DocumentCode :
1529500
Title :
Convex Total Variation Denoising of Poisson Fluorescence Confocal Images With Anisotropic Filtering
Author :
Rodrigues, Isabel Cabrita ; Sanches, João Miguel Raposo
Author_Institution :
Inst. for Syst. & Robotic, Lisbon, Portugal
Volume :
20
Issue :
1
fYear :
2011
Firstpage :
146
Lastpage :
160
Abstract :
Fluorescence confocal microscopy (FCM) is now one of the most important tools in biomedicine research. In fact, it makes it possible to accurately study the dynamic processes occurring inside the cell and its nucleus by following the motion of fluorescent molecules over time. Due to the small amount of acquired radiation and the huge optical and electronics amplification, the FCM images are usually corrupted by a severe type of Poisson noise. This noise may be even more damaging when very low intensity incident radiation is used to avoid phototoxicity. In this paper, a Bayesian algorithm is proposed to remove the Poisson intensity dependent noise corrupting the FCM image sequences. The observations are organized in a 3-D tensor where each plane is one of the images acquired along the time of a cell nucleus using the fluorescence loss in photobleaching (FLIP) technique. The method removes simultaneously the noise by considering different spatial and temporal correlations. This is accomplished by using an anisotropic 3-D filter that may be separately tuned in space and in time dimensions. Tests using synthetic and real data are described and presented to illustrate the application of the algorithm. A comparison with several state-of-the-art algorithms is also presented.
Keywords :
Bayes methods; filtering theory; image denoising; image sequences; medical image processing; stochastic processes; 3D tensor; Bayesian algorithm; FCM image sequences; Poisson fluorescence confocal images; Poisson noise; anisotropic filtering; cell nucleus; convex total variation denoising; fluorescence confocal microscopy; fluorescence loss in photobleaching; fluorescent molecules; incident radiation; Anisotropic filters; Bayesian methods; Biomedical optical imaging; Fluorescence; Image sequences; Microscopy; Noise reduction; Optical filters; Optical noise; Stimulated emission; Bayesian; Poisson; convex optimization; denoising; laser scanning confocal fluorescence microscopy (LSCFM); Algorithms; Bayes Theorem; Cell Nucleus; Computer Simulation; Cytological Techniques; Fluorescence Polarization; Hela Cells; Humans; Image Processing, Computer-Assisted; Microscopy, Confocal; Photobleaching; Poisson Distribution; Reproducibility of Results;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2010.2055879
Filename :
5504217
Link To Document :
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