A CANDLE for a deeper in vivo insight

Medical Image Analysis

Volumn 16, Issue 4, 2012, Pages 849-864

  Coupé, Pierrick ^a,b,c   Munz, Martin ^c   Manjón, Jose V ^d   Ruthazer, Edward S ^c   Louis Collins, D ^b,c  

^a UNIVERSITÉ BORDEAUX 1   (France)

^b MCGILL UNIVERSITY   (Canada)

^c MCGILL UNIVERSITY   (Canada)

^d UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

Author keywords

Confocal imaging; Image denoising; Image enhancement; Multiphoton imaging; Non local means filter

Indexed keywords

3D LASER SCANNING; BIOLOGICAL SPECIMENS; CANDLE FILTERS; COLLABORATIVE APPROACH; CONFOCAL IMAGING; DE-NOISING; IMAGING CAPABILITIES; IN-VIVO; IN-VIVO IMAGES; INHOMOGENEITIES; LASER SCANNING FLUORESCENCE MICROSCOPY; LOW SIGNAL-TO-NOISE RATIO; LOW SNR; MULTI-PHOTON MICROSCOPY; MULTIPHOTON IMAGING; NONLOCAL; STATE-OF-THE-ART METHODS; SYNTHETIC DATA; XENOPUS;

FLUORESCENCE MICROSCOPY; IMAGE ENHANCEMENT; OPTIMIZATION; TISSUE;

IMAGE DENOISING;

ARTICLE; BRAIN; COLLABORATIVE APPROACH FOR ENHANCED DENOISING UNDER LOW LIGHT EXCITATION; CONFOCAL LASER MICROSCOPY; FILTER; FLUORESCENCE MICROSCOPY; IMAGE ANALYSIS; IMAGE DENOISING; IMAGE PROCESSING; LASER SCANNING MULTIPHOTON MICROSCOPY; MULTIPHOTON MICROSCOPY; NERVE CELL; NERVE FIBER; NOISE REDUCTION; NONHUMAN; PRIORITY JOURNAL; SIGNAL NOISE RATIO; SIGNAL PROCESSING; THREE DIMENSIONAL IMAGING; XENOPUS;

ALGORITHMS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; LIGHTING; MICROSCOPY, CONFOCAL; MICROSCOPY, FLUORESCENCE, MULTIPHOTON; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84859421304     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2012.01.002     Document Type: Article

References (48)

1. Anscombe F.J. The transformation of Poisson, binomial and negative-binomial data. Biometrika 1948, 35:246-254.
2. Arce G.R. Nonlinear Signal Processing: A Statistical Approach 2005, Wiley, Hoboken, Chichester.
3. Baker M. Seeing more with less. Nat. Methods 2010, 7:782.
4. Boulanger J., Kervrann C., Bouthemy P., Elbau P., Sibarita J.B., Salamero J. Patch-based nonlocal functional for denoising fluorescence microscopy image sequences. IEEE Trans. Med. Imaging 2010, 29:442-454.
5. Broser, P.J., Schulte, R., Roth, A., Helmehen, F., Waters, J., Lang, S., Sakmann, B., Wittum, G., 2005. Nonlinear anisotropic diffusion filtering of three-dimensional image data from 2-photon microscopy. Image Processing: Algorithms and Systems IV, vol. 5672, pp. 44-69.
6. Buades, A., Coll, B., Morel, J.M., 2005. A non-local algorithm for image denoising. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 60-65.
7. Buades A., Coll B., Morel J.M. Image denoising methods. A new nonlocal principle. SIAM Rev. 2010, 52:113-147.
8. Carlton P.M., Boulanger J., Kervrann C., Sibarita J.B., Salamero J., Gordon-Messer S., Bressan D., Haber J.E., Haase S., Shao L., Winoto L., Matsuda A., Kner P., Uzawa S., Gustafsson M., Kam Z., Agard D.A., Sedat J.W. Fast live simultaneous multiwavelength four-dimensional optical microscopy. Proc. Natl. Acad. Sci. USA 2010, 107:16016-16022.
9. Chan C.L., Katsaggelos A.K., Sahakian A.V. Image sequence filtering in quantum-limited noise with applications to low-dose fluoroscopy. IEEE Trans. Med. Imaging 1993, 12:610-621.
10. Coupe P., Hellier P., Kervrann C., Barillot C. Nonlocal means-based speckle filtering for ultrasound images. IEEE Trans. Image Process. 2009, 18:2221-2229.
11. Coupe P., Yger P., Prima S., Hellier P., Kervrann C., Barillot C. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans. Med. Imaging 2008, 27:425-441.
12. Cui M. A high speed wavefront determination method based on spatial frequency modulations for focusing light through random scattering media. Opt. Express 2011, 19(4):2989-2995.
13. Dabov K., Foi A., Katkovnik V., Egiazarian K. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 2007, 16:2080-2095.
14. de Deckerk, A., Lee, J.A., Verlysen, M., 2009. Variance stabilizing transformations in patch-based bilateral filters for Poisson noise image denoising. In: Conference Proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society, pp. 3673-3676.
15. Deledalle C.A., Denis L., Tupin F. Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Trans. Image Process. 2009, 18:2661-2672.
16. Deledalle, C.A., Tupin, F., Denis, L., 2010. Poisson NL-means: unsupervised non local means for Poisson noise. In: IEEE 17th International Conference on Image Processing, pp. 26-29.
17. Delpretti, S., Luisier, F., Ramani, S., Blu, T., Unser, M., 2008. Multiframe SURE-LET denoising of timelapse fluorescence microscopy images. In; 2008 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, vols. 1-4, pp. 149-152.
18. Dima A., Scholz M., Obermayer K. Automatic segmentation and skeletonization of neurons from confocal microscopy images based on the 3-D wavelet transform. IEEE Trans. Image Process. 2002, 11:790-801.
19. Dodt U., Leischner U., Shierloh A., Jahrling N., Mauch C.P., Deininger K., Deussing J.M., Eder M. Ultramicroscopy: three dimensional visualization of neuronal networks in the whole mouse brain. Nat. Methods 2007, 4:331-336.
20. Donoho D.L. De-noising by soft-thresholding. IEEE Trans. Inf. Theory 1995, 41:613-627.
21. Fryzlewicz P., Nason G.P. A Haar-Fisz algorithm for Poisson intensity estimation. J. Comput. Graph. Stat. 2004, 13:621-638.
22. Fudenberg G., Paninski L. Bayesian image recovery for dendritic structures under low signal-to-noise conditions. IEEE Trans. Image Process.: Publ. IEEE Signal Process. Soc. 2009, 18:471-482.
23. Husson T.R., Mallik A.K., Zhang J.X., Issa N.P. Functional imaging of primary visual cortex using flavoprotein autofluorescence. J. Neurosci. 2007, 27:8665-8675.
24. Kervrann C., Boulanger J. Local adaptivity to variable smoothness for exemplar-based image regularization and representation. Int. J. Comput. Vision 2008, 79:45-69.
25. Kervrann, C., Trubuil, A., 2004. An adaptive window approach for Poisson noise reduction and structure preserving in confocal microscopy. In: IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2004, pp. 788-791.
26. Kobat D., Durst M.E., Nishimura N., Wong A.W., Schaffer C.B., Xu C. Deep tissue multiphoton microscopy using longer wavelength excitation. Opt. Express 2009, 17:13354-13364.
27. Lefkimmiatis S., Maragos P., Papandreou G. Bayesian inference on multiscale models for Poisson intensity estimation: applications to photon-limited image denoising. IEEE Trans. Image Process. 2009, 18:1724-1741.
28. Luisier F., Vonesch C., Blu T., Unser M. Fast interscale wavelet denoising of Poisson-corrupted images. Signal Process. 2010, 90:415-427.
29. Luisier F., Blu T., Unser M. Image Denoising in Mixed Poisson-Gaussian Noise. IEEE Trans Image Process 2011, 20:696-708.
30. Mairal J., Elad M., Sapiro G. Sparse representation for color image restoration. IEEE Trans. Image Process. 2008, 17:53-69.
31. Makitalo M., Foi A. Optimal inversion of the Anscombe transformation in low-count Poisson image denoising. IEEE Trans. Image Process. 2011, 20:99-109.
32. Manjon J.V., Coupe P., Marti-Bonmati L., Collins D.L., Robles M. Adaptive non-local means denoising of MR images with spatially varying noise levels. J. Magn. Reson. Imaging 2010, 31:192-203.
33. Murtagh F., Starck J.L., Bijaoui A. Image-restoration with noise suppression using a multiresolution support. Astron. Astrophys. Suppl. Ser. 1995, 112:179-189.
34. Nowak R.D., Baraniuk R.G. Wavelet-domain filtering for photon imaging systems. IEEE Trans. Image Process. 1999, 8:666-678.
35. Rice W.L., Kaplan D.L., Georgakoudi I. Two-photon microscopy for non-invasive, quantitative monitoring of stem cell differentiation. PLoS ONE 2010, 5:e10075.
36. Rodrigues, I., Sanches, J., 2009. Fluorescence microscopy imaging denoising with log-Euclidean Priors and photobleaching compensation. 16th IEEE International Conference on Image Processing, vols. 1-6, pp. 809-812.
37. Santamaria-Pang A., Bildea T.S., Tan S., Kakadiaris I.A. Denoising for 3-d photon-limited imaging data using nonseparable filterbanks. IEEE Trans. Image Process. 2008, 17:2312-2323.
38. Shibuki K., Hishida R., Murakami H., Kudoh M., Kawaguchi T., Watanabe M., Watanabe S., Kouuchi T., Tanaka R. Dynamic imaging of somatosensory cortical activity in the rat visualized by flavoprotein autofluorescence. J. Physiol. 2003, 549:919-927.
39. Starck J.L., Murtagh F., Bijaoui A. Image Processing and Data Analysis: The Multiscale Approach 1998, Cambridge University Press, Cambridge.
40. Swedlow J.R. Advanced hardware and software tools for fast multidimensional imaging of living cells. Proc. Natl. Acad. Sci. USA 2010, 107:16005-16006.
41. Theer P., Denk W. On the fundamental imaging-depth limit in two-photon microscopy. J. Opt. Soc. Am. A: Opt. Image Sci. Vis. 2006, 23:3139-3149.
42. Van De Ville D., Kocher M. SURE-based non-local means. IEEE Signal Process. Lett. 2009, 16:973-976.
43. Voie A.H., Burns D.H., Spelman F.A. Orthogonal-plane fluorescence optical sectioning: three dimensional imaging of macroscopic biological specimens. J. Microsc. 1993, 170:229-236.
44. Wang Z., Bovik A.C., Sheikh H.R., Simoncelli E.P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 2004, 13:600-612.
45. Wei D.Y., Yin C.C. An optimized locally adaptive non-local means denoising filter for cryo-electron microscopy data. J. Struct. Biol 2010.
46. Willett R.M., Nowak R.D. Multiscale Poisson intensity and density estimation. IEEE Trans. Inf. Theory 2007, 53:3171-3187.
47. Yang L., Parton R., Ball G., Qiu Z., Greenaway A.H., Davis I., Lu W. An adaptive non-local means filter for denoising live-cell images and improving particle detection. J. Struct. Biol 2010.
48. Zhang B., Fadili J.M., Starck J.L. Wavelets, ridgelets, and curvelets for Poisson noise removal. IEEE Trans. Image Process. 2008, 17:1093-1108.