4 Reconstruction of Retinal OCT Images with Sparse Representation
101
References
1. W. Drexler, U. Morgner, R.K. Ghanta, F.X. Kärtner, J.S. Schuman, J.G. Fujimoto, Ultrahighresolution ophthalmic optical coherence tomography. Nat. Med. 7, 502–507 (2001)
2. L. Fang, S. Li, Q. Nie, J.A. Izatt, C.A. Toth, S. Farsiu, Sparsity based denoising of spectral
domain optical coherence tomography images. Biomed. Opt. Express 3, 927–942 (2012)
3. H.M. Salinas, D.C. Fernández, Comparison of PDE-based nonlinear diffusion approaches for
image enhancement and denoising in optical coherence tomography. IEEE Trans. Med. Imag.
26, 761–771 (2007)
4. A. Wong, A. Mishra, K. Bizheva, D.A. Clausi, General Bayesian estimation for speckle noise
reduction in optical coherence tomography retinal imagery. Opt. Exp. 18, 8338–8352 (2010)
5. L. Fang, S. Li, R. McNabb, Q. Nie, A. Kuo, C. Toth et al., Fast acquisition and reconstruction
of optical coherence tomography images via sparse representation. IEEE Trans. Med. Imag.
32, 2034–2049 (2013)
6. P. Milanfar, A tour of modern image filtering: new insights and methods, both practical and
theoretical. IEEE Signal Process. Mag. 30, 106–128 (2013)
7. A. Ozcan, A. Bilenca, A.E. Desjardins, B.E. Bouma, G.J. Tearney, Speckle reduction in optical
coherence tomography images using digital filtering. J. Opt. Soc. Am. 24, 1901–1910 (2007)
8. A. Boroomand, A. Wong, E. Li, D.S. Cho, B. Ni, K. Bizheva, Multi-penalty conditional random field approach to super-resolved reconstruction of optical coherence tomography images.
Biomed. Opt. Exp. 4, 2032–2050 (2013)
9. X. Liu, J.U. Kang, Compressive SD-OCT: the application of compressed sensing in spectral
domain optical coherence tomography. Opt. Exp. 18, 22010–22019 (2010)
10. D. Xu, N. Vaswani, Y. Huang, J.U. Kang, Modified compressive sensing optical coherence
tomography with noise reduction. Opt. Lett. 37, 4209–4211 (2012)
11. D. Xu, Y. Huang, J.U. Kang, Real-time compressive sensing spectral domain optical coherence
tomography. Opt. Lett. 39, 76–79 (2014)
12. E. Lebed, P.J. Mackenzie, M.V. Sarunic, F.M. Beg, Rapid volumetric OCT image acquisition
using compressive sampling. Opt. Exp. 18, 21003–21012 (2010)
13. A.B. Wu, E. Lebed, M.V. Sarunic, M.F. Beg, Quantitative evaluation of transform domains
for compressive sampling-based recovery of sparsely sampled volumetric OCT images. IEEE
Trans. Biomed. Eng. 60, 470–478 (2013)
14. K.L. Lurie, R. Angst, A.K. Ellerbee, Automated mosaicing of feature-poor optical coherence
tomography volumes with an integrated white light imaging system. IEEE Trans. Biomed. Eng.
61, 2141–2153 (2014)
15. G.T. Chong, S. Farsiu, S.F. Freedman, N. Sarin, A.F. Koreishi, J.A. Izatt et al., Abnormal foveal
morphology in ocular albinism imaged with spectral-domain optical coherence tomography.
Arch. Ophthalmol. 127, 37–44 (2009)
16. G.K. Wallace, The JPEG still picture compression standard. ACM Commun. 34, 30–44 (1991)
17. H. Rabbani, R. Nezafat, S. Gazor, Wavelet-domain medical image denoising using bivariate
laplacian mixture model. IEEE Trans. Biomed. Eng. 56, 2826 (2009)
18. Z. Jian, L. Yu, B. Rao, B.J. Tromberg, Z. Chen, Three-dimensional speckle suppression in
optical coherence tomography based on the curvelet transform. Opt. Exp. 18, 1024–1032 (2010)
19. R. Rubinstein, A.M. Bruckstein, M. Elad, Dictionaries for sparse representation modeling.
Proc. IEEE 98, 1045–1057 (2010)
20. B.A. Olshausen, D.J. Field, Emergence of simple-cell receptive field properties by learning a
sparse code for natural images. Nature 381, 607–609 (1996)
21. S. Li, L. Fang, H. Yin, An efficient dictionary learning algorithm and its application to 3-D
medical image denoising. IEEE Trans. Biomed. Eng. 59, 417–427 (2012)
22. A. Wong, A. Mishra, P. Fieguth, D.A. Clausi, Sparse reconstruction of breast mri using homotopic minimization in a regional sparsified domain. IEEE Trans. Biomed. Eng. 60, 743–752
(2013)
23. S. Li, H. Yin, L. Fang, Group-sparse representation with dictionary learning for medical image
denoising and fusion. IEEE Trans. Biomed. Eng. 59, 3450–3459 (2012)
101
References
1. W. Drexler, U. Morgner, R.K. Ghanta, F.X. Kärtner, J.S. Schuman, J.G. Fujimoto, Ultrahighresolution ophthalmic optical coherence tomography. Nat. Med. 7, 502–507 (2001)
2. L. Fang, S. Li, Q. Nie, J.A. Izatt, C.A. Toth, S. Farsiu, Sparsity based denoising of spectral
domain optical coherence tomography images. Biomed. Opt. Express 3, 927–942 (2012)
3. H.M. Salinas, D.C. Fernández, Comparison of PDE-based nonlinear diffusion approaches for
image enhancement and denoising in optical coherence tomography. IEEE Trans. Med. Imag.
26, 761–771 (2007)
4. A. Wong, A. Mishra, K. Bizheva, D.A. Clausi, General Bayesian estimation for speckle noise
reduction in optical coherence tomography retinal imagery. Opt. Exp. 18, 8338–8352 (2010)
5. L. Fang, S. Li, R. McNabb, Q. Nie, A. Kuo, C. Toth et al., Fast acquisition and reconstruction
of optical coherence tomography images via sparse representation. IEEE Trans. Med. Imag.
32, 2034–2049 (2013)
6. P. Milanfar, A tour of modern image filtering: new insights and methods, both practical and
theoretical. IEEE Signal Process. Mag. 30, 106–128 (2013)
7. A. Ozcan, A. Bilenca, A.E. Desjardins, B.E. Bouma, G.J. Tearney, Speckle reduction in optical
coherence tomography images using digital filtering. J. Opt. Soc. Am. 24, 1901–1910 (2007)
8. A. Boroomand, A. Wong, E. Li, D.S. Cho, B. Ni, K. Bizheva, Multi-penalty conditional random field approach to super-resolved reconstruction of optical coherence tomography images.
Biomed. Opt. Exp. 4, 2032–2050 (2013)
9. X. Liu, J.U. Kang, Compressive SD-OCT: the application of compressed sensing in spectral
domain optical coherence tomography. Opt. Exp. 18, 22010–22019 (2010)
10. D. Xu, N. Vaswani, Y. Huang, J.U. Kang, Modified compressive sensing optical coherence
tomography with noise reduction. Opt. Lett. 37, 4209–4211 (2012)
11. D. Xu, Y. Huang, J.U. Kang, Real-time compressive sensing spectral domain optical coherence
tomography. Opt. Lett. 39, 76–79 (2014)
12. E. Lebed, P.J. Mackenzie, M.V. Sarunic, F.M. Beg, Rapid volumetric OCT image acquisition
using compressive sampling. Opt. Exp. 18, 21003–21012 (2010)
13. A.B. Wu, E. Lebed, M.V. Sarunic, M.F. Beg, Quantitative evaluation of transform domains
for compressive sampling-based recovery of sparsely sampled volumetric OCT images. IEEE
Trans. Biomed. Eng. 60, 470–478 (2013)
14. K.L. Lurie, R. Angst, A.K. Ellerbee, Automated mosaicing of feature-poor optical coherence
tomography volumes with an integrated white light imaging system. IEEE Trans. Biomed. Eng.
61, 2141–2153 (2014)
15. G.T. Chong, S. Farsiu, S.F. Freedman, N. Sarin, A.F. Koreishi, J.A. Izatt et al., Abnormal foveal
morphology in ocular albinism imaged with spectral-domain optical coherence tomography.
Arch. Ophthalmol. 127, 37–44 (2009)
16. G.K. Wallace, The JPEG still picture compression standard. ACM Commun. 34, 30–44 (1991)
17. H. Rabbani, R. Nezafat, S. Gazor, Wavelet-domain medical image denoising using bivariate
laplacian mixture model. IEEE Trans. Biomed. Eng. 56, 2826 (2009)
18. Z. Jian, L. Yu, B. Rao, B.J. Tromberg, Z. Chen, Three-dimensional speckle suppression in
optical coherence tomography based on the curvelet transform. Opt. Exp. 18, 1024–1032 (2010)
19. R. Rubinstein, A.M. Bruckstein, M. Elad, Dictionaries for sparse representation modeling.
Proc. IEEE 98, 1045–1057 (2010)
20. B.A. Olshausen, D.J. Field, Emergence of simple-cell receptive field properties by learning a
sparse code for natural images. Nature 381, 607–609 (1996)
21. S. Li, L. Fang, H. Yin, An efficient dictionary learning algorithm and its application to 3-D
medical image denoising. IEEE Trans. Biomed. Eng. 59, 417–427 (2012)
22. A. Wong, A. Mishra, P. Fieguth, D.A. Clausi, Sparse reconstruction of breast mri using homotopic minimization in a regional sparsified domain. IEEE Trans. Biomed. Eng. 60, 743–752
(2013)
23. S. Li, H. Yin, L. Fang, Group-sparse representation with dictionary learning for medical image
denoising and fusion. IEEE Trans. Biomed. Eng. 59, 3450–3459 (2012)