Automatic quality assessment for fluorescence microscopy images

8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008

Volumn , Issue , 2008, Pages

  Paul, Perrine ^a   Kalamatianos, Dimitrios ^a   Duessmann, Heiko ^b   Huber, Heinrich ^b  

^a NATIONAL UNIVERSITY OF IRELAND   (Ireland)

^b ROYAL COLLEGE OF SURGEONS IN IRELAND   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC METHOD; EXPERIMENTAL DATA; FLUORESCENCE MICROSCOPY IMAGES; FLUORESCENCE MICROSCOPY IMAGING; INDEPENDENT NOISE; PHOTO-TOXICITY; QUALITY ASSESSMENT; SIMULATED NOISE; SINGLE IMAGES; SPATIO-TEMPORAL RESOLUTION; TRADE OFF;

BIOINFORMATICS; FLUORESCENCE; FLUORESCENCE MICROSCOPY; IMAGE ACQUISITION; IMAGE QUALITY;

SIGNAL TO NOISE RATIO;

EID: 67549099967     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBE.2008.4696665     Document Type: Conference Paper

References (16)

1. LSM 510: Eliminating Crosstalk by Multitracking. ZEISS, micro info 40-520 e/10.99. available from wvm.zeiss.com http://wvm.zeiss.com.
2. M.D. Adams. Jasper software reference manual (version 1.900.0). Technical report, ISO/IEC JTC 1/SC 29/WG IN 2415, 2006.
3. A. Amer and E. Dubois. Fast and reliable structure-oriented video noise estimation. IEEE Trans. Circuits Syst. Video Technol, 15(1): 113-18, 2005.
4. T. Bernas, E.K. Asem, J.P. Robinson, and B. Rajwa. Compression of fluorescence microscopy images based on the signal-to-noise estimation. Microsc. Res. Tech., 69:1-9, 2006.
5. T. Bernas, D. Barnes, E.K. Asem, Robinson J.P., and B. Rajwa. Precision of light intensity measurement in biological optical microscopy. Journal of Microscopy, 226:163-74, 2007.
6. T.F. Coleman and A. Verma. A preconditioned conjugate gradient approach to linear equality constrained minimization. Computational Optimization and Applications, 20(l):61-72, 2001.
7. J.F. Dorn, G. Danuser, and G. Yang. Methods in Cell Biology, volume 85, chapter Computational Processing and Analysis of Dynamic Fluorescence Image Data, pages 497-538. Elsevier, 2008.
8. H. Duessmann, M. Rehm, D. Kgel, and J.H.M. Prehn. Outer mitochondrial membrane permeabilization during apoptosis triggers caspase-independent mitochondrial and caspase-dependent plasma membrane potential depolarization: a single-cell analysis. J. Cell Sci., 116(3):525-36, 2003.
9. Y. Garini, V. Gil, I. Bar-Am, D. Cabib, and N. Katzir. Signal to noise analysis of multiple color fluorescence imaging microscopy. Cytometry, 35:214-26, 1999.
10. J.A. Greenwood and M.M. Sandomire. Sample size required for estimating the standard deviation as a per cent of its true value. Journal of the American Statistical Association, 45:257-60, 1950.
11. X. Huang, A.C. Madoc, and A.D. Cheetham. Wavelet-based bayesian estimator for poisson noise removal from images. In IEEE International Conference on Multimedia & Expo, 2003.
12. R.D. Nowak and R.G. Baraniuk. Wavelet-domain filtering for photon imaging systems. IEEE Trans. Image Process., 8:666-78, 1999.
13. D. Sage. SNR: ImageJ plugin to compute the signal-to-noise ratio on images, http://bigwww.epfl.ch/sage/soft/snr/, 29 february 2008.
14. D. Sage, F.R. Neumann, F. Hediger, S.M. Gasser, and M. Unser. Automatic tracking of individual fluorescence particles: Application to the study of chromosome dynamics. IEEE Trans. Image Process., 14(0): 1372-83, 2005.
15. I.F. Sbalzarini and P. Koumoutsakos. Feature point tracking and trajectory analysis for video imaging in cell biology. J. Struct. Biol, 151(2):182-195, 2005.
16. G. van Kempen. Image Restoration in Fluorescence Microscopy. PhD thesis, Delft University, 1999.