Abstract

Multi-modal retinal image registration is often required to utilize the complementary information from different retinal imaging modalities. However, a robust and accurate registration is still a challenge due to the modality-varied resolution, contrast, and luminosity. In this paper, a two step registration method is proposed to address this problem. Descriptor matching on mean phase images is used to globally register images in the first step. Deformable registration based on modality independent neighbourhood descriptor (MIND) method is followed to locally refine the registration result in the second step. The proposed method is extensively evaluated on color fundus images and scanning laser ophthalmoscope (SLO) images. Both qualitative and quantitative tests demonstrate improved registration using the proposed method compared to the state-of-the-art. The proposed method produces significantly and substantially larger mean Dice coefficients compared to other methods (p<0.001). It may facilitate the measurement of corresponding features from different retinal images, which can aid in assessing certain retinal diseases.

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

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References

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2016 (4)

Z. Li, D. Mahapatra, J. A. Tielbeek, J. Stoker, L. J. van Vliet, and F. M. Vos, “Image registration based on autocorrelation of local structure,” IEEE Trans. Med. Imag. 35(1), 63–75, (2016).
[Crossref]

J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. Pluim, R. Duits, and B. M. ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imag. 35(12), 2631–2644 (2016).
[Crossref]

K. Adal, P. van Etten, J. P. Martinez, K. Rouwen, L. J. van Vliet, and K. A. Vermeer, “Automated detection and classification of longitudinal retinal changes due to microaneurysms for diabetic retinopathy screening,” Invest. Ophthalmol. Vis. Sci. 57(12), 3403 (2016).

M. S. Miri, M. D. Abràmoff, Y. H. Kwon, and M. K. Garvin, “Multimodal registration of SD-OCT volumes and fundus photographs using histograms of oriented gradients,” Biomed. Opt. Express 7(12), 5252–5267 (2016).
[Crossref] [PubMed]

2014 (2)

T. MacGillivray, E. Trucco, J. Cameron, B. Dhillon, J. Houston, and E. Van Beek, “Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions,” Br. J. Radiol. 87(1040), 20130832 (2014).
[Crossref] [PubMed]

B. I. Gramatikov, “Modern technologies for retinal scanning and imaging: an introduction for the biomedical engineer,” Biomed. Eng. Online 13(1), 52 (2014).
[Crossref] [PubMed]

2013 (5)

P. A. Legg, P. L. Rosin, D. Marshall, and J. E. Morgan, “Improving accuracy and efficiency of mutual information for multi-modal retinal image registration using adaptive probability density estimation,” Comput. Med. Imaging Graph. 37(7), 597–606 (2013).
[Crossref] [PubMed]

A. Cifor, L. Risser, D. Chung, E. M. Anderson, and J. A. Schnabel, “Hybrid feature-based diffeomorphic registration for tumor tracking in 2-d liver ultrasound images,” IEEE Trans. Med. Imag. 32(9), 1647–1656 (2013).
[Crossref]

R. Castillo, E. Castillo, D. Fuentes, M. Ahmad, A. M. Wood, M. S. Ludwig, and T. Guerrero, “A reference dataset for deformable image registration spatial accuracy evaluation using the COPD gene study archive,” Phys. Med. Biol. 58(9), 2861 (2013).
[Crossref] [PubMed]

M. Golabbakhsh and H. Rabbani, “Vessel-based registration of fundus and optical coherence tomography projection images of retina using a quadratic registration model,” IET Image Process. 7(8), 768–776 (2013).
[Crossref]

A. Sotiras, C. Davatzikos, and N. Paragios, “Deformable medical image registration: A survey,” IEEE Trans. Med. Imag. 32(7), 1153–1190 (2013).
[Crossref]

2012 (2)

L. S. Lim, P. Mitchell, J. M. Seddon, F. G. Holz, and T. Y. Wong, “Age-related macular degeneration,” Lancet 379(9827), 1728–1738, (2012).
[Crossref] [PubMed]

M. P. Heinrich, M. Jenkinson, M. Bhushan, T. Matin, F. V. Gleeson, M. Brady, and J. A. Schnabel, “Mind: Modality independent neighbourhood descriptor for multi-modal deformable registration,” Med. Image Anal. 16(7), 1423–1435 (2012).
[Crossref] [PubMed]

2011 (2)

T. Brox and J. Malik, “Large displacement optical flow: descriptor matching in variational motion estimation,” IEEE Trans. Pattern Anal. Mach. Intell. 33(3), 500–513 (2011).
[Crossref]

Y. Li, G. Gregori, R.W. Knighton, B.J. Lujan, and P.J. Rosenfeld, “Registration of OCT fundus images with color fundus photographs based on blood vessel ridges,” Opt. Express 19(1), 7–16 (2011).
[Crossref] [PubMed]

2010 (2)

A. Wong, D. A. Clausi, and P. Fieguth, “CPOL: Complex phase order likelihood as a similarity measure for MR–CT registration,” Med. Image Anal. 14(1), 50–57 (2010).
[Crossref]

M. D. Abràmoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[Crossref] [PubMed]

2008 (5)

R. Kolar, L. Kubecka, and J. Jan, “Registration and fusion of the autofluorescent and infrared retinal images,” Int. J. Biomed. Imaging 2008, 513478 (2008).
[Crossref] [PubMed]

A. Andronache, M. von Siebenthal, G. Székely, and P. Cattin, “Non-rigid registration of multi-modal images using both mutual information and cross-correlation,” Med. Image Anal. 12(1), 3–15 (2008).
[Crossref]

H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (surf),” Comput. Vis. Image Und. 110(3), 346–359 (2008).
[Crossref]

T. Y. Wong, N. Cheung, W. T. Tay, J. J. Wang, T. Aung, S. M. Saw, S. C. Lim, E. S. Tai, and P. Mitchell, “Prevalence and risk factors for diabetic retinopathy: the singapore malay eye study,” Ophthalmology 115(11), 1869–1875 (2008).
[Crossref] [PubMed]

N. Amerasinghe, T. Aung, N. Cheung, C. W. Fong, J. J. Wang, P. Mitchell, S.-M. Saw, and T. Y. Wong, “Evidence of retinal vascular narrowing in glaucomatous eyes in an asian population,” Invest. Ophthalmol. Vis. Sci. 49(12), 5397–5402 (2008).
[Crossref] [PubMed]

2007 (1)

J. Xu, O. Chutatape, E. Sung, C. Zheng, and P. C. T. Kuan, “Optic disc feature extraction via modified deformable model technique for glaucoma analysis,” Pattern Recogn. 40(7), 2063–2076 (2007).
[Crossref]

2004 (2)

H. Li and O. Chutatape, “Automated feature extraction in color retinal images by a model based approach,” IEEE Trans. Biomed. Eng. 51(2), 246–254 (2004).
[Crossref] [PubMed]

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60(2), 91–110, (2004).
[Crossref]

2003 (1)

C. V. Stewart, C.-L. Tsai, and B. Roysam, “The dual-bootstrap iterative closest point algorithm with application to retinal image registration,” IEEE Trans. Med. Imag. 22(11), 1379–1394, (2003).
[Crossref]

2002 (2)

T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987, (2002).
[Crossref]

A. Can, C. V. Stewart, B. Roysam, and H. L. Tanenbaum, “A feature-based technique for joint, linear estimation of high-order image-to-mosaic transformations: mosaicing the curved human retina,” IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 412–419 (2002).
[Crossref]

2001 (1)

M. Felsberg and G. Sommer, “The monogenic signal,” IEEE Trans. Signal Process. 49(12), 3136–3144 (2001).
[Crossref]

1999 (1)

N. Ritter, R. Owens, J. Cooper, R. H. Eikelboom, and P. P. Van Saarloos, “Registration of stereo and temporal images of the retina,” IEEE Trans. Med. Imag. 18(5), 404–418, (1999).
[Crossref]

1995 (1)

A. V. Cideciyan, “Registration of ocular fundus images: an algorithm using cross-correlation of triple invariant image descriptors,” IEEE Eng. Med. Biol. Mag. 14(1), 52–58 (1995).
[Crossref]

1987 (1)

Abràmoff, M. D.

Adal, K.

K. Adal, P. van Etten, J. P. Martinez, K. Rouwen, L. J. van Vliet, and K. A. Vermeer, “Automated detection and classification of longitudinal retinal changes due to microaneurysms for diabetic retinopathy screening,” Invest. Ophthalmol. Vis. Sci. 57(12), 3403 (2016).

Ahmad, M.

R. Castillo, E. Castillo, D. Fuentes, M. Ahmad, A. M. Wood, M. S. Ludwig, and T. Guerrero, “A reference dataset for deformable image registration spatial accuracy evaluation using the COPD gene study archive,” Phys. Med. Biol. 58(9), 2861 (2013).
[Crossref] [PubMed]

Amerasinghe, N.

N. Amerasinghe, T. Aung, N. Cheung, C. W. Fong, J. J. Wang, P. Mitchell, S.-M. Saw, and T. Y. Wong, “Evidence of retinal vascular narrowing in glaucomatous eyes in an asian population,” Invest. Ophthalmol. Vis. Sci. 49(12), 5397–5402 (2008).
[Crossref] [PubMed]

Anderson, E. M.

A. Cifor, L. Risser, D. Chung, E. M. Anderson, and J. A. Schnabel, “Hybrid feature-based diffeomorphic registration for tumor tracking in 2-d liver ultrasound images,” IEEE Trans. Med. Imag. 32(9), 1647–1656 (2013).
[Crossref]

Andronache, A.

A. Andronache, M. von Siebenthal, G. Székely, and P. Cattin, “Non-rigid registration of multi-modal images using both mutual information and cross-correlation,” Med. Image Anal. 12(1), 3–15 (2008).
[Crossref]

Aung, T.

N. Amerasinghe, T. Aung, N. Cheung, C. W. Fong, J. J. Wang, P. Mitchell, S.-M. Saw, and T. Y. Wong, “Evidence of retinal vascular narrowing in glaucomatous eyes in an asian population,” Invest. Ophthalmol. Vis. Sci. 49(12), 5397–5402 (2008).
[Crossref] [PubMed]

T. Y. Wong, N. Cheung, W. T. Tay, J. J. Wang, T. Aung, S. M. Saw, S. C. Lim, E. S. Tai, and P. Mitchell, “Prevalence and risk factors for diabetic retinopathy: the singapore malay eye study,” Ophthalmology 115(11), 1869–1875 (2008).
[Crossref] [PubMed]

Bay, H.

H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (surf),” Comput. Vis. Image Und. 110(3), 346–359 (2008).
[Crossref]

P. Cattin, H. Bay, L. Van Gool, and G. Székely, “Retina mosaicing using local features,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, Berlin, Heidelberg. (2006)) pp. 185–192.

Bekkers, E.

J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. Pluim, R. Duits, and B. M. ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imag. 35(12), 2631–2644 (2016).
[Crossref]

Bhushan, M.

M. P. Heinrich, M. Jenkinson, M. Bhushan, T. Matin, F. V. Gleeson, M. Brady, and J. A. Schnabel, “Mind: Modality independent neighbourhood descriptor for multi-modal deformable registration,” Med. Image Anal. 16(7), 1423–1435 (2012).
[Crossref] [PubMed]

Brady, M.

M. P. Heinrich, M. Jenkinson, M. Bhushan, T. Matin, F. V. Gleeson, M. Brady, and J. A. Schnabel, “Mind: Modality independent neighbourhood descriptor for multi-modal deformable registration,” Med. Image Anal. 16(7), 1423–1435 (2012).
[Crossref] [PubMed]

Brox, T.

T. Brox and J. Malik, “Large displacement optical flow: descriptor matching in variational motion estimation,” IEEE Trans. Pattern Anal. Mach. Intell. 33(3), 500–513 (2011).
[Crossref]

Cameron, J.

T. MacGillivray, E. Trucco, J. Cameron, B. Dhillon, J. Houston, and E. Van Beek, “Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions,” Br. J. Radiol. 87(1040), 20130832 (2014).
[Crossref] [PubMed]

Can, A.

A. Can, C. V. Stewart, B. Roysam, and H. L. Tanenbaum, “A feature-based technique for joint, linear estimation of high-order image-to-mosaic transformations: mosaicing the curved human retina,” IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 412–419 (2002).
[Crossref]

Castillo, E.

R. Castillo, E. Castillo, D. Fuentes, M. Ahmad, A. M. Wood, M. S. Ludwig, and T. Guerrero, “A reference dataset for deformable image registration spatial accuracy evaluation using the COPD gene study archive,” Phys. Med. Biol. 58(9), 2861 (2013).
[Crossref] [PubMed]

Castillo, R.

R. Castillo, E. Castillo, D. Fuentes, M. Ahmad, A. M. Wood, M. S. Ludwig, and T. Guerrero, “A reference dataset for deformable image registration spatial accuracy evaluation using the COPD gene study archive,” Phys. Med. Biol. 58(9), 2861 (2013).
[Crossref] [PubMed]

Cattin, P.

A. Andronache, M. von Siebenthal, G. Székely, and P. Cattin, “Non-rigid registration of multi-modal images using both mutual information and cross-correlation,” Med. Image Anal. 12(1), 3–15 (2008).
[Crossref]

P. Cattin, H. Bay, L. Van Gool, and G. Székely, “Retina mosaicing using local features,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, Berlin, Heidelberg. (2006)) pp. 185–192.

Chen, G.

K. Zhang, E. Zhang, J. Li, and G. Chen, “Retinal image automatic registration based on local bifurcation structure,” In Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), International Congress on (IEEE, 2016), pp. 1418–1422.

Chen, J.

J. Ma, J. Jiang, J. Chen, C. Liu, and C. Li, “Multimodal retinal image registration using edge map and feature guided Gaussian mixture model,” in Visual Communications and Image Processing (VCIP), (IEEE, 2016), pp. 1–4.

Chen, L.

L. Chen, Y. Xiang, Y. Chen, and X. Zhang, “Retinal image registration using bifurcation structures,” in Image Processing (ICIP), 2011 18th IEEE International Conference on (IEEE, 2011), pp. 2169–2172.

Chen, Q.

S. Niu, Q. Chen, H. Shen, L. de Sisternes, and D.L. Rubin, “Registration of SD-OCT en-face images with color fundus photographs based on local patch matching,” In Proceedings of the Ophthalmic Medical Image Analysis First International Workshop (OMIA, 2014), pp. 25–32.

Chen, Y.

L. Chen, Y. Xiang, Y. Chen, and X. Zhang, “Retinal image registration using bifurcation structures,” in Image Processing (ICIP), 2011 18th IEEE International Conference on (IEEE, 2011), pp. 2169–2172.

Cheung, N.

T. Y. Wong, N. Cheung, W. T. Tay, J. J. Wang, T. Aung, S. M. Saw, S. C. Lim, E. S. Tai, and P. Mitchell, “Prevalence and risk factors for diabetic retinopathy: the singapore malay eye study,” Ophthalmology 115(11), 1869–1875 (2008).
[Crossref] [PubMed]

N. Amerasinghe, T. Aung, N. Cheung, C. W. Fong, J. J. Wang, P. Mitchell, S.-M. Saw, and T. Y. Wong, “Evidence of retinal vascular narrowing in glaucomatous eyes in an asian population,” Invest. Ophthalmol. Vis. Sci. 49(12), 5397–5402 (2008).
[Crossref] [PubMed]

Chung, D.

A. Cifor, L. Risser, D. Chung, E. M. Anderson, and J. A. Schnabel, “Hybrid feature-based diffeomorphic registration for tumor tracking in 2-d liver ultrasound images,” IEEE Trans. Med. Imag. 32(9), 1647–1656 (2013).
[Crossref]

Chutatape, O.

J. Xu, O. Chutatape, E. Sung, C. Zheng, and P. C. T. Kuan, “Optic disc feature extraction via modified deformable model technique for glaucoma analysis,” Pattern Recogn. 40(7), 2063–2076 (2007).
[Crossref]

H. Li and O. Chutatape, “Automated feature extraction in color retinal images by a model based approach,” IEEE Trans. Biomed. Eng. 51(2), 246–254 (2004).
[Crossref] [PubMed]

Cideciyan, A. V.

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J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. Pluim, R. Duits, and B. M. ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imag. 35(12), 2631–2644 (2016).
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Dhillon, B.

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M. P. Heinrich, M. Jenkinson, M. Bhushan, T. Matin, F. V. Gleeson, M. Brady, and J. A. Schnabel, “Mind: Modality independent neighbourhood descriptor for multi-modal deformable registration,” Med. Image Anal. 16(7), 1423–1435 (2012).
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L. S. Lim, P. Mitchell, J. M. Seddon, F. G. Holz, and T. Y. Wong, “Age-related macular degeneration,” Lancet 379(9827), 1728–1738, (2012).
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E. Shechtman and M. Irani, “Matching local self-similarities across images and videos,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.

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Kolar, R.

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J. Xu, O. Chutatape, E. Sung, C. Zheng, and P. C. T. Kuan, “Optic disc feature extraction via modified deformable model technique for glaucoma analysis,” Pattern Recogn. 40(7), 2063–2076 (2007).
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Li, Y.

Li, Z.

Z. Li, D. Mahapatra, J. A. Tielbeek, J. Stoker, L. J. van Vliet, and F. M. Vos, “Image registration based on autocorrelation of local structure,” IEEE Trans. Med. Imag. 35(1), 63–75, (2016).
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T. Y. Wong, N. Cheung, W. T. Tay, J. J. Wang, T. Aung, S. M. Saw, S. C. Lim, E. S. Tai, and P. Mitchell, “Prevalence and risk factors for diabetic retinopathy: the singapore malay eye study,” Ophthalmology 115(11), 1869–1875 (2008).
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J. Ma, J. Jiang, J. Chen, C. Liu, and C. Li, “Multimodal retinal image registration using edge map and feature guided Gaussian mixture model,” in Visual Communications and Image Processing (VCIP), (IEEE, 2016), pp. 1–4.

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Ma, J.

J. Ma, J. Jiang, J. Chen, C. Liu, and C. Li, “Multimodal retinal image registration using edge map and feature guided Gaussian mixture model,” in Visual Communications and Image Processing (VCIP), (IEEE, 2016), pp. 1–4.

MacGillivray, T.

T. MacGillivray, E. Trucco, J. Cameron, B. Dhillon, J. Houston, and E. Van Beek, “Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions,” Br. J. Radiol. 87(1040), 20130832 (2014).
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P. A. Legg, P. L. Rosin, D. Marshall, and J. E. Morgan, “Improving accuracy and efficiency of mutual information for multi-modal retinal image registration using adaptive probability density estimation,” Comput. Med. Imaging Graph. 37(7), 597–606 (2013).
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K. Adal, P. van Etten, J. P. Martinez, K. Rouwen, L. J. van Vliet, and K. A. Vermeer, “Automated detection and classification of longitudinal retinal changes due to microaneurysms for diabetic retinopathy screening,” Invest. Ophthalmol. Vis. Sci. 57(12), 3403 (2016).

Matin, T.

M. P. Heinrich, M. Jenkinson, M. Bhushan, T. Matin, F. V. Gleeson, M. Brady, and J. A. Schnabel, “Mind: Modality independent neighbourhood descriptor for multi-modal deformable registration,” Med. Image Anal. 16(7), 1423–1435 (2012).
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Mitchell, P.

L. S. Lim, P. Mitchell, J. M. Seddon, F. G. Holz, and T. Y. Wong, “Age-related macular degeneration,” Lancet 379(9827), 1728–1738, (2012).
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T. Y. Wong, N. Cheung, W. T. Tay, J. J. Wang, T. Aung, S. M. Saw, S. C. Lim, E. S. Tai, and P. Mitchell, “Prevalence and risk factors for diabetic retinopathy: the singapore malay eye study,” Ophthalmology 115(11), 1869–1875 (2008).
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P. A. Legg, P. L. Rosin, D. Marshall, and J. E. Morgan, “Improving accuracy and efficiency of mutual information for multi-modal retinal image registration using adaptive probability density estimation,” Comput. Med. Imaging Graph. 37(7), 597–606 (2013).
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T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987, (2002).
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A. Sotiras, C. Davatzikos, and N. Paragios, “Deformable medical image registration: A survey,” IEEE Trans. Med. Imag. 32(7), 1153–1190 (2013).
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T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987, (2002).
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J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. Pluim, R. Duits, and B. M. ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imag. 35(12), 2631–2644 (2016).
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M. Golabbakhsh and H. Rabbani, “Vessel-based registration of fundus and optical coherence tomography projection images of retina using a quadratic registration model,” IET Image Process. 7(8), 768–776 (2013).
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A. Cifor, L. Risser, D. Chung, E. M. Anderson, and J. A. Schnabel, “Hybrid feature-based diffeomorphic registration for tumor tracking in 2-d liver ultrasound images,” IEEE Trans. Med. Imag. 32(9), 1647–1656 (2013).
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N. Ritter, R. Owens, J. Cooper, R. H. Eikelboom, and P. P. Van Saarloos, “Registration of stereo and temporal images of the retina,” IEEE Trans. Med. Imag. 18(5), 404–418, (1999).
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Romeny, B. M. ter Haar

J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. Pluim, R. Duits, and B. M. ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imag. 35(12), 2631–2644 (2016).
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Rosin, P. L.

P. A. Legg, P. L. Rosin, D. Marshall, and J. E. Morgan, “Improving accuracy and efficiency of mutual information for multi-modal retinal image registration using adaptive probability density estimation,” Comput. Med. Imaging Graph. 37(7), 597–606 (2013).
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K. Adal, P. van Etten, J. P. Martinez, K. Rouwen, L. J. van Vliet, and K. A. Vermeer, “Automated detection and classification of longitudinal retinal changes due to microaneurysms for diabetic retinopathy screening,” Invest. Ophthalmol. Vis. Sci. 57(12), 3403 (2016).

Roysam, B.

C. V. Stewart, C.-L. Tsai, and B. Roysam, “The dual-bootstrap iterative closest point algorithm with application to retinal image registration,” IEEE Trans. Med. Imag. 22(11), 1379–1394, (2003).
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A. Can, C. V. Stewart, B. Roysam, and H. L. Tanenbaum, “A feature-based technique for joint, linear estimation of high-order image-to-mosaic transformations: mosaicing the curved human retina,” IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 412–419 (2002).
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S. Niu, Q. Chen, H. Shen, L. de Sisternes, and D.L. Rubin, “Registration of SD-OCT en-face images with color fundus photographs based on local patch matching,” In Proceedings of the Ophthalmic Medical Image Analysis First International Workshop (OMIA, 2014), pp. 25–32.

Saw, S. M.

T. Y. Wong, N. Cheung, W. T. Tay, J. J. Wang, T. Aung, S. M. Saw, S. C. Lim, E. S. Tai, and P. Mitchell, “Prevalence and risk factors for diabetic retinopathy: the singapore malay eye study,” Ophthalmology 115(11), 1869–1875 (2008).
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Saw, S.-M.

N. Amerasinghe, T. Aung, N. Cheung, C. W. Fong, J. J. Wang, P. Mitchell, S.-M. Saw, and T. Y. Wong, “Evidence of retinal vascular narrowing in glaucomatous eyes in an asian population,” Invest. Ophthalmol. Vis. Sci. 49(12), 5397–5402 (2008).
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A. Cifor, L. Risser, D. Chung, E. M. Anderson, and J. A. Schnabel, “Hybrid feature-based diffeomorphic registration for tumor tracking in 2-d liver ultrasound images,” IEEE Trans. Med. Imag. 32(9), 1647–1656 (2013).
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M. P. Heinrich, M. Jenkinson, M. Bhushan, T. Matin, F. V. Gleeson, M. Brady, and J. A. Schnabel, “Mind: Modality independent neighbourhood descriptor for multi-modal deformable registration,” Med. Image Anal. 16(7), 1423–1435 (2012).
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Seddon, J. M.

L. S. Lim, P. Mitchell, J. M. Seddon, F. G. Holz, and T. Y. Wong, “Age-related macular degeneration,” Lancet 379(9827), 1728–1738, (2012).
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G. Shakhnarovich, T. Darrell, and P. Indyk, Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing) (The MIT Press, 2006).

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E. Shechtman and M. Irani, “Matching local self-similarities across images and videos,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.

Shen, H.

S. Niu, Q. Chen, H. Shen, L. de Sisternes, and D.L. Rubin, “Registration of SD-OCT en-face images with color fundus photographs based on local patch matching,” In Proceedings of the Ophthalmic Medical Image Analysis First International Workshop (OMIA, 2014), pp. 25–32.

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M. Felsberg and G. Sommer, “The monogenic signal,” IEEE Trans. Signal Process. 49(12), 3136–3144 (2001).
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A. Sotiras, C. Davatzikos, and N. Paragios, “Deformable medical image registration: A survey,” IEEE Trans. Med. Imag. 32(7), 1153–1190 (2013).
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Stewart, C. V.

C. V. Stewart, C.-L. Tsai, and B. Roysam, “The dual-bootstrap iterative closest point algorithm with application to retinal image registration,” IEEE Trans. Med. Imag. 22(11), 1379–1394, (2003).
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A. Can, C. V. Stewart, B. Roysam, and H. L. Tanenbaum, “A feature-based technique for joint, linear estimation of high-order image-to-mosaic transformations: mosaicing the curved human retina,” IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 412–419 (2002).
[Crossref]

Stoker, J.

Z. Li, D. Mahapatra, J. A. Tielbeek, J. Stoker, L. J. van Vliet, and F. M. Vos, “Image registration based on autocorrelation of local structure,” IEEE Trans. Med. Imag. 35(1), 63–75, (2016).
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Sung, E.

J. Xu, O. Chutatape, E. Sung, C. Zheng, and P. C. T. Kuan, “Optic disc feature extraction via modified deformable model technique for glaucoma analysis,” Pattern Recogn. 40(7), 2063–2076 (2007).
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Tai, E. S.

T. Y. Wong, N. Cheung, W. T. Tay, J. J. Wang, T. Aung, S. M. Saw, S. C. Lim, E. S. Tai, and P. Mitchell, “Prevalence and risk factors for diabetic retinopathy: the singapore malay eye study,” Ophthalmology 115(11), 1869–1875 (2008).
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A. Can, C. V. Stewart, B. Roysam, and H. L. Tanenbaum, “A feature-based technique for joint, linear estimation of high-order image-to-mosaic transformations: mosaicing the curved human retina,” IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 412–419 (2002).
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Tay, W. T.

T. Y. Wong, N. Cheung, W. T. Tay, J. J. Wang, T. Aung, S. M. Saw, S. C. Lim, E. S. Tai, and P. Mitchell, “Prevalence and risk factors for diabetic retinopathy: the singapore malay eye study,” Ophthalmology 115(11), 1869–1875 (2008).
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Tielbeek, J. A.

Z. Li, D. Mahapatra, J. A. Tielbeek, J. Stoker, L. J. van Vliet, and F. M. Vos, “Image registration based on autocorrelation of local structure,” IEEE Trans. Med. Imag. 35(1), 63–75, (2016).
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N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005) 1, pp. 886–893.

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T. MacGillivray, E. Trucco, J. Cameron, B. Dhillon, J. Houston, and E. Van Beek, “Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions,” Br. J. Radiol. 87(1040), 20130832 (2014).
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Tsai, C.-L.

C. V. Stewart, C.-L. Tsai, and B. Roysam, “The dual-bootstrap iterative closest point algorithm with application to retinal image registration,” IEEE Trans. Med. Imag. 22(11), 1379–1394, (2003).
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H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (surf),” Comput. Vis. Image Und. 110(3), 346–359 (2008).
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T. MacGillivray, E. Trucco, J. Cameron, B. Dhillon, J. Houston, and E. Van Beek, “Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions,” Br. J. Radiol. 87(1040), 20130832 (2014).
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K. Adal, P. van Etten, J. P. Martinez, K. Rouwen, L. J. van Vliet, and K. A. Vermeer, “Automated detection and classification of longitudinal retinal changes due to microaneurysms for diabetic retinopathy screening,” Invest. Ophthalmol. Vis. Sci. 57(12), 3403 (2016).

Van Gool, L.

H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (surf),” Comput. Vis. Image Und. 110(3), 346–359 (2008).
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Van Saarloos, P. P.

N. Ritter, R. Owens, J. Cooper, R. H. Eikelboom, and P. P. Van Saarloos, “Registration of stereo and temporal images of the retina,” IEEE Trans. Med. Imag. 18(5), 404–418, (1999).
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K. Adal, P. van Etten, J. P. Martinez, K. Rouwen, L. J. van Vliet, and K. A. Vermeer, “Automated detection and classification of longitudinal retinal changes due to microaneurysms for diabetic retinopathy screening,” Invest. Ophthalmol. Vis. Sci. 57(12), 3403 (2016).

Vermeer, K. A.

K. Adal, P. van Etten, J. P. Martinez, K. Rouwen, L. J. van Vliet, and K. A. Vermeer, “Automated detection and classification of longitudinal retinal changes due to microaneurysms for diabetic retinopathy screening,” Invest. Ophthalmol. Vis. Sci. 57(12), 3403 (2016).

von Siebenthal, M.

A. Andronache, M. von Siebenthal, G. Székely, and P. Cattin, “Non-rigid registration of multi-modal images using both mutual information and cross-correlation,” Med. Image Anal. 12(1), 3–15 (2008).
[Crossref]

Vos, F. M.

Z. Li, D. Mahapatra, J. A. Tielbeek, J. Stoker, L. J. van Vliet, and F. M. Vos, “Image registration based on autocorrelation of local structure,” IEEE Trans. Med. Imag. 35(1), 63–75, (2016).
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Wang, J. J.

T. Y. Wong, N. Cheung, W. T. Tay, J. J. Wang, T. Aung, S. M. Saw, S. C. Lim, E. S. Tai, and P. Mitchell, “Prevalence and risk factors for diabetic retinopathy: the singapore malay eye study,” Ophthalmology 115(11), 1869–1875 (2008).
[Crossref] [PubMed]

N. Amerasinghe, T. Aung, N. Cheung, C. W. Fong, J. J. Wang, P. Mitchell, S.-M. Saw, and T. Y. Wong, “Evidence of retinal vascular narrowing in glaucomatous eyes in an asian population,” Invest. Ophthalmol. Vis. Sci. 49(12), 5397–5402 (2008).
[Crossref] [PubMed]

Webb, R. H.

Wong, A.

A. Wong, D. A. Clausi, and P. Fieguth, “CPOL: Complex phase order likelihood as a similarity measure for MR–CT registration,” Med. Image Anal. 14(1), 50–57 (2010).
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Wong, T. Y.

L. S. Lim, P. Mitchell, J. M. Seddon, F. G. Holz, and T. Y. Wong, “Age-related macular degeneration,” Lancet 379(9827), 1728–1738, (2012).
[Crossref] [PubMed]

T. Y. Wong, N. Cheung, W. T. Tay, J. J. Wang, T. Aung, S. M. Saw, S. C. Lim, E. S. Tai, and P. Mitchell, “Prevalence and risk factors for diabetic retinopathy: the singapore malay eye study,” Ophthalmology 115(11), 1869–1875 (2008).
[Crossref] [PubMed]

N. Amerasinghe, T. Aung, N. Cheung, C. W. Fong, J. J. Wang, P. Mitchell, S.-M. Saw, and T. Y. Wong, “Evidence of retinal vascular narrowing in glaucomatous eyes in an asian population,” Invest. Ophthalmol. Vis. Sci. 49(12), 5397–5402 (2008).
[Crossref] [PubMed]

Wood, A. M.

R. Castillo, E. Castillo, D. Fuentes, M. Ahmad, A. M. Wood, M. S. Ludwig, and T. Guerrero, “A reference dataset for deformable image registration spatial accuracy evaluation using the COPD gene study archive,” Phys. Med. Biol. 58(9), 2861 (2013).
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Xiang, Y.

L. Chen, Y. Xiang, Y. Chen, and X. Zhang, “Retinal image registration using bifurcation structures,” in Image Processing (ICIP), 2011 18th IEEE International Conference on (IEEE, 2011), pp. 2169–2172.

Xu, J.

J. Xu, O. Chutatape, E. Sung, C. Zheng, and P. C. T. Kuan, “Optic disc feature extraction via modified deformable model technique for glaucoma analysis,” Pattern Recogn. 40(7), 2063–2076 (2007).
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Yi, Z.

Z. Yi and S. Soatto, “Nonrigid registration combining global and local statistics,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2009), pp. 2200–2207.

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K. Zhang, E. Zhang, J. Li, and G. Chen, “Retinal image automatic registration based on local bifurcation structure,” In Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), International Congress on (IEEE, 2016), pp. 1418–1422.

Zhang, J.

J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. Pluim, R. Duits, and B. M. ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imag. 35(12), 2631–2644 (2016).
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Zhang, K.

K. Zhang, E. Zhang, J. Li, and G. Chen, “Retinal image automatic registration based on local bifurcation structure,” In Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), International Congress on (IEEE, 2016), pp. 1418–1422.

Zhang, X.

L. Chen, Y. Xiang, Y. Chen, and X. Zhang, “Retinal image registration using bifurcation structures,” in Image Processing (ICIP), 2011 18th IEEE International Conference on (IEEE, 2011), pp. 2169–2172.

Zheng, C.

J. Xu, O. Chutatape, E. Sung, C. Zheng, and P. C. T. Kuan, “Optic disc feature extraction via modified deformable model technique for glaucoma analysis,” Pattern Recogn. 40(7), 2063–2076 (2007).
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J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. Pluim, R. Duits, and B. M. ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imag. 35(12), 2631–2644 (2016).
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K. Adal, P. van Etten, J. P. Martinez, K. Rouwen, L. J. van Vliet, and K. A. Vermeer, “Automated detection and classification of longitudinal retinal changes due to microaneurysms for diabetic retinopathy screening,” Invest. Ophthalmol. Vis. Sci. 57(12), 3403 (2016).

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L. S. Lim, P. Mitchell, J. M. Seddon, F. G. Holz, and T. Y. Wong, “Age-related macular degeneration,” Lancet 379(9827), 1728–1738, (2012).
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A. Andronache, M. von Siebenthal, G. Székely, and P. Cattin, “Non-rigid registration of multi-modal images using both mutual information and cross-correlation,” Med. Image Anal. 12(1), 3–15 (2008).
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[Crossref]

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K. Zhang, E. Zhang, J. Li, and G. Chen, “Retinal image automatic registration based on local bifurcation structure,” In Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), International Congress on (IEEE, 2016), pp. 1418–1422.

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S. Niu, Q. Chen, H. Shen, L. de Sisternes, and D.L. Rubin, “Registration of SD-OCT en-face images with color fundus photographs based on local patch matching,” In Proceedings of the Ophthalmic Medical Image Analysis First International Workshop (OMIA, 2014), pp. 25–32.

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Figures (9)

Fig. 1
Fig. 1 The retina of the same subject acquired by (a) color fundus camera (Canon Cr-1 Mark II) and (b) Scanning Laser Ophthalmoscope (Spectralis HRA OCT). Retinal landmarks such as blood vessel and the optic disc have different representations on both modalities. (c) and (d) are mean phase images derived from (a) and (b), respectively.
Fig. 2
Fig. 2 Descriptor matching result between a color image (moving image) and an SLO image (fixed image, e.g. acquired by a Spectralis HRA OCT camera). (a) and (c) show matching pairs based on HOG and HOG-MP with the RANSAC process; (b) and (d) are the matching result using an affine transformation based on HOG and HOG-MP, respectively.
Fig. 3
Fig. 3 Five types of eye images need to be registered. The images of Spectralis fundus camera are the fixed image with size of 1536×1536 pixels; images from Canon, Topcon, EasyScan and Nidek are moving images (registered to Spectralis) with the size of 3456×2403, 2408×1536, 1024×1024 and 3744×3744 pixels, respectively. The images are shown in relative pixel size.
Fig. 4
Fig. 4 Flowchart of our method.
Fig. 5
Fig. 5 Registration results between a SLO and a Canon image. (a)–(c) are from method-1, method-2 and method-p; (d)–(f) are sub-regions from the red box of (a)–(c). The yellow arrows point out the misalignment.
Fig. 6
Fig. 6 Registration results between SLO and EasyScan image. (a)–(c) are from method-1, method-2 and method-p; (d)–(f) are sub-regions from the red box of (a)–(c). The yellow arrows point out the misalignment.
Fig. 7
Fig. 7 Registration results between SLO and Nidek image. (a)–(c) are from method-1, method-2 and method-p; (d)–(f) are sub-regions from the red box of (a)–(c). The yellow arrows point out the misalignment.
Fig. 8
Fig. 8 Registration results between SLO and Topcon image. (a)–(c) are from method-1, method-2 and method-p; (d)–(f) are sub-regions from the red box of (a)–(c). The yellow arrows point out the misalignment.
Fig. 9
Fig. 9 The box-plot of the Dice coefficients of our proposed method and the state-of-art method (method-1).

Tables (2)

Tables Icon

Table 1 The detail of the fundus cameras that are used for registration. The examples shown in the last column are cropped from the original one to show the same region on one retina, where the luminosity and contrast variation among different cameras can be observed.

Tables Icon

Table 2 Dice coefficient from three different registration methods

Equations (6)

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

φ ( x ) = atan ( | f R ( x ) | f e ( x ) ) ,
W ( x ) = x + u ( x )
E ( u ) = Ω | MIND f ( x ) MIND m ( x + u ( x ) ) | 2
E ( u ) = Ω | MIND f ( x ) MIND m ( x + u ( x ) ) | 2 + α u
R = ( A 1 , 1 1 ) 2 + ( A 2 , 1 1 ) 2
DSC = 2 | X Y | | X | + | Y | ,

Metrics