Abstract

In this paper, we introduce a novel feature-point-matching based framework for achieving an optimized joint-alignment of sequential images from multispectral imaging (MSI). It solves a low-rank and semidefinite matrix that stores all pairwise-image feature-mappings by minimizing the total amount of point-to-point matching cost via a convex optimization of a semidefinite programming formulation. This unique strategy takes a complete consideration of the information aggregated by all point-matching costs and enables the entire set of pairwise-image feature-mappings to be solved simultaneously and near-optimally. Our framework is capable of running in an automatic or interactive fashion, offering an effective tool for eliminating spatial misalignments introduced into sequential MSI images during the imaging process. Our experimental results obtained from a database of 28 sequences of MSI images of human eye demonstrate the superior performances of our approach to the state-of-the-art techniques. Our framework is potentially invaluable in a large variety of practical applications of MSI images.

© 2017 Optical Society of America

Full Article  |  PDF Article
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References

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2016 (2)

J. Lin, Y. Zheng, W. Jiao, B. Zhao, S. Zhang, J. Gee, and R. Xiao, “Groupwise registration of sequential images from multispectral imaging (MSI) of the retina and choroid,” Opt. Express 24(22), 25,277–25,290 (2016).
[Crossref]

Y. Zheng, P. Parikh, B. L. VanderBeek, and J. Gee, “Automated Quantification of Morphologic Features and Vasculature of Choroid on Enhanced Depth Imaging Optical Coherence Tomography (EDI-OCT),” Investigative Ophthalmology & Visual Science 57(12), 4652 (2016).

2015 (4)

J. Kutarnia and P. Pedersen, “A Markov random field approach to group-wise registration/mosaicing with application to ultrasound,” Med. Image Anal. 24(1), 106–124 (2015).
[Crossref] [PubMed]

Y. Zheng, B. Wei, H. Liu, R. Xiao, and J. C. Gee, “Measuring sparse temporal-variation for accurate registration of dynamic contrast-enhanced breast MR images,” Computerized Medical Imaging and Graphics 46, 73–80 (2015).
[Crossref] [PubMed]

N. T. Clancy, S. Arya, D. Stoyanov, M. Singh, G. B. Hanna, and D. S. Elson, “Intraoperative measurement of bowel oxygen saturation using a multispectral imaging laparoscope,” Biomed. Opt. Express 6(10), 4179–4190 (2015).
[Crossref] [PubMed]

H. Rabbani, M. J. Allingham, P. S. Mettu, S. W. Cousins, and S. Farsiu, “Fully Automatic Segmentation of Fluorescein Leakage in Subjects With Diabetic Macular EdemaAutomatic Leakage Segmentation in DME,” Investigative ophthalmology & visual science 56(3), 1482–1492 (2015).
[Crossref]

2014 (4)

C. Zimmer, D. Kahn, R. Clayton, P. Dugel, and K. Freund, “Innovation in diagnostic retinal imaging: multispectral imaging,” Retina Today 9(7), 94–99 (2014).

F. P. Oliveira and J. M. R. Tavares, “Medical image registration: a review,” Comput. Method. Biomec. 17(2), 73–93 (2014).
[Crossref]

P. Ghassemi, T. E. Travis, L. T. Moffatt, J. W. Shupp, and J. C. Ramella-Roman, “A polarized multispectral imaging system for quantitative assessment of hypertrophic scars,” Biomed. Opt. Express 5(10), 3337–3354 (2014).
[Crossref] [PubMed]

S. Park, S.-K. Park, and M. Hebert, “Fast and scalable approximate spectral matching for higher order graph matching,” IEEE transactions on pattern analysis and machine intelligence 36(3), 479–492 (2014).
[Crossref] [PubMed]

2013 (4)

B. Li, W. Wang, and H. Ye, “Multi-sensor image registration based on algebraic projective invariants,” Opt. Express 21(8), 9824–9838 (2013).
[Crossref] [PubMed]

C. Wachinger and N. Navab, “Simultaneous registration of multiple images: Similarity metrics and efficient optimization,” IEEE Trans. Pattern Anal. Mach. Intell. 35(5), 1221–1233 (2013).
[Crossref] [PubMed]

D. Hitchmoth, “Multispectral Imaging: A revolution in retinal diagnosis and health assessment,” Adv. Ocul. Care 4(4), 76–79 (2013).

P. Ghosh and B. Manjunath, “Robust simultaneous registration and segmentation with sparse error reconstruction,” IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 425–436 (2013).
[Crossref]

2012 (3)

A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, and Y. Ma, “Toward a practical face recognition system: Robust alignment and illumination by sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 372–386 (2012).
[Crossref]

A. Rasoulian, R. Rohling, and P. Abolmaesumi, “Group-wise registration of point sets for statistical shape models,” IEEE Trans. Med. Imaging 31(11), 2025–2034 (2012).
[Crossref] [PubMed]

D. L. Shechtman and P. M. Karpecki, “A look at MSI: multispectral imaging may help eye care providers diagnose retinal conditions earlier than conventional fundoscopy,” Rev. Opt. 149(1), 88–90 (2012).

2011 (4)

R. Maharaj, “The clinical applications of multispectral imaging,” Rev. Opt. 148(11), SS19 (2011).

Y. Ou, A. Sotiras, N. Paragios, and C. Davatzikos, “DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting,” Medical image analysis 15(4), 622–639 (2011).
[Crossref]

R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, and S. Farsiu, “Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing,” Biomedical optics express 2(10), 2871–2887 (2011).
[Crossref] [PubMed]

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine Learning 3(1), 1–122 (2011).
[Crossref]

2009 (1)

M. B. Bouchard, B. R. Chen, S. A. Burgess, and E. M. Hillman, “Ultra-fast multispectral optical imaging of cortical oxygenation, blood flow, and intracellular calcium dynamics,” Opt. Express 17(18), 15,670–15,678 (2009).
[Crossref]

2007 (1)

A. Gholipour, N. Kehtarnavaz, R. Briggs, M. Devous, and K. Gopinath, “Brain functional localization: a survey of image registration techniques,” IEEE Trans. Med. Imaging 26, 427–451 (2007).
[Crossref] [PubMed]

2006 (1)

E. G. Learned-Miller, “Data driven image models through continuous joint alignment,” IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 236–250 (2006).
[Crossref] [PubMed]

2000 (1)

C. Rorden and M. Brett, “Stereotaxic display of brain lesions,” Behav. Neurol. 12(4), 191–200 (2000).
[Crossref]

1999 (1)

C. V. Stewart, “Robust parameter estimation in computer vision,” SIAM Rev. 41(3), 513–537 (1999).
[Crossref]

Abolmaesumi, P.

A. Rasoulian, R. Rohling, and P. Abolmaesumi, “Group-wise registration of point sets for statistical shape models,” IEEE Trans. Med. Imaging 31(11), 2025–2034 (2012).
[Crossref] [PubMed]

Allingham, M. J.

H. Rabbani, M. J. Allingham, P. S. Mettu, S. W. Cousins, and S. Farsiu, “Fully Automatic Segmentation of Fluorescein Leakage in Subjects With Diabetic Macular EdemaAutomatic Leakage Segmentation in DME,” Investigative ophthalmology & visual science 56(3), 1482–1492 (2015).
[Crossref]

Anyfanti, P.

C. Hernandez-Matas, X. Zabulis, A. Triantafyllou, P. Anyfanti, and A. A. Argyros, “Retinal image registration under the assumption of a spherical eye,” Computerized Medical Imaging and Graphics (2016).

Arandjelovic, O.

O. Arandjelovic, D.-S. Pham, and S. Venkatesh, “Groupwise registration of aerial images,”presented on the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015) (2015).

Argyros, A. A.

C. Hernandez-Matas, X. Zabulis, A. Triantafyllou, P. Anyfanti, and A. A. Argyros, “Retinal image registration under the assumption of a spherical eye,” Computerized Medical Imaging and Graphics (2016).

Arya, S.

Balci, S. K.

S. K. Balci, P. Golland, and W. Wells, “Non-rigid groupwise registration using B-spline deformation model,” Open source and open data for MICCAI pp. 105–121 (2007).

Bouchard, M. B.

M. B. Bouchard, B. R. Chen, S. A. Burgess, and E. M. Hillman, “Ultra-fast multispectral optical imaging of cortical oxygenation, blood flow, and intracellular calcium dynamics,” Opt. Express 17(18), 15,670–15,678 (2009).
[Crossref]

Boyd, S.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine Learning 3(1), 1–122 (2011).
[Crossref]

Brett, M.

C. Rorden and M. Brett, “Stereotaxic display of brain lesions,” Behav. Neurol. 12(4), 191–200 (2000).
[Crossref]

Briggs, R.

A. Gholipour, N. Kehtarnavaz, R. Briggs, M. Devous, and K. Gopinath, “Brain functional localization: a survey of image registration techniques,” IEEE Trans. Med. Imaging 26, 427–451 (2007).
[Crossref] [PubMed]

Burgess, S. A.

M. B. Bouchard, B. R. Chen, S. A. Burgess, and E. M. Hillman, “Ultra-fast multispectral optical imaging of cortical oxygenation, blood flow, and intracellular calcium dynamics,” Opt. Express 17(18), 15,670–15,678 (2009).
[Crossref]

Cabrera, M. T.

R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, and S. Farsiu, “Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing,” Biomedical optics express 2(10), 2871–2887 (2011).
[Crossref] [PubMed]

Cai, W.

Y. Zheng, S. Zhang, J. Huang, and W. Cai, “Guest Editorial: Special issue on advances in computing techniques for big medical image data,” Neurocomputing (2016).

Calcagni, A.

N. Everdell, I. Styles, E. Claridge, J. Hebden, and A. Calcagni, “Multispectral imaging of the ocular fundus using LED illumination,” in European Conferences on Biomedical Optics, pp. 73,711C (International Society for Optics and Photonics, 2009).

Chen, B. R.

M. B. Bouchard, B. R. Chen, S. A. Burgess, and E. M. Hillman, “Ultra-fast multispectral optical imaging of cortical oxygenation, blood flow, and intracellular calcium dynamics,” Opt. Express 17(18), 15,670–15,678 (2009).
[Crossref]

Chen, C.

Y. Li, C. Chen, F. Yang, and J. Huang, “Deep sparse representation for robust image registration,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4894–4901 (2015).

Chu, E.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine Learning 3(1), 1–122 (2011).
[Crossref]

Clancy, N. T.

Claridge, E.

N. Everdell, I. Styles, E. Claridge, J. Hebden, and A. Calcagni, “Multispectral imaging of the ocular fundus using LED illumination,” in European Conferences on Biomedical Optics, pp. 73,711C (International Society for Optics and Photonics, 2009).

Clayton, R.

C. Zimmer, D. Kahn, R. Clayton, P. Dugel, and K. Freund, “Innovation in diagnostic retinal imaging: multispectral imaging,” Retina Today 9(7), 94–99 (2014).

Cousins, S. W.

H. Rabbani, M. J. Allingham, P. S. Mettu, S. W. Cousins, and S. Farsiu, “Fully Automatic Segmentation of Fluorescein Leakage in Subjects With Diabetic Macular EdemaAutomatic Leakage Segmentation in DME,” Investigative ophthalmology & visual science 56(3), 1482–1492 (2015).
[Crossref]

Davatzikos, C.

Y. Ou, A. Sotiras, N. Paragios, and C. Davatzikos, “DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting,” Medical image analysis 15(4), 622–639 (2011).
[Crossref]

Devous, M.

A. Gholipour, N. Kehtarnavaz, R. Briggs, M. Devous, and K. Gopinath, “Brain functional localization: a survey of image registration techniques,” IEEE Trans. Med. Imaging 26, 427–451 (2007).
[Crossref] [PubMed]

Dugel, P.

C. Zimmer, D. Kahn, R. Clayton, P. Dugel, and K. Freund, “Innovation in diagnostic retinal imaging: multispectral imaging,” Retina Today 9(7), 94–99 (2014).

Eckstein, J.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine Learning 3(1), 1–122 (2011).
[Crossref]

Efros, A. A.

T. Zhou, Y. J. Lee, X. Y. Stella, and A. A. Efros, “Flowweb: Joint image set alignment by weaving consistent, pixel-wise correspondences,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1191–1200 (IEEE, 2015).

Elson, D. S.

Estrada, R.

R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, and S. Farsiu, “Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing,” Biomedical optics express 2(10), 2871–2887 (2011).
[Crossref] [PubMed]

Everdell, N.

N. Everdell, I. Styles, E. Claridge, J. Hebden, and A. Calcagni, “Multispectral imaging of the ocular fundus using LED illumination,” in European Conferences on Biomedical Optics, pp. 73,711C (International Society for Optics and Photonics, 2009).

Farsiu, S.

H. Rabbani, M. J. Allingham, P. S. Mettu, S. W. Cousins, and S. Farsiu, “Fully Automatic Segmentation of Fluorescein Leakage in Subjects With Diabetic Macular EdemaAutomatic Leakage Segmentation in DME,” Investigative ophthalmology & visual science 56(3), 1482–1492 (2015).
[Crossref]

R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, and S. Farsiu, “Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing,” Biomedical optics express 2(10), 2871–2887 (2011).
[Crossref] [PubMed]

Freedman, S. F.

R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, and S. Farsiu, “Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing,” Biomedical optics express 2(10), 2871–2887 (2011).
[Crossref] [PubMed]

Freund, K.

C. Zimmer, D. Kahn, R. Clayton, P. Dugel, and K. Freund, “Innovation in diagnostic retinal imaging: multispectral imaging,” Retina Today 9(7), 94–99 (2014).

Ganesh, A.

A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, and Y. Ma, “Toward a practical face recognition system: Robust alignment and illumination by sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 372–386 (2012).
[Crossref]

Gee, J.

Y. Zheng, P. Parikh, B. L. VanderBeek, and J. Gee, “Automated Quantification of Morphologic Features and Vasculature of Choroid on Enhanced Depth Imaging Optical Coherence Tomography (EDI-OCT),” Investigative Ophthalmology & Visual Science 57(12), 4652 (2016).

J. Lin, Y. Zheng, W. Jiao, B. Zhao, S. Zhang, J. Gee, and R. Xiao, “Groupwise registration of sequential images from multispectral imaging (MSI) of the retina and choroid,” Opt. Express 24(22), 25,277–25,290 (2016).
[Crossref]

Gee, J. C.

Y. Zheng, B. Wei, H. Liu, R. Xiao, and J. C. Gee, “Measuring sparse temporal-variation for accurate registration of dynamic contrast-enhanced breast MR images,” Computerized Medical Imaging and Graphics 46, 73–80 (2015).
[Crossref] [PubMed]

Ghassemi, P.

Gholipour, A.

A. Gholipour, N. Kehtarnavaz, R. Briggs, M. Devous, and K. Gopinath, “Brain functional localization: a survey of image registration techniques,” IEEE Trans. Med. Imaging 26, 427–451 (2007).
[Crossref] [PubMed]

Ghosh, P.

P. Ghosh and B. Manjunath, “Robust simultaneous registration and segmentation with sparse error reconstruction,” IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 425–436 (2013).
[Crossref]

Golland, P.

S. K. Balci, P. Golland, and W. Wells, “Non-rigid groupwise registration using B-spline deformation model,” Open source and open data for MICCAI pp. 105–121 (2007).

Gopinath, K.

A. Gholipour, N. Kehtarnavaz, R. Briggs, M. Devous, and K. Gopinath, “Brain functional localization: a survey of image registration techniques,” IEEE Trans. Med. Imaging 26, 427–451 (2007).
[Crossref] [PubMed]

Grauman, K.

J. Kim, C. Liu, F. Sha, and K. Grauman, “Deformable spatial pyramid matching for fast dense correspondences,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2307–2314 (2013).

Grimson, E.

L. Zöllei, E. Learned-Miller, E. Grimson, and W. Wells, “Efficient population registration of 3D data,” in International Workshop on Computer Vision for Biomedical Image Applications, pp. 291–301 (Springer, 2005).

Guibas, L.

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S. Park, S.-K. Park, and M. Hebert, “Fast and scalable approximate spectral matching for higher order graph matching,” IEEE transactions on pattern analysis and machine intelligence 36(3), 479–492 (2014).
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C. Hernandez-Matas, X. Zabulis, A. Triantafyllou, P. Anyfanti, and A. A. Argyros, “Retinal image registration under the assumption of a spherical eye,” Computerized Medical Imaging and Graphics (2016).

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M. B. Bouchard, B. R. Chen, S. A. Burgess, and E. M. Hillman, “Ultra-fast multispectral optical imaging of cortical oxygenation, blood flow, and intracellular calcium dynamics,” Opt. Express 17(18), 15,670–15,678 (2009).
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Y. Li, C. Chen, F. Yang, and J. Huang, “Deep sparse representation for robust image registration,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4894–4901 (2015).

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Y. Zheng, S. Zhang, J. Huang, and W. Cai, “Guest Editorial: Special issue on advances in computing techniques for big medical image data,” Neurocomputing (2016).

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Q.-X. Huang and L. Guibas, “Consistent shape maps via semidefinite programming,” in Computer Graphics Forum, vol. 32, pp. 177–186 (Wiley Online Library, 2013).
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J. Yao, Z. Xu, X. Huang, and J. Huang, “Accelerated dynamic MRI reconstruction with total variation and nuclear norm regularization,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 635–642 (Springer, 2015).

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X. Shen, L. Xu, Q. Zhang, and J. Jia, “Multi-modal and multi-spectral registration for natural images,” in European Conference on Computer Vision, pp. 309–324 (Springer, 2014).

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J. Lin, Y. Zheng, W. Jiao, B. Zhao, S. Zhang, J. Gee, and R. Xiao, “Groupwise registration of sequential images from multispectral imaging (MSI) of the retina and choroid,” Opt. Express 24(22), 25,277–25,290 (2016).
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C. Zimmer, D. Kahn, R. Clayton, P. Dugel, and K. Freund, “Innovation in diagnostic retinal imaging: multispectral imaging,” Retina Today 9(7), 94–99 (2014).

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D. L. Shechtman and P. M. Karpecki, “A look at MSI: multispectral imaging may help eye care providers diagnose retinal conditions earlier than conventional fundoscopy,” Rev. Opt. 149(1), 88–90 (2012).

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A. Gholipour, N. Kehtarnavaz, R. Briggs, M. Devous, and K. Gopinath, “Brain functional localization: a survey of image registration techniques,” IEEE Trans. Med. Imaging 26, 427–451 (2007).
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J. Kutarnia and P. Pedersen, “A Markov random field approach to group-wise registration/mosaicing with application to ultrasound,” Med. Image Anal. 24(1), 106–124 (2015).
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L. Zöllei, E. Learned-Miller, E. Grimson, and W. Wells, “Efficient population registration of 3D data,” in International Workshop on Computer Vision for Biomedical Image Applications, pp. 291–301 (Springer, 2005).

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E. G. Learned-Miller, “Data driven image models through continuous joint alignment,” IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 236–250 (2006).
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J. Lin, Y. Zheng, W. Jiao, B. Zhao, S. Zhang, J. Gee, and R. Xiao, “Groupwise registration of sequential images from multispectral imaging (MSI) of the retina and choroid,” Opt. Express 24(22), 25,277–25,290 (2016).
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Y. Zheng, B. Wei, H. Liu, R. Xiao, and J. C. Gee, “Measuring sparse temporal-variation for accurate registration of dynamic contrast-enhanced breast MR images,” Computerized Medical Imaging and Graphics 46, 73–80 (2015).
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H. Rabbani, M. J. Allingham, P. S. Mettu, S. W. Cousins, and S. Farsiu, “Fully Automatic Segmentation of Fluorescein Leakage in Subjects With Diabetic Macular EdemaAutomatic Leakage Segmentation in DME,” Investigative ophthalmology & visual science 56(3), 1482–1492 (2015).
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Y. Zheng, P. Parikh, B. L. VanderBeek, and J. Gee, “Automated Quantification of Morphologic Features and Vasculature of Choroid on Enhanced Depth Imaging Optical Coherence Tomography (EDI-OCT),” Investigative Ophthalmology & Visual Science 57(12), 4652 (2016).

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S. Park, S.-K. Park, and M. Hebert, “Fast and scalable approximate spectral matching for higher order graph matching,” IEEE transactions on pattern analysis and machine intelligence 36(3), 479–492 (2014).
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S. Park, S.-K. Park, and M. Hebert, “Fast and scalable approximate spectral matching for higher order graph matching,” IEEE transactions on pattern analysis and machine intelligence 36(3), 479–492 (2014).
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J. Kutarnia and P. Pedersen, “A Markov random field approach to group-wise registration/mosaicing with application to ultrasound,” Med. Image Anal. 24(1), 106–124 (2015).
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H. Rabbani, M. J. Allingham, P. S. Mettu, S. W. Cousins, and S. Farsiu, “Fully Automatic Segmentation of Fluorescein Leakage in Subjects With Diabetic Macular EdemaAutomatic Leakage Segmentation in DME,” Investigative ophthalmology & visual science 56(3), 1482–1492 (2015).
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D. L. Shechtman and P. M. Karpecki, “A look at MSI: multispectral imaging may help eye care providers diagnose retinal conditions earlier than conventional fundoscopy,” Rev. Opt. 149(1), 88–90 (2012).

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X. Shen, L. Xu, Q. Zhang, and J. Jia, “Multi-modal and multi-spectral registration for natural images,” in European Conference on Computer Vision, pp. 309–324 (Springer, 2014).

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Y. Ou, A. Sotiras, N. Paragios, and C. Davatzikos, “DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting,” Medical image analysis 15(4), 622–639 (2011).
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T. Zhou, Y. J. Lee, X. Y. Stella, and A. A. Efros, “Flowweb: Joint image set alignment by weaving consistent, pixel-wise correspondences,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1191–1200 (IEEE, 2015).

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Y. Zheng, P. Parikh, B. L. VanderBeek, and J. Gee, “Automated Quantification of Morphologic Features and Vasculature of Choroid on Enhanced Depth Imaging Optical Coherence Tomography (EDI-OCT),” Investigative Ophthalmology & Visual Science 57(12), 4652 (2016).

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C. Wachinger and N. Navab, “Simultaneous registration of multiple images: Similarity metrics and efficient optimization,” IEEE Trans. Pattern Anal. Mach. Intell. 35(5), 1221–1233 (2013).
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Y. Zheng, B. Wei, H. Liu, R. Xiao, and J. C. Gee, “Measuring sparse temporal-variation for accurate registration of dynamic contrast-enhanced breast MR images,” Computerized Medical Imaging and Graphics 46, 73–80 (2015).
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A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, and Y. Ma, “Toward a practical face recognition system: Robust alignment and illumination by sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 372–386 (2012).
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Xiao, R.

J. Lin, Y. Zheng, W. Jiao, B. Zhao, S. Zhang, J. Gee, and R. Xiao, “Groupwise registration of sequential images from multispectral imaging (MSI) of the retina and choroid,” Opt. Express 24(22), 25,277–25,290 (2016).
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Y. Zheng, B. Wei, H. Liu, R. Xiao, and J. C. Gee, “Measuring sparse temporal-variation for accurate registration of dynamic contrast-enhanced breast MR images,” Computerized Medical Imaging and Graphics 46, 73–80 (2015).
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X. Shen, L. Xu, Q. Zhang, and J. Jia, “Multi-modal and multi-spectral registration for natural images,” in European Conference on Computer Vision, pp. 309–324 (Springer, 2014).

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J. Yao, Z. Xu, X. Huang, and J. Huang, “Accelerated dynamic MRI reconstruction with total variation and nuclear norm regularization,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 635–642 (Springer, 2015).

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Y. Li, C. Chen, F. Yang, and J. Huang, “Deep sparse representation for robust image registration,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4894–4901 (2015).

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X. Peng, S. Zhang, Y. Yang, and D. N. Metaxas, “Piefa: Personalized incremental and ensemble face alignment,” in Proceedings of the IEEE International Conference on Computer Vision, pp. 3880–3888 (2015).

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J. Yao, Z. Xu, X. Huang, and J. Huang, “Accelerated dynamic MRI reconstruction with total variation and nuclear norm regularization,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 635–642 (Springer, 2015).

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Zabulis, X.

C. Hernandez-Matas, X. Zabulis, A. Triantafyllou, P. Anyfanti, and A. A. Argyros, “Retinal image registration under the assumption of a spherical eye,” Computerized Medical Imaging and Graphics (2016).

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J. Lin, Y. Zheng, W. Jiao, B. Zhao, S. Zhang, J. Gee, and R. Xiao, “Groupwise registration of sequential images from multispectral imaging (MSI) of the retina and choroid,” Opt. Express 24(22), 25,277–25,290 (2016).
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Zhao, B.

J. Lin, Y. Zheng, W. Jiao, B. Zhao, S. Zhang, J. Gee, and R. Xiao, “Groupwise registration of sequential images from multispectral imaging (MSI) of the retina and choroid,” Opt. Express 24(22), 25,277–25,290 (2016).
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J. Lin, Y. Zheng, W. Jiao, B. Zhao, S. Zhang, J. Gee, and R. Xiao, “Groupwise registration of sequential images from multispectral imaging (MSI) of the retina and choroid,” Opt. Express 24(22), 25,277–25,290 (2016).
[Crossref]

Y. Zheng, P. Parikh, B. L. VanderBeek, and J. Gee, “Automated Quantification of Morphologic Features and Vasculature of Choroid on Enhanced Depth Imaging Optical Coherence Tomography (EDI-OCT),” Investigative Ophthalmology & Visual Science 57(12), 4652 (2016).

Y. Zheng, B. Wei, H. Liu, R. Xiao, and J. C. Gee, “Measuring sparse temporal-variation for accurate registration of dynamic contrast-enhanced breast MR images,” Computerized Medical Imaging and Graphics 46, 73–80 (2015).
[Crossref] [PubMed]

Y. Zheng, S. Zhang, J. Huang, and W. Cai, “Guest Editorial: Special issue on advances in computing techniques for big medical image data,” Neurocomputing (2016).

Zhou, T.

T. Zhou, Y. J. Lee, X. Y. Stella, and A. A. Efros, “Flowweb: Joint image set alignment by weaving consistent, pixel-wise correspondences,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1191–1200 (IEEE, 2015).

Zhou, Z.

A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, and Y. Ma, “Toward a practical face recognition system: Robust alignment and illumination by sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 372–386 (2012).
[Crossref]

Zimmer, C.

C. Zimmer, D. Kahn, R. Clayton, P. Dugel, and K. Freund, “Innovation in diagnostic retinal imaging: multispectral imaging,” Retina Today 9(7), 94–99 (2014).

Zöllei, L.

L. Zöllei, E. Learned-Miller, E. Grimson, and W. Wells, “Efficient population registration of 3D data,” in International Workshop on Computer Vision for Biomedical Image Applications, pp. 291–301 (Springer, 2005).

Adv. Ocul. Care (1)

D. Hitchmoth, “Multispectral Imaging: A revolution in retinal diagnosis and health assessment,” Adv. Ocul. Care 4(4), 76–79 (2013).

Behav. Neurol. (1)

C. Rorden and M. Brett, “Stereotaxic display of brain lesions,” Behav. Neurol. 12(4), 191–200 (2000).
[Crossref]

Biomed. Opt. Express (2)

Biomedical optics express (1)

R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, and S. Farsiu, “Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing,” Biomedical optics express 2(10), 2871–2887 (2011).
[Crossref] [PubMed]

Comput. Method. Biomec. (1)

F. P. Oliveira and J. M. R. Tavares, “Medical image registration: a review,” Comput. Method. Biomec. 17(2), 73–93 (2014).
[Crossref]

Computerized Medical Imaging and Graphics (1)

Y. Zheng, B. Wei, H. Liu, R. Xiao, and J. C. Gee, “Measuring sparse temporal-variation for accurate registration of dynamic contrast-enhanced breast MR images,” Computerized Medical Imaging and Graphics 46, 73–80 (2015).
[Crossref] [PubMed]

Foundations and Trends® in Machine Learning (1)

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine Learning 3(1), 1–122 (2011).
[Crossref]

IEEE Trans. Med. Imaging (2)

A. Rasoulian, R. Rohling, and P. Abolmaesumi, “Group-wise registration of point sets for statistical shape models,” IEEE Trans. Med. Imaging 31(11), 2025–2034 (2012).
[Crossref] [PubMed]

A. Gholipour, N. Kehtarnavaz, R. Briggs, M. Devous, and K. Gopinath, “Brain functional localization: a survey of image registration techniques,” IEEE Trans. Med. Imaging 26, 427–451 (2007).
[Crossref] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell. (4)

C. Wachinger and N. Navab, “Simultaneous registration of multiple images: Similarity metrics and efficient optimization,” IEEE Trans. Pattern Anal. Mach. Intell. 35(5), 1221–1233 (2013).
[Crossref] [PubMed]

P. Ghosh and B. Manjunath, “Robust simultaneous registration and segmentation with sparse error reconstruction,” IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 425–436 (2013).
[Crossref]

A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, and Y. Ma, “Toward a practical face recognition system: Robust alignment and illumination by sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 372–386 (2012).
[Crossref]

E. G. Learned-Miller, “Data driven image models through continuous joint alignment,” IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 236–250 (2006).
[Crossref] [PubMed]

IEEE transactions on pattern analysis and machine intelligence (1)

S. Park, S.-K. Park, and M. Hebert, “Fast and scalable approximate spectral matching for higher order graph matching,” IEEE transactions on pattern analysis and machine intelligence 36(3), 479–492 (2014).
[Crossref] [PubMed]

Investigative Ophthalmology & Visual Science (1)

Y. Zheng, P. Parikh, B. L. VanderBeek, and J. Gee, “Automated Quantification of Morphologic Features and Vasculature of Choroid on Enhanced Depth Imaging Optical Coherence Tomography (EDI-OCT),” Investigative Ophthalmology & Visual Science 57(12), 4652 (2016).

H. Rabbani, M. J. Allingham, P. S. Mettu, S. W. Cousins, and S. Farsiu, “Fully Automatic Segmentation of Fluorescein Leakage in Subjects With Diabetic Macular EdemaAutomatic Leakage Segmentation in DME,” Investigative ophthalmology & visual science 56(3), 1482–1492 (2015).
[Crossref]

Med. Image Anal. (1)

J. Kutarnia and P. Pedersen, “A Markov random field approach to group-wise registration/mosaicing with application to ultrasound,” Med. Image Anal. 24(1), 106–124 (2015).
[Crossref] [PubMed]

Medical image analysis (1)

Y. Ou, A. Sotiras, N. Paragios, and C. Davatzikos, “DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting,” Medical image analysis 15(4), 622–639 (2011).
[Crossref]

Opt. Express (3)

B. Li, W. Wang, and H. Ye, “Multi-sensor image registration based on algebraic projective invariants,” Opt. Express 21(8), 9824–9838 (2013).
[Crossref] [PubMed]

J. Lin, Y. Zheng, W. Jiao, B. Zhao, S. Zhang, J. Gee, and R. Xiao, “Groupwise registration of sequential images from multispectral imaging (MSI) of the retina and choroid,” Opt. Express 24(22), 25,277–25,290 (2016).
[Crossref]

M. B. Bouchard, B. R. Chen, S. A. Burgess, and E. M. Hillman, “Ultra-fast multispectral optical imaging of cortical oxygenation, blood flow, and intracellular calcium dynamics,” Opt. Express 17(18), 15,670–15,678 (2009).
[Crossref]

Retina Today (1)

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Figures (4)

Fig. 1
Fig. 1 A sequence of MSI images acquired by Annidis RHA from a patient diagnosed with hypertensive retinopathy, in which from left to right and from top to bottom, the first 11 images are captured with short wavelengths of “Green” (MSI-550), “Yellow” (MSI-580) and “Amber” (MSI-590), respectively, followed by 4 wavelengths of “Red” (MSI-620, MSI-660, MSI-690 and MSI-740) and 4 wavelengths of “Infrared” (MSI-760, MSI-780, MSI-810 and MSI-850), respectively.
Fig. 2
Fig. 2 Performance curves of our approach when different percentage (0%, 25%, 50%, 75% and 100%, respectively) of the images in each sequence are matched jointly while the left ones are matched in a pairwise fashion. The y-axis shows the ratio of correct matches while the x-axis denotes the threshold of matching distance divided by the radius of retina.
Fig. 3
Fig. 3 Performance curve of our approach when Gaussian noise with zero mean and various variances (0, 0001, 0.05, 0.1, 0.15 and 0.2, respectively) is added in the matching cost and when different percentage (0%, 25%, 50%, 75% and 95%, respectively) of manual correspondences are fixed (via Eq. (8)) when solving the joint alignment.
Fig. 4
Fig. 4 Comparisons of matched feature points on the images in Fig. 1 by our algorithm in a pairwise-matching fashion (upper-left), our algorithm in a joint alignment way (upper-right), the technique in [30] (lower-left) and the one in [28] (lower-right). From top to down of each panel, a local area of the “Amber” (MSI=590), “Green” (MSI=550) and “Red” (MSI=620) images is shown, respectively.

Tables (1)

Tables Icon

Table 1 Ratios of correct matches (with a distance threshold 0.03) of our approach in a pairwise way (“SDP-Pairwise”), our approach of joint alignment (“SDP-Joint”), the quadratic programming based group registration in [30] (“QP-Group”) and the pairwise robust matching in [28] (“Robust”), performed on the healthy subjects and patients, respectively.

Equations (10)

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

{ X i j 1 = 1 , if | S i | | S j | X i j T 1 = 1 , otherwise
X = [ I m 1 X 12 X 1 N X 21 I m 2 X 2 N X N 1 X N 2 I m N ]
C = [ C 11 C 12 C 1 N C 21 C 22 C 2 N C N 1 C N 2 C N N ] .
min Tr [ CX ]
X { 0 , 1 } M × M ,
X 0
X i i = I m i ,
X ( u , v ) = 1 , ( u , v ) U ,
C i , j ( k , l ) = w ρ ( 1 γ ( I i ( k ) , I j ( l ) ) ) + ( 1 w ) ρ ( 1 γ ( I i ( k ) , I j ( l ) ) )
ρ ( t ) = t 2 1 + t 2 / a 2

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