Abstract

Patient motion artifacts are often visible in densely sampled or large wide field-of-view (FOV) retinal optical coherence tomography (OCT) volumes. A popular strategy for reducing motion artifacts is to capture two orthogonally oriented volumetric scans. However, due to larger volume sizes, longer acquisition times, and corresponding larger motion artifacts, the registration of wide FOV scans remains a challenging problem. In particular, gaps in data acquisition due to eye motion, such as saccades, can be significant and their modeling becomes critical for successful registration. In this article, we develop a complete computational pipeline for the automatic motion correction and accurate registration of wide FOV orthogonally scanned OCT images of the human retina. The proposed framework utilizes the retinal boundary segmentation as a guide for registration and requires only a minimal transformation of the acquired data to produce a successful registration. It includes saccade detection and correction, a custom version of the optical flow algorithm for dense lateral registration and a linear optimization approach for axial registration. Utilizing a wide FOV swept source OCT system, we acquired retinal volumes of 12 subjects and we provide qualitative and quantitative experimental results to validate the state-of-the-art effectiveness of the proposed technique. The source code corresponding to the proposed algorithm is available online.

© 2016 Optical Society of America

Full Article  |  PDF Article
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    [Crossref]

2016 (4)

P. Zang, G. Liu, M. Zhang, C. Dongye, J. Wang, A. D. Pechauer, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and et al., “Automated motion correction using parallel-strip registration for wide-field en face OCT angiogram,” Biomedical Optics Express 7, 2823–2836 (2016).
[Crossref] [PubMed]

R. P. McNabb, D. S. Grewal, R. Mehta, S. G. Schuman, J. A. Izatt, T. H. Mahmoud, G. J. Jaffe, P. Mruthyunjaya, and A. N. Kuo, “Wide field of view swept-source optical coherence tomography for peripheral retinal disease,” Br. J. Ophthalmol. 2015307480 (2016).
[Crossref] [PubMed]

B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” Journal of Biomedical Optics 21, 076015 (2016).
[Crossref]

I. Gorczynska, J. V. Migacz, R. J. Zawadzki, A. G. Capps, and J. S. Werner, “Comparison of amplitude-decorrelation, speckle-variance and phase-variance oct angiography methods for imaging the human retina and choroid,” Biomed. Opt. Express 7, 911–942 (2016).
[Crossref] [PubMed]

2015 (5)

J. Polans, B. Jaeken, R. P. McNabb, P. Artal, and J. A. Izatt, “Wide-field optical model of the human eye with asymmetrically tilted and decentered lens that reproduces measured ocular aberrations,” Optica 2, 124–134 (2015).
[Crossref]

S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6, 1172–1194 (2015).
[Crossref] [PubMed]

R. P. McNabb, S. Farsiu, S. S. Stinnett, J. A. Izatt, and A. N. Kuo, “Optical coherence tomography accurately measures corneal power change from laser refractive surgery,” Ophthalmology 122, 677–686 (2015).
[Crossref]

A. Baghaie, Z. Yu, and R. M. D’Souza, “State-of-the-art in retinal optical coherence tomography image analysis,” Quant. Imaging Med. Surg. 5, 603 (2015).
[PubMed]

M. F. Kraus and J. Hornegger, “OCT motion correction,” Optical Coherence Tomography: Technology and Applications 5459–476 (2015).
[Crossref]

2014 (2)

2013 (2)

2012 (3)

2011 (3)

X. Song, R. Estrada, S. J. Chiu, A.-H. Dhalla, C. A. Toth, J. A. Izatt, and S. Farsiu, “Segmentation-based registration of retinal optical coherence tomography images with pathology,” Invest. Ophthalmol. Vis. Sci. 52, 1309 (2011).

C. Liu, J. Yuen, and A. Torralba, “Sift flow: Dense correspondence across scenes and its applications,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 978–994 (2011).
[Crossref]

B. Antony, M. D. Abramoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-d method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. Opt. Express 2, 2403–2416 (2011).
[Crossref] [PubMed]

2010 (3)

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18, 19413–19428 (2010).
[Crossref] [PubMed]

M. D. Robinson, S. J. Chiu, J. Lo, C. Toth, J. Izatt, and S. Farsiu, “New applications of super-resolution in medical imaging,” Super-Resolution Imaging 2010384–412 (2010).

M. Tang, A. Chen, Y. Li, and D. Huang, “Corneal power measurement with fourier-domain optical coherence tomography,” J. Cataract Refract. Surg. 36, 2115–2122 (2010).
[Crossref] [PubMed]

2009 (1)

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-d intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
[Crossref] [PubMed]

2008 (1)

2006 (1)

2005 (1)

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Macular segmentation with optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 46, 2012 (2005).
[Crossref] [PubMed]

2004 (2)

1994 (1)

P. Soille, “Generalized geodesy via geodesic time,” Pattern Recognit. Lett. 15, 1235–1240 (1994).
[Crossref]

1993 (1)

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, and C. A. Puliafito, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

1950 (1)

F. Ratliff and L. A. Riggs, “Involuntary motions of the eye during monocular fixation,” J. Exp. Psychol. 40, 687 (1950).
[Crossref] [PubMed]

Abramoff, M. D.

Abràmoff, M. D.

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-d intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
[Crossref] [PubMed]

Allingham, M. J.

Antony, B.

Artal, P.

Baghaie, A.

A. Baghaie, Z. Yu, and R. M. D’Souza, “State-of-the-art in retinal optical coherence tomography image analysis,” Quant. Imaging Med. Surg. 5, 603 (2015).
[PubMed]

Baumann, B.

Beaton, S.

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Macular segmentation with optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 46, 2012 (2005).
[Crossref] [PubMed]

R. D. Ferguson, D. X. Hammer, L. A. Paunescu, S. Beaton, and J. S. Schuman, “Tracking optical coherence tomography,” Opt. Lett. 29, 2139–2141 (2004).
[Crossref] [PubMed]

Bock, R.

Bouma, B.

Branchini, L.

Buades, A.

A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” in “IEEE Computer Society Conference on Computer Vision and Pattern Recognition,” , vol. 2 (IEEE, 2005), vol. 2, pp. 60–65.

Budai, A.

Burns, T. L.

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-d intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
[Crossref] [PubMed]

Cable, A.

Capps, A. G.

Chang, W.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Chen, A.

M. Tang, A. Chen, Y. Li, and D. Huang, “Corneal power measurement with fourier-domain optical coherence tomography,” J. Cataract Refract. Surg. 36, 2115–2122 (2010).
[Crossref] [PubMed]

Chen, C.-L.

Chen, M.

S. Ricco, M. Chen, H. Ishikawa, G. Wollstein, and J. Schuman, “Correcting motion artifacts in retinal spectral domain optical coherence tomography via image registration,” in “Medical Image Computing and Computer-Assisted Intervention,” , vol. 5761 of Lecture Notes in Computer Science G.-Z. Yang, D. Hawkes, D. Rueckert, A. Noble, and C. Taylor, eds. (SpringerBerlin Heidelberg, 2009), pp. 100–107.

Chen, Y.

B. Potsaid, I. Gorczynska, V. J. Srinivasan, Y. Chen, J. Jiang, A. Cable, and J. G. Fujimoto, “Ultrahigh speed spectral/fourier domain OCT ophthalmic imaging at 70,000 to 312,500 axial scans per second,” Opt. Express 16, 15149–15169 (2008).
[Crossref] [PubMed]

Y.-J. Hong, Y. Chen, E. Li, M. Miura, S. Makita, and Y. Yasuno, “Eye motion corrected oct imaging with lissajous scan pattern,” in “Proc. SPIE,” (International Society for Optics and Photonics, 2016), pp. 96930P.

Chiu, S. J.

Choi, S. S.

R. J. Zawadzki, A. R. Fuller, S. S. Choi, D. F. Wiley, B. Hamann, and J. S. Werner, “Correction of motion artifacts and scanning beam distortions in 3d ophthalmic optical coherence tomography imaging,” in “Biomedical Optics (BiOS) 2007,” (International Society for Optics and Photonics, 2007), pp. 642607.

Coll, B.

A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” in “IEEE Computer Society Conference on Computer Vision and Pattern Recognition,” , vol. 2 (IEEE, 2005), vol. 2, pp. 60–65.

Cousins, S. W.

Cunefare, D.

B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” Journal of Biomedical Optics 21, 076015 (2016).
[Crossref]

D’Souza, R. M.

A. Baghaie, Z. Yu, and R. M. D’Souza, “State-of-the-art in retinal optical coherence tomography image analysis,” Quant. Imaging Med. Surg. 5, 603 (2015).
[PubMed]

De Boer, J.

Dhalla, A.-H.

D. Nankivil, A.-H. Dhalla, N. Gahm, K. Shia, S. Farsiu, and J. A. Izatt, “Coherence revival multiplexed, buffered swept source optical coherence tomography: 400 khz imaging with a 100 khz source,” Opt. Lett. 39, 3740–3743 (2014).
[Crossref] [PubMed]

X. Song, R. Estrada, S. J. Chiu, A.-H. Dhalla, C. A. Toth, J. A. Izatt, and S. Farsiu, “Segmentation-based registration of retinal optical coherence tomography images with pathology,” Invest. Ophthalmol. Vis. Sci. 52, 1309 (2011).

Ding, X.

H. He, G. Liu, P. Mo, B. Li, J. Wu, and X. Ding, “Correction of motion artifact in 3d retinal optical coherence tomography imaging,” in “International Congress on Image and Signal Processing,” , vol. 1 (IEEE, 2013), vol. 1, pp. 261–265.

Dongye, C.

P. Zang, G. Liu, M. Zhang, C. Dongye, J. Wang, A. D. Pechauer, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and et al., “Automated motion correction using parallel-strip registration for wide-field en face OCT angiogram,” Biomedical Optics Express 7, 2823–2836 (2016).
[Crossref] [PubMed]

Duker, J. S.

J. S. Schuman, C. A. Puliafito, J. G. Fujimoto, and J. S. Duker, Optical Coherence Tomography of Ocular Diseases (SlackNew Jersey, 2004).

Estrada, R.

H. C. Hendargo, R. Estrada, S. J. Chiu, C. Tomasi, S. Farsiu, and J. A. Izatt, “Automated non-rigid registration and mosaicing for robust imaging of distinct retinal capillary beds using speckle variance optical coherence tomography,” Biomed. Opt. Express 4, 803–821 (2013).
[Crossref] [PubMed]

X. Song, R. Estrada, S. J. Chiu, A.-H. Dhalla, C. A. Toth, J. A. Izatt, and S. Farsiu, “Segmentation-based registration of retinal optical coherence tomography images with pathology,” Invest. Ophthalmol. Vis. Sci. 52, 1309 (2011).

Farsiu, S.

B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” Journal of Biomedical Optics 21, 076015 (2016).
[Crossref]

R. P. McNabb, S. Farsiu, S. S. Stinnett, J. A. Izatt, and A. N. Kuo, “Optical coherence tomography accurately measures corneal power change from laser refractive surgery,” Ophthalmology 122, 677–686 (2015).
[Crossref]

S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6, 1172–1194 (2015).
[Crossref] [PubMed]

D. Nankivil, A.-H. Dhalla, N. Gahm, K. Shia, S. Farsiu, and J. A. Izatt, “Coherence revival multiplexed, buffered swept source optical coherence tomography: 400 khz imaging with a 100 khz source,” Opt. Lett. 39, 3740–3743 (2014).
[Crossref] [PubMed]

F. LaRocca, D. Nankivil, S. Farsiu, and J. A. Izatt, “Handheld simultaneous scanning laser ophthalmoscopy and optical coherence tomography system,” Biomed. Opt. Express 4, 2307–2321 (2013).
[Crossref] [PubMed]

H. C. Hendargo, R. Estrada, S. J. Chiu, C. Tomasi, S. Farsiu, and J. A. Izatt, “Automated non-rigid registration and mosaicing for robust imaging of distinct retinal capillary beds using speckle variance optical coherence tomography,” Biomed. Opt. Express 4, 803–821 (2013).
[Crossref] [PubMed]

R. P. McNabb, F. LaRocca, S. Farsiu, A. N. Kuo, and J. A. Izatt, “Distributed scanning volumetric SDOCT for motion corrected corneal biometry,” Biomed. Opt. Express 3, 2050–2065 (2012).
[Crossref] [PubMed]

X. Song, R. Estrada, S. J. Chiu, A.-H. Dhalla, C. A. Toth, J. A. Izatt, and S. Farsiu, “Segmentation-based registration of retinal optical coherence tomography images with pathology,” Invest. Ophthalmol. Vis. Sci. 52, 1309 (2011).

M. D. Robinson, S. J. Chiu, J. Lo, C. Toth, J. Izatt, and S. Farsiu, “New applications of super-resolution in medical imaging,” Super-Resolution Imaging 2010384–412 (2010).

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18, 19413–19428 (2010).
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Ferguson, R. D.

Flotte, T.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
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Gan, Y.

Y. Gan, W. Yao, K. M. Myers, and C. P. Hendon, “An automated 3d registration method for optical coherence tomography volumes,” in “IEEE International Conference in Engineering in Medicine and Biology Society,” (IEEE, 2014), pp. 3873–3876.

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B. Antony, M. D. Abramoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-d method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. Opt. Express 2, 2403–2416 (2011).
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A. Montuoro, J. Wu, S. Waldstein, B. Gerendas, G. Langs, C. Simader, and U. Schmidt-Erfurth, “Motion artefact correction in retinal optical coherence tomography using local symmetry,” in “Medical Image Computing and Computer-Assisted Intervention,” (Springer, 2014), pp. 130–137.

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D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
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B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” Journal of Biomedical Optics 21, 076015 (2016).
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R. P. McNabb, D. S. Grewal, R. Mehta, S. G. Schuman, J. A. Izatt, T. H. Mahmoud, G. J. Jaffe, P. Mruthyunjaya, and A. N. Kuo, “Wide field of view swept-source optical coherence tomography for peripheral retinal disease,” Br. J. Ophthalmol. 2015307480 (2016).
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R. J. Zawadzki, A. R. Fuller, S. S. Choi, D. F. Wiley, B. Hamann, and J. S. Werner, “Correction of motion artifacts and scanning beam distortions in 3d ophthalmic optical coherence tomography imaging,” in “Biomedical Optics (BiOS) 2007,” (International Society for Optics and Photonics, 2007), pp. 642607.

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He, H.

H. He, G. Liu, P. Mo, B. Li, J. Wu, and X. Ding, “Correction of motion artifact in 3d retinal optical coherence tomography imaging,” in “International Congress on Image and Signal Processing,” , vol. 1 (IEEE, 2013), vol. 1, pp. 261–265.

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Hee, M. R.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
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Hendon, C. P.

Y. Gan, W. Yao, K. M. Myers, and C. P. Hendon, “An automated 3d registration method for optical coherence tomography volumes,” in “IEEE International Conference in Engineering in Medicine and Biology Society,” (IEEE, 2014), pp. 3873–3876.

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Hong, Y.-J.

Y.-J. Hong, Y. Chen, E. Li, M. Miura, S. Makita, and Y. Yasuno, “Eye motion corrected oct imaging with lissajous scan pattern,” in “Proc. SPIE,” (International Society for Optics and Photonics, 2016), pp. 96930P.

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Huang, D.

P. Zang, G. Liu, M. Zhang, C. Dongye, J. Wang, A. D. Pechauer, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and et al., “Automated motion correction using parallel-strip registration for wide-field en face OCT angiogram,” Biomedical Optics Express 7, 2823–2836 (2016).
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M. Tang, A. Chen, Y. Li, and D. Huang, “Corneal power measurement with fourier-domain optical coherence tomography,” J. Cataract Refract. Surg. 36, 2115–2122 (2010).
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E. A. Swanson, J. Izatt, C. Lin, J. Fujimoto, J. Schuman, M. Hee, D. Huang, and C. Puliafito, “In vivo retinal imaging by optical coherence tomography,” Opt. Lett. 18, 1864–1866 (1993).
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D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
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P. Zang, G. Liu, M. Zhang, C. Dongye, J. Wang, A. D. Pechauer, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and et al., “Automated motion correction using parallel-strip registration for wide-field en face OCT angiogram,” Biomedical Optics Express 7, 2823–2836 (2016).
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M. F. Kraus, J. J. Liu, J. Schottenhamml, C.-L. Chen, A. Budai, L. Branchini, T. Ko, H. Ishikawa, G. Wollstein, J. Schuman, and et al., “Quantitative 3D-OCT motion correction with tilt and illumination correction, robust similarity measure and regularization,” Biomed. Opt. Express 5, 2591–2613 (2014).
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J. Xu, H. Ishikawa, G. Wollstein, L. Kagemann, and J. S. Schuman, “Alignment of 3-d optical coherence tomography scans to correct eye movement using a particle filtering,” IEEE Trans. Med. Imaging 31, 1337–1345 (2012).
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H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Macular segmentation with optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 46, 2012 (2005).
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S. Ricco, M. Chen, H. Ishikawa, G. Wollstein, and J. Schuman, “Correcting motion artifacts in retinal spectral domain optical coherence tomography via image registration,” in “Medical Image Computing and Computer-Assisted Intervention,” , vol. 5761 of Lecture Notes in Computer Science G.-Z. Yang, D. Hawkes, D. Rueckert, A. Noble, and C. Taylor, eds. (SpringerBerlin Heidelberg, 2009), pp. 100–107.

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M. D. Robinson, S. J. Chiu, J. Lo, C. Toth, J. Izatt, and S. Farsiu, “New applications of super-resolution in medical imaging,” Super-Resolution Imaging 2010384–412 (2010).

E. A. Swanson, J. Izatt, C. Lin, J. Fujimoto, J. Schuman, M. Hee, D. Huang, and C. Puliafito, “In vivo retinal imaging by optical coherence tomography,” Opt. Lett. 18, 1864–1866 (1993).
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Izatt, J. A.

B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” Journal of Biomedical Optics 21, 076015 (2016).
[Crossref]

R. P. McNabb, D. S. Grewal, R. Mehta, S. G. Schuman, J. A. Izatt, T. H. Mahmoud, G. J. Jaffe, P. Mruthyunjaya, and A. N. Kuo, “Wide field of view swept-source optical coherence tomography for peripheral retinal disease,” Br. J. Ophthalmol. 2015307480 (2016).
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R. P. McNabb, S. Farsiu, S. S. Stinnett, J. A. Izatt, and A. N. Kuo, “Optical coherence tomography accurately measures corneal power change from laser refractive surgery,” Ophthalmology 122, 677–686 (2015).
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S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6, 1172–1194 (2015).
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J. Polans, B. Jaeken, R. P. McNabb, P. Artal, and J. A. Izatt, “Wide-field optical model of the human eye with asymmetrically tilted and decentered lens that reproduces measured ocular aberrations,” Optica 2, 124–134 (2015).
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H. C. Hendargo, R. Estrada, S. J. Chiu, C. Tomasi, S. Farsiu, and J. A. Izatt, “Automated non-rigid registration and mosaicing for robust imaging of distinct retinal capillary beds using speckle variance optical coherence tomography,” Biomed. Opt. Express 4, 803–821 (2013).
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R. P. McNabb, F. LaRocca, S. Farsiu, A. N. Kuo, and J. A. Izatt, “Distributed scanning volumetric SDOCT for motion corrected corneal biometry,” Biomed. Opt. Express 3, 2050–2065 (2012).
[Crossref] [PubMed]

X. Song, R. Estrada, S. J. Chiu, A.-H. Dhalla, C. A. Toth, J. A. Izatt, and S. Farsiu, “Segmentation-based registration of retinal optical coherence tomography images with pathology,” Invest. Ophthalmol. Vis. Sci. 52, 1309 (2011).

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18, 19413–19428 (2010).
[Crossref] [PubMed]

Jaeken, B.

Jaffe, G. J.

R. P. McNabb, D. S. Grewal, R. Mehta, S. G. Schuman, J. A. Izatt, T. H. Mahmoud, G. J. Jaffe, P. Mruthyunjaya, and A. N. Kuo, “Wide field of view swept-source optical coherence tomography for peripheral retinal disease,” Br. J. Ophthalmol. 2015307480 (2016).
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Jansonius, N. M.

Jiang, J.

Kagemann, L.

J. Xu, H. Ishikawa, G. Wollstein, L. Kagemann, and J. S. Schuman, “Alignment of 3-d optical coherence tomography scans to correct eye movement using a particle filtering,” IEEE Trans. Med. Imaging 31, 1337–1345 (2012).
[Crossref] [PubMed]

Keller, B.

B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” Journal of Biomedical Optics 21, 076015 (2016).
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Ko, T.

Kraus, M. F.

Kuo, A. N.

R. P. McNabb, D. S. Grewal, R. Mehta, S. G. Schuman, J. A. Izatt, T. H. Mahmoud, G. J. Jaffe, P. Mruthyunjaya, and A. N. Kuo, “Wide field of view swept-source optical coherence tomography for peripheral retinal disease,” Br. J. Ophthalmol. 2015307480 (2016).
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R. P. McNabb, S. Farsiu, S. S. Stinnett, J. A. Izatt, and A. N. Kuo, “Optical coherence tomography accurately measures corneal power change from laser refractive surgery,” Ophthalmology 122, 677–686 (2015).
[Crossref]

R. P. McNabb, F. LaRocca, S. Farsiu, A. N. Kuo, and J. A. Izatt, “Distributed scanning volumetric SDOCT for motion corrected corneal biometry,” Biomed. Opt. Express 3, 2050–2065 (2012).
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Langs, G.

A. Montuoro, J. Wu, S. Waldstein, B. Gerendas, G. Langs, C. Simader, and U. Schmidt-Erfurth, “Motion artefact correction in retinal optical coherence tomography using local symmetry,” in “Medical Image Computing and Computer-Assisted Intervention,” (Springer, 2014), pp. 130–137.

LaRocca, F.

Lee, K.

Li, B.

H. He, G. Liu, P. Mo, B. Li, J. Wu, and X. Ding, “Correction of motion artifact in 3d retinal optical coherence tomography imaging,” in “International Congress on Image and Signal Processing,” , vol. 1 (IEEE, 2013), vol. 1, pp. 261–265.

Li, D.

P. Zang, G. Liu, M. Zhang, C. Dongye, J. Wang, A. D. Pechauer, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and et al., “Automated motion correction using parallel-strip registration for wide-field en face OCT angiogram,” Biomedical Optics Express 7, 2823–2836 (2016).
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Y.-J. Hong, Y. Chen, E. Li, M. Miura, S. Makita, and Y. Yasuno, “Eye motion corrected oct imaging with lissajous scan pattern,” in “Proc. SPIE,” (International Society for Optics and Photonics, 2016), pp. 96930P.

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Li, Y.

M. Tang, A. Chen, Y. Li, and D. Huang, “Corneal power measurement with fourier-domain optical coherence tomography,” J. Cataract Refract. Surg. 36, 2115–2122 (2010).
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Lin, C. P.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
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H. He, G. Liu, P. Mo, B. Li, J. Wu, and X. Ding, “Correction of motion artifact in 3d retinal optical coherence tomography imaging,” in “International Congress on Image and Signal Processing,” , vol. 1 (IEEE, 2013), vol. 1, pp. 261–265.

Liu, J. J.

Lo, J.

M. D. Robinson, S. J. Chiu, J. Lo, C. Toth, J. Izatt, and S. Farsiu, “New applications of super-resolution in medical imaging,” Super-Resolution Imaging 2010384–412 (2010).

Mahmoud, T. H.

B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” Journal of Biomedical Optics 21, 076015 (2016).
[Crossref]

R. P. McNabb, D. S. Grewal, R. Mehta, S. G. Schuman, J. A. Izatt, T. H. Mahmoud, G. J. Jaffe, P. Mruthyunjaya, and A. N. Kuo, “Wide field of view swept-source optical coherence tomography for peripheral retinal disease,” Br. J. Ophthalmol. 2015307480 (2016).
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Y.-J. Hong, Y. Chen, E. Li, M. Miura, S. Makita, and Y. Yasuno, “Eye motion corrected oct imaging with lissajous scan pattern,” in “Proc. SPIE,” (International Society for Optics and Photonics, 2016), pp. 96930P.

Mayer, M. A.

McNabb, R. P.

R. P. McNabb, D. S. Grewal, R. Mehta, S. G. Schuman, J. A. Izatt, T. H. Mahmoud, G. J. Jaffe, P. Mruthyunjaya, and A. N. Kuo, “Wide field of view swept-source optical coherence tomography for peripheral retinal disease,” Br. J. Ophthalmol. 2015307480 (2016).
[Crossref] [PubMed]

R. P. McNabb, S. Farsiu, S. S. Stinnett, J. A. Izatt, and A. N. Kuo, “Optical coherence tomography accurately measures corneal power change from laser refractive surgery,” Ophthalmology 122, 677–686 (2015).
[Crossref]

J. Polans, B. Jaeken, R. P. McNabb, P. Artal, and J. A. Izatt, “Wide-field optical model of the human eye with asymmetrically tilted and decentered lens that reproduces measured ocular aberrations,” Optica 2, 124–134 (2015).
[Crossref]

R. P. McNabb, F. LaRocca, S. Farsiu, A. N. Kuo, and J. A. Izatt, “Distributed scanning volumetric SDOCT for motion corrected corneal biometry,” Biomed. Opt. Express 3, 2050–2065 (2012).
[Crossref] [PubMed]

Mehta, R.

R. P. McNabb, D. S. Grewal, R. Mehta, S. G. Schuman, J. A. Izatt, T. H. Mahmoud, G. J. Jaffe, P. Mruthyunjaya, and A. N. Kuo, “Wide field of view swept-source optical coherence tomography for peripheral retinal disease,” Br. J. Ophthalmol. 2015307480 (2016).
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Migacz, J. V.

Miura, M.

Y.-J. Hong, Y. Chen, E. Li, M. Miura, S. Makita, and Y. Yasuno, “Eye motion corrected oct imaging with lissajous scan pattern,” in “Proc. SPIE,” (International Society for Optics and Photonics, 2016), pp. 96930P.

Mo, P.

H. He, G. Liu, P. Mo, B. Li, J. Wu, and X. Ding, “Correction of motion artifact in 3d retinal optical coherence tomography imaging,” in “International Congress on Image and Signal Processing,” , vol. 1 (IEEE, 2013), vol. 1, pp. 261–265.

Montuoro, A.

A. Montuoro, J. Wu, S. Waldstein, B. Gerendas, G. Langs, C. Simader, and U. Schmidt-Erfurth, “Motion artefact correction in retinal optical coherence tomography using local symmetry,” in “Medical Image Computing and Computer-Assisted Intervention,” (Springer, 2014), pp. 130–137.

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Mruthyunjaya, P.

R. P. McNabb, D. S. Grewal, R. Mehta, S. G. Schuman, J. A. Izatt, T. H. Mahmoud, G. J. Jaffe, P. Mruthyunjaya, and A. N. Kuo, “Wide field of view swept-source optical coherence tomography for peripheral retinal disease,” Br. J. Ophthalmol. 2015307480 (2016).
[Crossref] [PubMed]

Myers, K. M.

Y. Gan, W. Yao, K. M. Myers, and C. P. Hendon, “An automated 3d registration method for optical coherence tomography volumes,” in “IEEE International Conference in Engineering in Medicine and Biology Society,” (IEEE, 2014), pp. 3873–3876.

Nankivil, D.

Nicholas, P.

Paunescu, L. A.

Pechauer, A. D.

P. Zang, G. Liu, M. Zhang, C. Dongye, J. Wang, A. D. Pechauer, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and et al., “Automated motion correction using parallel-strip registration for wide-field en face OCT angiogram,” Biomedical Optics Express 7, 2823–2836 (2016).
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Polans, J.

Potsaid, B.

Puliafito, C.

Puliafito, C. A.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
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J. S. Schuman, C. A. Puliafito, J. G. Fujimoto, and J. S. Duker, Optical Coherence Tomography of Ocular Diseases (SlackNew Jersey, 2004).

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S. Ricco, M. Chen, H. Ishikawa, G. Wollstein, and J. Schuman, “Correcting motion artifacts in retinal spectral domain optical coherence tomography via image registration,” in “Medical Image Computing and Computer-Assisted Intervention,” , vol. 5761 of Lecture Notes in Computer Science G.-Z. Yang, D. Hawkes, D. Rueckert, A. Noble, and C. Taylor, eds. (SpringerBerlin Heidelberg, 2009), pp. 100–107.

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M. D. Robinson, S. J. Chiu, J. Lo, C. Toth, J. Izatt, and S. Farsiu, “New applications of super-resolution in medical imaging,” Super-Resolution Imaging 2010384–412 (2010).

Russell, S. R.

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-d intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
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Schmidt-Erfurth, U.

A. Montuoro, J. Wu, S. Waldstein, B. Gerendas, G. Langs, C. Simader, and U. Schmidt-Erfurth, “Motion artefact correction in retinal optical coherence tomography using local symmetry,” in “Medical Image Computing and Computer-Assisted Intervention,” (Springer, 2014), pp. 130–137.

Schottenhamml, J.

Schuman, J.

M. F. Kraus, J. J. Liu, J. Schottenhamml, C.-L. Chen, A. Budai, L. Branchini, T. Ko, H. Ishikawa, G. Wollstein, J. Schuman, and et al., “Quantitative 3D-OCT motion correction with tilt and illumination correction, robust similarity measure and regularization,” Biomed. Opt. Express 5, 2591–2613 (2014).
[Crossref] [PubMed]

E. A. Swanson, J. Izatt, C. Lin, J. Fujimoto, J. Schuman, M. Hee, D. Huang, and C. Puliafito, “In vivo retinal imaging by optical coherence tomography,” Opt. Lett. 18, 1864–1866 (1993).
[Crossref] [PubMed]

S. Ricco, M. Chen, H. Ishikawa, G. Wollstein, and J. Schuman, “Correcting motion artifacts in retinal spectral domain optical coherence tomography via image registration,” in “Medical Image Computing and Computer-Assisted Intervention,” , vol. 5761 of Lecture Notes in Computer Science G.-Z. Yang, D. Hawkes, D. Rueckert, A. Noble, and C. Taylor, eds. (SpringerBerlin Heidelberg, 2009), pp. 100–107.

Schuman, J. S.

J. Xu, H. Ishikawa, G. Wollstein, L. Kagemann, and J. S. Schuman, “Alignment of 3-d optical coherence tomography scans to correct eye movement using a particle filtering,” IEEE Trans. Med. Imaging 31, 1337–1345 (2012).
[Crossref] [PubMed]

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Macular segmentation with optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 46, 2012 (2005).
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D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
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J. S. Schuman, C. A. Puliafito, J. G. Fujimoto, and J. S. Duker, Optical Coherence Tomography of Ocular Diseases (SlackNew Jersey, 2004).

Schuman, S. G.

R. P. McNabb, D. S. Grewal, R. Mehta, S. G. Schuman, J. A. Izatt, T. H. Mahmoud, G. J. Jaffe, P. Mruthyunjaya, and A. N. Kuo, “Wide field of view swept-source optical coherence tomography for peripheral retinal disease,” Br. J. Ophthalmol. 2015307480 (2016).
[Crossref] [PubMed]

Schunck, B. G.

B. K. Horn and B. G. Schunck, “Determining optical flow,” in “1981 Technical symposium East,” (International Society for Optics and Photonics, 1981), pp. 319–331.

Shia, K.

Simader, C.

A. Montuoro, J. Wu, S. Waldstein, B. Gerendas, G. Langs, C. Simader, and U. Schmidt-Erfurth, “Motion artefact correction in retinal optical coherence tomography using local symmetry,” in “Medical Image Computing and Computer-Assisted Intervention,” (Springer, 2014), pp. 130–137.

Soille, P.

P. Soille, “Generalized geodesy via geodesic time,” Pattern Recognit. Lett. 15, 1235–1240 (1994).
[Crossref]

Song, X.

X. Song, R. Estrada, S. J. Chiu, A.-H. Dhalla, C. A. Toth, J. A. Izatt, and S. Farsiu, “Segmentation-based registration of retinal optical coherence tomography images with pathology,” Invest. Ophthalmol. Vis. Sci. 52, 1309 (2011).

Sonka, M.

B. Antony, M. D. Abramoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-d method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. Opt. Express 2, 2403–2416 (2011).
[Crossref] [PubMed]

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-d intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
[Crossref] [PubMed]

Srinivasan, V. J.

Stein, D. M.

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Macular segmentation with optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 46, 2012 (2005).
[Crossref] [PubMed]

Stinnett, S. S.

R. P. McNabb, S. Farsiu, S. S. Stinnett, J. A. Izatt, and A. N. Kuo, “Optical coherence tomography accurately measures corneal power change from laser refractive surgery,” Ophthalmology 122, 677–686 (2015).
[Crossref]

Stinson, W. G.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
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E. A. Swanson, J. Izatt, C. Lin, J. Fujimoto, J. Schuman, M. Hee, D. Huang, and C. Puliafito, “In vivo retinal imaging by optical coherence tomography,” Opt. Lett. 18, 1864–1866 (1993).
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Tang, M.

M. Tang, A. Chen, Y. Li, and D. Huang, “Corneal power measurement with fourier-domain optical coherence tomography,” J. Cataract Refract. Surg. 36, 2115–2122 (2010).
[Crossref] [PubMed]

Tearney, G.

Tomasi, C.

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C. Liu, J. Yuen, and A. Torralba, “Sift flow: Dense correspondence across scenes and its applications,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 978–994 (2011).
[Crossref]

Toth, C.

M. D. Robinson, S. J. Chiu, J. Lo, C. Toth, J. Izatt, and S. Farsiu, “New applications of super-resolution in medical imaging,” Super-Resolution Imaging 2010384–412 (2010).

Toth, C. A.

X. Song, R. Estrada, S. J. Chiu, A.-H. Dhalla, C. A. Toth, J. A. Izatt, and S. Farsiu, “Segmentation-based registration of retinal optical coherence tomography images with pathology,” Invest. Ophthalmol. Vis. Sci. 52, 1309 (2011).

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18, 19413–19428 (2010).
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Vingerling, J. R.

Waldstein, S.

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Wang, J.

P. Zang, G. Liu, M. Zhang, C. Dongye, J. Wang, A. D. Pechauer, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and et al., “Automated motion correction using parallel-strip registration for wide-field en face OCT angiogram,” Biomedical Optics Express 7, 2823–2836 (2016).
[Crossref] [PubMed]

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I. Gorczynska, J. V. Migacz, R. J. Zawadzki, A. G. Capps, and J. S. Werner, “Comparison of amplitude-decorrelation, speckle-variance and phase-variance oct angiography methods for imaging the human retina and choroid,” Biomed. Opt. Express 7, 911–942 (2016).
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R. J. Zawadzki, A. R. Fuller, S. S. Choi, D. F. Wiley, B. Hamann, and J. S. Werner, “Correction of motion artifacts and scanning beam distortions in 3d ophthalmic optical coherence tomography imaging,” in “Biomedical Optics (BiOS) 2007,” (International Society for Optics and Photonics, 2007), pp. 642607.

Wiley, D. F.

R. J. Zawadzki, A. R. Fuller, S. S. Choi, D. F. Wiley, B. Hamann, and J. S. Werner, “Correction of motion artifacts and scanning beam distortions in 3d ophthalmic optical coherence tomography imaging,” in “Biomedical Optics (BiOS) 2007,” (International Society for Optics and Photonics, 2007), pp. 642607.

Wilson, D. J.

P. Zang, G. Liu, M. Zhang, C. Dongye, J. Wang, A. D. Pechauer, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and et al., “Automated motion correction using parallel-strip registration for wide-field en face OCT angiogram,” Biomedical Optics Express 7, 2823–2836 (2016).
[Crossref] [PubMed]

Wollstein, G.

M. F. Kraus, J. J. Liu, J. Schottenhamml, C.-L. Chen, A. Budai, L. Branchini, T. Ko, H. Ishikawa, G. Wollstein, J. Schuman, and et al., “Quantitative 3D-OCT motion correction with tilt and illumination correction, robust similarity measure and regularization,” Biomed. Opt. Express 5, 2591–2613 (2014).
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J. Xu, H. Ishikawa, G. Wollstein, L. Kagemann, and J. S. Schuman, “Alignment of 3-d optical coherence tomography scans to correct eye movement using a particle filtering,” IEEE Trans. Med. Imaging 31, 1337–1345 (2012).
[Crossref] [PubMed]

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Macular segmentation with optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 46, 2012 (2005).
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S. Ricco, M. Chen, H. Ishikawa, G. Wollstein, and J. Schuman, “Correcting motion artifacts in retinal spectral domain optical coherence tomography via image registration,” in “Medical Image Computing and Computer-Assisted Intervention,” , vol. 5761 of Lecture Notes in Computer Science G.-Z. Yang, D. Hawkes, D. Rueckert, A. Noble, and C. Taylor, eds. (SpringerBerlin Heidelberg, 2009), pp. 100–107.

Wu, J.

A. Montuoro, J. Wu, S. Waldstein, B. Gerendas, G. Langs, C. Simader, and U. Schmidt-Erfurth, “Motion artefact correction in retinal optical coherence tomography using local symmetry,” in “Medical Image Computing and Computer-Assisted Intervention,” (Springer, 2014), pp. 130–137.

H. He, G. Liu, P. Mo, B. Li, J. Wu, and X. Ding, “Correction of motion artifact in 3d retinal optical coherence tomography imaging,” in “International Congress on Image and Signal Processing,” , vol. 1 (IEEE, 2013), vol. 1, pp. 261–265.

Wu, X.

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-d intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
[Crossref] [PubMed]

Xu, J.

J. Xu, H. Ishikawa, G. Wollstein, L. Kagemann, and J. S. Schuman, “Alignment of 3-d optical coherence tomography scans to correct eye movement using a particle filtering,” IEEE Trans. Med. Imaging 31, 1337–1345 (2012).
[Crossref] [PubMed]

Yamanari, M.

Yao, W.

Y. Gan, W. Yao, K. M. Myers, and C. P. Hendon, “An automated 3d registration method for optical coherence tomography volumes,” in “IEEE International Conference in Engineering in Medicine and Biology Society,” (IEEE, 2014), pp. 3873–3876.

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Yatagai, T.

Yu, Z.

A. Baghaie, Z. Yu, and R. M. D’Souza, “State-of-the-art in retinal optical coherence tomography image analysis,” Quant. Imaging Med. Surg. 5, 603 (2015).
[PubMed]

Yuen, J.

C. Liu, J. Yuen, and A. Torralba, “Sift flow: Dense correspondence across scenes and its applications,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 978–994 (2011).
[Crossref]

Yun, S.

Zang, P.

P. Zang, G. Liu, M. Zhang, C. Dongye, J. Wang, A. D. Pechauer, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and et al., “Automated motion correction using parallel-strip registration for wide-field en face OCT angiogram,” Biomedical Optics Express 7, 2823–2836 (2016).
[Crossref] [PubMed]

Zawadzki, R. J.

I. Gorczynska, J. V. Migacz, R. J. Zawadzki, A. G. Capps, and J. S. Werner, “Comparison of amplitude-decorrelation, speckle-variance and phase-variance oct angiography methods for imaging the human retina and choroid,” Biomed. Opt. Express 7, 911–942 (2016).
[Crossref] [PubMed]

R. J. Zawadzki, A. R. Fuller, S. S. Choi, D. F. Wiley, B. Hamann, and J. S. Werner, “Correction of motion artifacts and scanning beam distortions in 3d ophthalmic optical coherence tomography imaging,” in “Biomedical Optics (BiOS) 2007,” (International Society for Optics and Photonics, 2007), pp. 642607.

Zhang, M.

P. Zang, G. Liu, M. Zhang, C. Dongye, J. Wang, A. D. Pechauer, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and et al., “Automated motion correction using parallel-strip registration for wide-field en face OCT angiogram,” Biomedical Optics Express 7, 2823–2836 (2016).
[Crossref] [PubMed]

Biomed. Opt. Express (8)

B. Antony, M. D. Abramoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-d method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. Opt. Express 2, 2403–2416 (2011).
[Crossref] [PubMed]

M. F. Kraus, B. Potsaid, M. A. Mayer, R. Bock, B. Baumann, J. J. Liu, J. Hornegger, and J. G. Fujimoto, “Motion correction in optical coherence tomography volumes on a per a-scan basis using orthogonal scan patterns,” Biomed. Opt. Express 3, 1182–1199 (2012).
[Crossref] [PubMed]

R. P. McNabb, F. LaRocca, S. Farsiu, A. N. Kuo, and J. A. Izatt, “Distributed scanning volumetric SDOCT for motion corrected corneal biometry,” Biomed. Opt. Express 3, 2050–2065 (2012).
[Crossref] [PubMed]

H. C. Hendargo, R. Estrada, S. J. Chiu, C. Tomasi, S. Farsiu, and J. A. Izatt, “Automated non-rigid registration and mosaicing for robust imaging of distinct retinal capillary beds using speckle variance optical coherence tomography,” Biomed. Opt. Express 4, 803–821 (2013).
[Crossref] [PubMed]

F. LaRocca, D. Nankivil, S. Farsiu, and J. A. Izatt, “Handheld simultaneous scanning laser ophthalmoscopy and optical coherence tomography system,” Biomed. Opt. Express 4, 2307–2321 (2013).
[Crossref] [PubMed]

M. F. Kraus, J. J. Liu, J. Schottenhamml, C.-L. Chen, A. Budai, L. Branchini, T. Ko, H. Ishikawa, G. Wollstein, J. Schuman, and et al., “Quantitative 3D-OCT motion correction with tilt and illumination correction, robust similarity measure and regularization,” Biomed. Opt. Express 5, 2591–2613 (2014).
[Crossref] [PubMed]

S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6, 1172–1194 (2015).
[Crossref] [PubMed]

I. Gorczynska, J. V. Migacz, R. J. Zawadzki, A. G. Capps, and J. S. Werner, “Comparison of amplitude-decorrelation, speckle-variance and phase-variance oct angiography methods for imaging the human retina and choroid,” Biomed. Opt. Express 7, 911–942 (2016).
[Crossref] [PubMed]

Biomedical Optics Express (1)

P. Zang, G. Liu, M. Zhang, C. Dongye, J. Wang, A. D. Pechauer, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and et al., “Automated motion correction using parallel-strip registration for wide-field en face OCT angiogram,” Biomedical Optics Express 7, 2823–2836 (2016).
[Crossref] [PubMed]

Br. J. Ophthalmol. (1)

R. P. McNabb, D. S. Grewal, R. Mehta, S. G. Schuman, J. A. Izatt, T. H. Mahmoud, G. J. Jaffe, P. Mruthyunjaya, and A. N. Kuo, “Wide field of view swept-source optical coherence tomography for peripheral retinal disease,” Br. J. Ophthalmol. 2015307480 (2016).
[Crossref] [PubMed]

IEEE Trans. Med. Imaging (2)

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-d intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
[Crossref] [PubMed]

J. Xu, H. Ishikawa, G. Wollstein, L. Kagemann, and J. S. Schuman, “Alignment of 3-d optical coherence tomography scans to correct eye movement using a particle filtering,” IEEE Trans. Med. Imaging 31, 1337–1345 (2012).
[Crossref] [PubMed]

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

C. Liu, J. Yuen, and A. Torralba, “Sift flow: Dense correspondence across scenes and its applications,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 978–994 (2011).
[Crossref]

Invest. Ophthalmol. Vis. Sci. (2)

X. Song, R. Estrada, S. J. Chiu, A.-H. Dhalla, C. A. Toth, J. A. Izatt, and S. Farsiu, “Segmentation-based registration of retinal optical coherence tomography images with pathology,” Invest. Ophthalmol. Vis. Sci. 52, 1309 (2011).

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Macular segmentation with optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 46, 2012 (2005).
[Crossref] [PubMed]

J. Cataract Refract. Surg. (1)

M. Tang, A. Chen, Y. Li, and D. Huang, “Corneal power measurement with fourier-domain optical coherence tomography,” J. Cataract Refract. Surg. 36, 2115–2122 (2010).
[Crossref] [PubMed]

J. Exp. Psychol. (1)

F. Ratliff and L. A. Riggs, “Involuntary motions of the eye during monocular fixation,” J. Exp. Psychol. 40, 687 (1950).
[Crossref] [PubMed]

Journal of Biomedical Optics (1)

B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” Journal of Biomedical Optics 21, 076015 (2016).
[Crossref]

Ophthalmology (1)

R. P. McNabb, S. Farsiu, S. S. Stinnett, J. A. Izatt, and A. N. Kuo, “Optical coherence tomography accurately measures corneal power change from laser refractive surgery,” Ophthalmology 122, 677–686 (2015).
[Crossref]

Opt. Express (4)

Opt. Lett. (3)

Optica (1)

Optical Coherence Tomography: Technology and Applications (1)

M. F. Kraus and J. Hornegger, “OCT motion correction,” Optical Coherence Tomography: Technology and Applications 5459–476 (2015).
[Crossref]

Pattern Recognit. Lett. (1)

P. Soille, “Generalized geodesy via geodesic time,” Pattern Recognit. Lett. 15, 1235–1240 (1994).
[Crossref]

Quant. Imaging Med. Surg. (1)

A. Baghaie, Z. Yu, and R. M. D’Souza, “State-of-the-art in retinal optical coherence tomography image analysis,” Quant. Imaging Med. Surg. 5, 603 (2015).
[PubMed]

Science (1)

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Super-Resolution Imaging (1)

M. D. Robinson, S. J. Chiu, J. Lo, C. Toth, J. Izatt, and S. Farsiu, “New applications of super-resolution in medical imaging,” Super-Resolution Imaging 2010384–412 (2010).

Other (10)

Y.-J. Hong, Y. Chen, E. Li, M. Miura, S. Makita, and Y. Yasuno, “Eye motion corrected oct imaging with lissajous scan pattern,” in “Proc. SPIE,” (International Society for Optics and Photonics, 2016), pp. 96930P.

J. S. Schuman, C. A. Puliafito, J. G. Fujimoto, and J. S. Duker, Optical Coherence Tomography of Ocular Diseases (SlackNew Jersey, 2004).

A. Montuoro, J. Wu, S. Waldstein, B. Gerendas, G. Langs, C. Simader, and U. Schmidt-Erfurth, “Motion artefact correction in retinal optical coherence tomography using local symmetry,” in “Medical Image Computing and Computer-Assisted Intervention,” (Springer, 2014), pp. 130–137.

S. Ricco, M. Chen, H. Ishikawa, G. Wollstein, and J. Schuman, “Correcting motion artifacts in retinal spectral domain optical coherence tomography via image registration,” in “Medical Image Computing and Computer-Assisted Intervention,” , vol. 5761 of Lecture Notes in Computer Science G.-Z. Yang, D. Hawkes, D. Rueckert, A. Noble, and C. Taylor, eds. (SpringerBerlin Heidelberg, 2009), pp. 100–107.

R. J. Zawadzki, A. R. Fuller, S. S. Choi, D. F. Wiley, B. Hamann, and J. S. Werner, “Correction of motion artifacts and scanning beam distortions in 3d ophthalmic optical coherence tomography imaging,” in “Biomedical Optics (BiOS) 2007,” (International Society for Optics and Photonics, 2007), pp. 642607.

H. He, G. Liu, P. Mo, B. Li, J. Wu, and X. Ding, “Correction of motion artifact in 3d retinal optical coherence tomography imaging,” in “International Congress on Image and Signal Processing,” , vol. 1 (IEEE, 2013), vol. 1, pp. 261–265.

Y. Gan, W. Yao, K. M. Myers, and C. P. Hendon, “An automated 3d registration method for optical coherence tomography volumes,” in “IEEE International Conference in Engineering in Medicine and Biology Society,” (IEEE, 2014), pp. 3873–3876.

MathWorks Inc., “Intensity-based automatic image registration,” http://www.mathworks.com/help/images/intensity-based-automatic-image-registration.html (2016).

B. K. Horn and B. G. Schunck, “Determining optical flow,” in “1981 Technical symposium East,” (International Society for Optics and Photonics, 1981), pp. 319–331.

A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” in “IEEE Computer Society Conference on Computer Vision and Pattern Recognition,” , vol. 2 (IEEE, 2005), vol. 2, pp. 60–65.

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Figures (14)

Fig. 1
Fig. 1

Motion artifacts in volumetric OCT imaging of the human retina. (a) Example of a volumetric OCT scan. The B-scan in the fast scan direction shows smooth retinal layers without visible motion artifacts (X-fast scan). The wavy retinal layers in the slow (Y) scan direction is the result of uncompensated motion of the subject’s eye. (b) Example summed voxel projection (SVP) image (Z-direction) of the original volume. The red arrow indicates the clear location of a saccade. (c) SVP after motion compensation. The dark gap region in this image, an artifact of saccades, corresponds to a region not scanned by OCT.

Fig. 2
Fig. 2

Block diagram of the proposed method. The method is divided in three main blocks. A: retinal surface segmentation, performed in each B-scan separately. B: lateral registration, including saccade detection and correction, and local dense registration using an orientation-aware version of optical flow. C: axial registration, obtained by solving a linear model on the segmented surfaces, equivalent to applying a tilt and an offset to each B-scan.

Fig. 3
Fig. 3

Retinal boundary segmentation. (a) B-scan image (denoised with non-local means [34]). (b) Background pixels binary mask (green=1, blue=0). (c) Normalized square of the gradient magnitude (red is highest, blue is lowest). (d) Resulting segmentation. Red: retinal boundary. Blue: IS/OS layer.

Fig. 4
Fig. 4

Saccade detection based on SVP analysis. (a) & (d): SVP image of the X-fast and (rotated) Y-fast volumes of one subject (Section 2.2). Red arrows indicate saccades. (b) & (e): horizontal gradient of (a) & (d). (c) & (f): Green curve: negative of the correlation between lines (normalized). Blue curve: green curve minus a local average. Peaks in the blue curve determine the location of saccades.

Fig. 5
Fig. 5

Saccades detection and lateral eye motion correction for three example volumes. (a), (e), (c) & (d): original X-fast and Y-fast SVP of two different subjects; red arrows indicate locations of detected saccades. Each SVP image is divided into tiles at the locations of the saccades and the tiles true location and rotation are found using the orthogonal counterpart. (b), (f), (d) & (h): SVP images after correction; green, orange and red arrows indicate successful, partially successful, and unsuccessful corrections, respectively. Notice also how occlusions are revealed as dark gaps between the tiles.

Fig. 6
Fig. 6

Vessel likelihood maps. (a) & (c): original SVP images of the inner retina of two different subjects. (b) & (d): vessel likelihood maps obtained using the Gabor filtering process of [10]. These are used during lateral registration to weigh the regions of the image according to the presence of vasculature.

Fig. 7
Fig. 7

Orientation-aware dense registration. (a) Original overlay between orthogonal SVPs in a cropped region of (d). Green colored vessels correspond to the X-fast SVP and pink to Y-fast. Blue arrows point to regions where displacements need to be corrected. (b) Overlay after horizontal dense motion correction. Blue arrows indicate regions where vertical corrections are required. (c) Overlay after vertical dense motion correction. The two images now match. (d) Resulting overlay of entire orthogonal SVPs. The red box indicates the location of the cropped region in (a)–(c). (e) Flow vectors from (a) to (b) (horizontal motion on X-fast SVP). Note that the orientation of the arrows are those of the original B-scans, so no information is exchanged between B-scans. (f) Flow vectors from (b) to (c) (vertical motion on Y-fast SVP). The resolution of the flow grids has been reduced for improved visualization.

Fig. 8
Fig. 8

Axial registration. (a) Original segmented surface in the X-fast volumes. (b) Linear distortion found by solving (13). The distortions consist of linear displacements of each B-scan (rescaled with respect to (a) for improved visualization). (c) Resulting retinal surface after subtracting (b) from (a). (d) Original segmented surface in the Y-fast volumes. (e) Linear distortions found for the Y-fast volume (rescaled for improved visualization). (f) Resulting retinal surface after subtracting (e) from (d). As a result, the motion in the slow scan direction has been corrected on both volumes, producing matching smooth surfaces.

Fig. 9
Fig. 9

Results of the lateral registration for three test subjects, A, B and C. The SVP images were computed as described in Section 2.2.6. For each subject, the top and bottom rows show the X-fast and Y-fast SVP images, and their overlay before and after registration, respectively. In the overlay images, the X-fast image is shown in green and the Y-fast in pink. Note the presence of strong saccades in the original images, and the originally poor overlap of the vessels between them. After registration, the saccades have been detected and the rigid motion between them has been corrected for, producing an accurate overlap of the vasculature. Locations where no data was acquired are shown in black. The yellow and cyan lines indicate the locations of the cross-sectional images shown in Fig. 10.

Fig. 10
Fig. 10

Result of the axial registration step for the three subjects of Figure 9. For each subject, we show cross-sectional images along the X-direction (two columns on the left) and Y directions (two columns on the right). The locations of the X and Y slices are shown with yellow and cyan lines in Fig. 9, respectively. Before axial registration, the X-fast volume presents strong motion artifacts along the Y direction, and the Y-fast volume is corrupted along the X-direction. After registration, the smooth shape of the retina is recovered for the slow-scan directions, whereas for the fast-scan directions, only a tilt and an offset of the image is performed. The black regions correspond to the missing data gaps that have been revealed after lateral registration.

Fig. 11
Fig. 11

Examples of shortcomings of our algorithm. Because of the extreme number of saccades, the regions between saccades are too narrow and their overlap contains too little information to achieve a successful registration. From the left, the first two columns show the original SVP images for the X-fast and Y-fast volumes of two test subjects. The third column shows the overlay of the registered images. Green corresponds to the X-fast image and pink to the Y-fast. The rightmost column shows the result of filling the gaps in the Y-fast image with the corresponding pixel values from the X-fast image. Small regions where no data has been acquired in any of the volumes appear in black.

Fig. 12
Fig. 12

Additional results of the axial registration step. The column on the left shows B-scan images for subjects G and F. The second column shows a cross-sectional image of the orthogonal volume in the same location, showing strong axial motion artifacts. The third column shows the overlay image of the registered images. Green corresponds to X-fast and pink to Y-fast. The rightmost column shows the result of registering the target with respect to the reference, plus filling the missing data by copying the pixel values from the reference. Note that the reference image (B-scan) was only rigidly transformed, but the shape was accurately recovered in the motion-corrupted image.

Fig. 13
Fig. 13

3D rendering of the OCT volume of Subject B (Figs. 9 and 10) before and after motion correction. Left: original X-fast volume, presenting strong motion artifacts. Right merging of the registered and motion-corrected X-fast and Y-fast volumes.

Fig. 14
Fig. 14

Result of the algorithm on volumes with simulated motion corruption. (a) & (c): SVPs of two motion-free ground truth volumes. (a) corresponds to the X-fast volume of one subject and (c) to the Y-fast of a different subject. The red lines indicate the location of the cross-sectional images. (b) & (d): Motion-free cross-sectional images of the ground truth volumes. (e) & (g): SVPs of simulated motion corrupted volumes. The simulated saccades are indicated with red arrows. (f) & (h): Cross-sectional images of simulated volumes, showing motion corruption along the slow-scan direction. (i) to (l): Corrected versions of (e)–(h) using the proposed algorithm. Note how the simulated motion has been corrected, producing images that match the ground truth.

Tables (3)

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Table 1 Quantitative performance evaluation of the volumetric registration on 24 test volumes. For the RMSE calculation, pixel values are assumed to be between 0 and 255.

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Table 2 Quantitative performance evaluation of the lateral registration on 24 test volumes.

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Table 3 Average registration performance of motion simulation/correction.

Equations (13)

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I ( x , y ) = z = z 1 z 2 V ( x , y , z ) z 2 z 1 z = z 2 z 3 V ( x , y , z ) z 3 z 2 ,
r i , i + 1 = j = 1 W ( I ( j , i ) m i s i ) ( I ( j , i + 1 ) m i + 1 s i + 1 ) W ,
r = r r * 1 λ / λ ,
A R = N R + β p R l R ( p ) ,
I X ( x , y ) = I ( x + u ( y ) , y ) ,
I Y ( x , y ) = I ( x , y + v ( x ) ) .
h ^ X ( x , y ) = h ( x , y ) + m X ( y ) x + n X ( y ) ,
h ^ Y ( x , y ) = h ( x , y ) + m Y ( x ) y + n Y ( x ) ,
m X ( y ) x + n X ( y ) m Y ( x ) y n Y ( x ) = h ^ X ( x , y ) h ^ Y ( x , y ) .
h ^ X ( x , y ) = h ( x , y ) + m X i ( y θ i ) x θ i + n X i ( y θ i )
h ^ Y ( x , y ) = h ( x , y ) + m Y j ( x θ j ) y θ j + n Y j ( x θ j )
x θ = x cos ( θ ) + y sin ( θ ) and y θ = y cos ( θ ) x sin ( θ ) .
m X i ( y θ i ) x θ i + n X i ( y θ i ) m Y j ( x θ j ) y θ j n Y j ( x θ j ) = h ^ X ( x , y ) h ^ Y ( x , y ) .

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