Superpixels,” EPFL Technical Report 149300 (2010).

Swanson, E. A.

Takahashi, H.

Tan, O.

Tang, S.

Tokayer, J.

Tsuda, S.

Uematsu, S.

Villiger, M.

Wang, L.

Wang, R. K.

Wang, Y.

Weiter, J. J.

Wing, G. L.

Xiang, S. H.

J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in Optical Coherence Tomography,” Journal of Biomedical Optics 4, 95–105 (1999).
[Crossref] [PubMed]

Yamanari, M.

S. Makita, M. Yamanari, and Y. Yasuno, “Generalized Jones matrix optical coherence tomography: performance and local birefringence imaging,” Opt. Express 18, 854–876 (2010).
[Crossref] [PubMed]

S. Makita, Y. Hong, M. Yamanari, T. Yatagai, and Y. Yasuno, “Optical coherence angiography,” Opt. Express 14, 7821–7840 (2006).
[Crossref] [PubMed]

Yarmush, M.

Yasuno, Y.

J. F. d. Boer, C. K. Hitzenberger, and Y. Yasuno, “Polarization sensitive optical coherence tomography – a review [Invited],” Biomed. Opt. Express 8, 1838–1873 (2017).
[Crossref] [PubMed]

U. Bhaskar, Y.-J. Hong, M. Miura, and Y. Yasuno, “Five-dimensional analysis of multi-contrast Jones matrix tomography of posterior eye,” Proceedings of SPIE 8930, 893008 (2014).
[Crossref]

S. Makita, M. Yamanari, and Y. Yasuno, “Generalized Jones matrix optical coherence tomography: performance and local birefringence imaging,” Opt. Express 18, 854–876 (2010).
[Crossref] [PubMed]

S. Makita, Y. Hong, M. Yamanari, T. Yatagai, and Y. Yasuno, “Optical coherence angiography,” Opt. Express 14, 7821–7840 (2006).
[Crossref] [PubMed]

Yatagai, T.

S. Makita, Y. Hong, M. Yamanari, T. Yatagai, and Y. Yasuno, “Optical coherence angiography,” Opt. Express 14, 7821–7840 (2006).
[Crossref] [PubMed]

Yokoyama, Y.

Yoon, Y.

Yung, K. M.

J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in Optical Coherence Tomography,” Journal of Biomedical Optics 4, 95–105 (1999).
[Crossref] [PubMed]

Zhang, J.

Zotter, S.

Biomed. Opt. Express (9)

J. F. d. Boer, C. K. Hitzenberger, and Y. Yasuno, “Polarization sensitive optical coherence tomography – a review [Invited],” Biomed. Opt. Express 8, 1838–1873 (2017).
[Crossref] [PubMed]

Biomedical Optics Express (2)

IEEE Trans. Pattern Anal. Mach. Intell. (1)

Invest. Ophthalmol. Vis. Sci. (1)

Investigative Ophthalmology & Visual Science (1)

J. Invest. Dermatol. (1)

J. Mod. Opt. (1)

Journal of Biomedical Optics (1)

J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in Optical Coherence Tomography,” Journal of Biomedical Optics 4, 95–105 (1999).
[Crossref] [PubMed]

Ophthalmology (1)

Opt. Express (8)

S. Makita, M. Yamanari, and Y. Yasuno, “Generalized Jones matrix optical coherence tomography: performance and local birefringence imaging,” Opt. Express 18, 854–876 (2010).
[Crossref] [PubMed]

S. Makita, Y. Hong, M. Yamanari, T. Yatagai, and Y. Yasuno, “Optical coherence angiography,” Opt. Express 14, 7821–7840 (2006).
[Crossref] [PubMed]

Opt. Lett. (3)

Proceedings of SPIE (1)

U. Bhaskar, Y.-J. Hong, M. Miura, and Y. Yasuno, “Five-dimensional analysis of multi-contrast Jones matrix tomography of posterior eye,” Proceedings of SPIE 8930, 893008 (2014).
[Crossref]

Prog. Retin. Eye Res. (1)

M. Pircher, C. K. Hitzenberger, and U. Schmidt-Erfurth, “Polarization sensitive optical coherence tomography in the human eye,” Prog. Retin. Eye Res. 30, 431–451 (2011).
[Crossref] [PubMed]

Science (1)

Other (4)

American National Standard Institute, American National Standard for the Safe Use of Lasers ANSI Z 136.1-2014 (American National Standards Institute, New York, 2014).

X. Ren and J. Malik, “Learning a classification model for segmentation,” Proc. Ninth IEEE Int. Conf. Comp. Vis. pp. 10–17 vol.1 (2003).

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC Superpixels,” EPFL Technical Report 149300 (2010).

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Fig. 1 Flow of optimization for parameters used in superpixel generation. The parameters to be optimized include the compactness factor and weights of the optical features. Three loops are nested in the optimization process: the solid red, dashed green, and dashed and dotted blue boxes indicate the loops for compactness factor optimization (Section 3.2.3), weight optimization, (Section 3.2.2), and superpixel generation (Section 3.1), respectively.

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Fig. 2 The alteration of the weights of the optical features during iterative optimization in three trials. (a) to (c) correspond to the first to third trials, respectively. Each plot color represents each optical feature as OCT intensity (purple, circle), OCTA (green, triangle), BR (red, square), and DOPU (blue, cross).

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Fig. 3 (a) The alterations of the cost function E(w) and (b) the contribution metrics of each optical feature during the iterative optimization of the weights in the first trial. Each plot color in (b) represents each optical feature as OCT intensity (purple, circle), OCTA (green, triangle), BR (red, square), and DOPU (blue, cross).

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Fig. 4 Superpixels generated with several compactness factor configurations. The first column ((a), (c), (e), and (g)) shows the kernel-averaged images with superpixel kernels. The second column ((b), (d), (f), and (h)) shows the superpixels where each superpixel is displayed with randomly assigned colors. The third column ((j)–(r)) shows magnified images of the images in the first and second columns, where the magnified regions are indicated by yellow boxes. Each row corresponds to the compactness factor of 100, 70, 40, and 5. The bottom row ((i) and (r)) shows the original (non-superpixelized) OCT intensity images.

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Fig. 5 Mean normalized-ISPVs for several compactness factor configurations.

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Fig. 6 An example of an ONH. The first column shows the original images of (a) OCT intensity, (c) OCTA, (e) BR, and (g) DOPU. The second column ((b), (d), (f), and (h)) shows the kernel-averaged images with reshaped superpixels that correspond to the images in the first column. (i)–(k), (l)–(n), (o)–(q), and (r)–(t) show three types of images in the square windows in (b), (d), (f), and (h), respectively; (i), (l), (o), and (r) show the original images; (j), (m), (p), and (s) show the kernel-averaged images with initial (hexagon) superpixels; and (k), (n), (q), and (t) show the kernel-averaged images with reshaped superpixels.

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Fig. 7 An example of a macula. The first column shows the original images of (a) OCT intensity, (c) OCTA, (e) BR, and (g) DOPU. The second column ((b), (d), (f), and (h)) shows kernel-averaged images with reshaped superpixels. The square boxes in (b), (d), (f), and (h) show the locations of (i)–(k), (l)–(n), (o)–(q), and (r)–(t). (i), (l), (o), and (r) show the magnified original images; (j), (m), (p), and (s) show the kernel-averaged images with initial (hexagon) superpixels; and (k), (n), (q), and (t) show the kernel-averaged images with reshaped superpixels.

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Fig. 8 Comparison among intensity images of original (a), moving average with a rectangle kernel (b), and local average with superpixel kernels (c). The rectangle kernel has a size of 6 × 6 pixels and the average pixel number per superpixel kernel is 36. The insets are the magnified images of the regions indicated by the yellow box. (a) and (c) are the same with Figs. 6(a) and 6(b), respectively.

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Fig. 9 The comparison of superpixel generated by the presented method (standard superpixel) and that generated only from intensity OCT. The first to third columns show original images, standard superpixel kernel averaged images, and intensity-only superpixel kernel averaged images, respectively.

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Tables (6)

Table 1 Validation for intra-dataset universality examined with an ONH of subject-1.

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Table 2 Validation for intra-dataset universality examined with a macula of subject-2.

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Table 3 Validation of inter-dataset-and-intra-subject universality. The validation was performed with ONH datasets. VOC denotes variance of the contributions and nISPV denotes normalized-ISPV. “Optimal” indicates that the weights were optimized and w/o optimization indicates that the weights were not optimized but unities.

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Table 4 Validation of inter-dataset-and-intra-subject universality. The validation was performed with macular datasets. VOC denotes variance of the contributions and nISPV denotes normalized-ISPV. “Optimal” indicates that the weights were optimized and w/o optimization indicates that the weights were not optimized but unities.

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Table 5 Validation of inter-subject universality. The validation was performed with ONH datasets. VOC denotes the variance of the contributions and nISPV denotes the normalized-ISPV. “Optimal” indicates that the weights were optimized and w/o optimization indicates that the weights were not optimized but unities.

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Table 6 Validation of inter-subject universality. The validation was performed with macular datasets. VOC denotes the variance of contributions and nISPV denotes the normalized-ISPV. “Optimal” indicates that the weights were optimized and w/o optimization indicates that the weights were not optimized but unities.

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Equations (14)

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D_{o} (a, b) \equiv \sqrt{\sum_{i = 1}^{4} w_{i} {(a_{i} - b_{i})}^{2}},

D_{s} (a, b) \equiv \sqrt{\sum_{i = 5}^{6} {(a_{i} - b_{i})}^{2}},

D_{t} (a, b) \equiv D_{o} (a, b) + \frac{m}{S} D_{s} (a, b),

σ_{i, k}^{2} = \frac{1}{s_{k}} \sum_{j = 1}^{s_{k}} w_{i} {(x_{i, j, k} - {\bar{x}}_{i, k})}^{2},

V_{i} \equiv \frac{1}{K} \sum_{k = 1}^{K} σ_{i, k}^{2},

C_{i} \equiv {(\frac{V_{i}^{r e s h a p e d}}{V_{i}^{i n i t i a l}})}^{- 1} = \frac{V_{i}^{i n i t i a l}}{V_{i}^{r e s h a p e d}},

E (w) = \frac{1}{4} \sum_{i = 1}^{4} {(C_{i} (w_{i}) - \frac{1}{4} \sum_{i = 1}^{4} C_{i} (w_{i}))}^{2},

w^{(l + 1)} \propto w^{(l)} + α d^{(l)},

d^{(l)} \equiv - [C^{(l)} - \max {C^{(l)}}],

\begin{array}{l} τ_{D} = [(τ_{a} + 2 τ_{m}) n_{o} + (n_{o} - 1) τ_{a} + τ_{s q r t}] + [(τ_{a} + τ_{m}) n_{s} + (n_{s} - 1) τ_{a} + τ_{s q r t}] \\ = 2 (n_{o} + n_{s} - 1) τ_{a} + (2 n_{o} + n_{s}) τ_{m} + 2 τ_{s q r t}, \end{array}

\begin{array}{l} τ_{C} = (n_{o} + n_{s}) [\sum_{k = 0}^{K - 1} (N_{k} - 1) τ_{a} + K τ_{d}] \\ = (n_{o} + n_{s}) [(N - K) τ_{a} + K τ_{m}], \end{array}

τ_{t o t a l} ≃ [4 N τ_{D} + τ_{C}] ι + τ_{C E},

τ_{t o t a l} \sim {[(8 n_{o} + 4 n_{s}) τ_{m} + 8 τ_{s q r t}] N + (n_{o} + n_{s}) τ_{m} K} ι + τ_{C E} .

τ_{t o t a l} \sim ι (48 N + 6 K) τ_{m} + τ_{C E} .

	Training B-scan 1	Test-1 B-scan 2	Test-2 B-scan 3	Test-3 B-scan 4
Variance of contributions	0.000111	0.00286	0.00377	0.00523
Largest normalized-ISPV with optimal weights	0.551	0.569	0.578	0.572
Largest normalized-ISPV without optimization	0.684	0.684	0.673	0.772

	Training B-scan 1	Test-1 B-scan 2	Test-2 B-scan 3	Test-3 B-scan 4
Variance of contributions	0.0000700	0.00206	0.00221	0.00404
Largest nISPV with optimal weights	0.553	0.589	0.581	0.593
Largest nISPV without optimization	0.648	0.662	0.655	0.668

	Training-1 Subject-1	Test-1 Subject-1	Training-2 Subject-2	Test-2 Subject-2	Training-3 Subject-3	Test-3 Subject-3
VOC	0.000111	0.00244	0.0000304	0.00609	0.0000787	0.00446
Largest nISPV, optimal	0.551	0.592	0.567	0.590	0.541	0.586
Largest nISPV, w/o optimization	0.684	0.750	0.731	0.777	0.795	0.781

	Training-1 Subject-2	Test-1 Subject-2	Training-2 Subject-4	Test-2 Subject-4	Training-3 Subject-1	Test-3 Subject-1
VOC	0.000111	0.00541	0.00000604	0.00307	0.00000785	0.00944
Largest nISPV, optimal	0.553	0.604	0.552	0.582	0.546	0.609
Largest nISPV, w/o optimization	0.648	0.676	0.641	0.656	0.630	0.627

	Training Subject-1	Test-1 Subject-3	Test-2 Subject-4	Test-3 Subject-2
VOC	0.000111	0.00368	0.00373	0.00516
Largest nISPV, optimal	0.551	0.603	0.571	0.587
Largest nISPV, w/o optimization	0.684	0.731	0.795	0.746

	Training Subject-2	Test-1 Subject-3	Test-2 Subject-4	Test-3 Subject-1
VOC	0.0000700	0.0116	0.0165	0.00602
Largest nISPV, optimal	0.553	0.627	0.614	0.567
Largest nISPV, w/o optimization	0.648	0.731	0.641	0.630

Abstract

References

2017 (5)

2016 (3)

2015 (3)

2014 (3)

2013 (1)

2012 (4)

2011 (3)

2010 (2)

2008 (2)

2006 (1)

2004 (1)

1999 (1)

1995 (1)

1991 (1)

1986 (1)

Achanta, R.

Ahlers, C.

Aizawa, N.

An, L.

Austen, W. G.

Baumann, B.

Baumann, S. O.

Beheregaray, S.

Bhaskar, U.

Boer, J. F. d.

Bouma, B. E.

Broelsch, G. F.

Chang, W.

Chen, Z.

Chung, K. Y.

Delori, F. C.

Duan, L.

Duker, J. S.

Fitch, K. A.

Flotte, T.

Fua, P.

Fujimoto, J. G.

Fukuda, S.

Geitzenauer, W.

Glösmann, M.

Golbaz, I.

Golberg, A.

Götzinger, E.

Gregory, K.

Gröger, M.

Guo, S.

Hee, M. R.

Himori, N.

Hitzenberger, C. K.

Hong, Y.

Hong, Y.-J.

Hornegger, J.

Huang, D.

Ikuno, Y.

Izatt, J. A.

Jang, W. H.

Jia, Y.

Ju, M. J.

Kaji, Y.

Kasaragod, D.

Khan, S.

Kim, B.

Kim, K. H.

Kiuchi, T.

Kokubun, T.

Konegger, T.

Kraus, M. F.

Kunikata, H.

Kunimatsu-Sanuki, S.

Kurokawa, K.

Kwon, S.

Lammer, J.

Leitgeb, R. A.

Li, E.

Lian, C. G.

Lim, Y.

Lin, C. P.

Litschauer, M.