New variational image decomposition model for simultaneously denoising and segmenting optical coherence tomography images

Physics in Medicine and Biology

Volumn 60, Issue 22, 2015, Pages 8901-8922

  Duan, Jinming ^a   Tench, Christopher ^a   Gottlob, Irene ^b   Proudlock, Frank ^b   Bai, Li ^a  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^b UNIVERSITY OF LEICESTER   (United Kingdom)

Author keywords

fast Fourier transform; image decomposition; image segmentation; noise removal; Optical coherence tomography; split Bregman algorithm; variational methods

Indexed keywords

COMPUTATIONAL EFFICIENCY; DIAGNOSIS; FAST FOURIER TRANSFORMS; IMAGE SEGMENTATION; NOISE ABATEMENT; OPHTHALMOLOGY; OPTICAL TOMOGRAPHY;

AUTOMATED ANALYSIS; IMAGE DECOMPOSITION; IMAGE DECOMPOSITION MODELS; NOISE REMOVAL; OSCILLATORY PATTERNS; SPECKLE NOISE REDUCTION; SPLIT BREGMAN ALGORITHMS; VARIATIONAL METHODS;

IMAGE DENOISING;

ADULT; ALGORITHM; ANATOMY AND HISTOLOGY; COMPUTER ASSISTED DIAGNOSIS; FEMALE; HUMAN; INFRARED SPECTROSCOPY; MALE; OPTICAL COHERENCE TOMOGRAPHY; PROCEDURES; RADIOGRAPHY; RETINA; SIGNAL NOISE RATIO; SIGNAL PROCESSING; THEORETICAL MODEL;

ADULT; ALGORITHMS; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; MODELS, THEORETICAL; RETINA; SIGNAL PROCESSING, COMPUTER-ASSISTED; SIGNAL-TO-NOISE RATIO; SPECTROSCOPY, FOURIER TRANSFORM INFRARED; TOMOGRAPHY, OPTICAL COHERENCE;

EID: 84946602099     PISSN: 00319155     EISSN: 13616560     Source Type: Journal    
DOI: 10.1088/0031-9155/60/22/8901     Document Type: Article

References (30)

1. Adler D C, Ko T H and Fujimoto J G 2004 Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter Opt. Lett. 29 2878-80
2. Ambrosio L and Tortorelli V M 1990 Approximation of functional depending on jumps by elliptic functional via t-convergence Commun. Pure Appl. Math. 43 999-1036
3. Aujol J F, Gilboa G, Chan T and Osher S 2006 Structure-texture image decomposition-modeling, algorithms, and parameter selection Int. J. Comput. Vision 67 111-36
4. Bashkansky M and Reintjes J 2000 Statistics and reduction of speckle in optical coherence tomography Opt. Lett. 25 545-547
5. Bernardes R, Maduro C, Serranho P, Araújo A, Barbeiro S and Cunha-Vaz J 2010 Improved adaptive complex diffusion despeckling filter Opt. Express 18 24048-59
6. Bredies K, Kunisch K and Pock T 2010 Total generalized variation SIAM J. Imaging Sci. 3 492-526
7. Cabrera Fernández D, Salinas H M and Puliafito C A 2005 Automated detection of retinal layer structures on optical coherence tomography images Opt. Express 13 10200-16
8. Chitchian S, Fiddy M A and Fried N M 2009 Denoising during optical coherence tomography of the prostate nerves via wavelet shrinkage using dual-tree complex wavelet transform J. Biomed. Opt. 14 014031
9. Drexler W and Fujimoto J G 2008 Optical Coherence Tomography: Technology and Applications (New York: Springer)
10. Drexler W, Morgner U, Ghanta R K, Kärtner F X, Schuman J S and Fujimoto J G 2001 Ultrahigh-resolution ophthalmic optical coherence tomography Nat. Med. 7 502-7
11. Gilboa G, Sochen N and Zeevi Y Y 2004 Image enhancement and denoising by complex diffusion processes IEEE Trans. Pattern Anal. Mach. Intell. 26 1020-36
12. Goldstein T and Osher S 2009 The split bregman method for l1-regularized problems SIAM J. Imaging Sci. 2 323-43
13. Lee H, Sheth V, Bibi M, Maconachie G, Patel A, McLean R, Michaelides M, Thomas M, Proudlock F and Gottlob I 2013 Potential of handheld optical coherence tomography to determine cause of infantile nystagmus in children by using foveal morphology Ophthalmology 120 2714-24
14. Mayer M A, Borsdorf A, Wagner M, Hornegger J, Mardin C Y and Tornow R P 2012 Wavelet denoising of multiframe optical coherence tomography data Biomed. Opt. Express 3 572-89
15. Nieminen A, Heinonen P and Neuvo Y 1987 A new class of detail-preserving filters for image processing IEEE Trans. Pattern Anal. Mach. Intell. 9 74-90
16. Ozcan A, Bilenca A, Desjardins A E, Bouma B E and Tearney G J 2007 Speckle reduction in optical coherence tomography images using digital filtering J. Opt. Soc. Am. A 24 1901-10
17. Pircher M et al 2003 Speckle reduction in optical coherence tomography by frequency compounding J. Biomed. Opt. 8 565-9
18. Puvanathasan P and Bizheva K 2007 Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set Opt. Express 15 15747-58
19. Puvanathasan P and Bizheva K 2009 Interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in optical coherence tomography images Opt. Express 17 733-46
20. Rodrigues P and Bernardes R 2012 3D adaptive nonlinear complex-diffusion despeckling filter IEEE Trans. Med. Imaging 31 2205-12
21. Rudin L I, Osher S and Fatemi E 1992 Nonlinear total variation based noise removal algorithms Phys. D: Nonlinear Phenom. 60 259-68
22. Salinas H M and Fernández D C 2007 Comparison of PDE-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography IEEE Trans. Med. Imaging 26 761-71
23. Schmitt J M, Xiang S and Yung K M 1999 Speckle in optical coherence tomography J. Biomed. Opt. 4 95-105
24. Tai X C, Hahn J and Chung G J 2011 A fast algorithm for Euler's elastica model using augmented lagrangian method SIAM J. Imaging Sci. 4 313-44
25. Tai X C 2013 Fast numerical schemes related to curvature minimization: a brief and elementary review 14-40 UCLA CAM Report
26. Vese L A and Osher S J 2003 Modeling textures with total variation minimization and oscillating patterns in image processing J. Sci. Comput. 19 553-72
27. Wang Z, Bovik A C, Sheikh H R and Simoncelli E P 2004 Image quality assessment: from error visibility to structural similarity IEEE Trans. Image Process. 13 600-12
28. Weickert J 1998 Anisotropic Diffusion in Image Processing vol 1 (Stuttgart: Teubner)
29. Yang J, Yin W, Zhang Y and Wang Y 2009 A fast algorithm for edge-preserving variational multichannel image restoration SIAM J. Imaging Sci. 2 569-92
30. Zhu W, Tai X C and Chan T 2013 Augmented lagrangian method for a mean curvature based image denoising model Inverse Problems Imaging 7 1409-32