|Quantitative Image Analysis - CNRS URA 2582|
|HEAD||OLIVO-MARIN Jean-Christophe / email@example.com|
|MEMBERS||BERLEMONT Sylvain, CHENOUARD Nicolas, Dr DUFOUR Alexandre, LIN Marie-Anne, Dr MEAS-YEDID Vannary, Dr de CHAUMONT Fabrice, DE MORAES MARIM Marcio, Dr TAUBERT Clovis
The scientific project of the Quantitative Image Analysis (QuIA) unit is to develop image analysis and computer vision tools for the processing and quantification of biological images, with a strong focus on multi-channel temporal 3D sequences. Our work over the last years has been centered on developing new algorithms for multi-particle tracking, active contours models, PSF approximations for image deconvolution and colour image analysis. It has resulted in powerful tools for spot detection and counting in real-time imaging of virus and genes, movement and shape analysis in 3D+t microscopy and histological biopsies analysis. These methods and algorithms are made available to biological groups with which we collaborate in a large number of projects.
Multiple spot tracking in 3D+t microscopy
We have developed a method to perform the detection and the tracking of microscopic spots directly on four dimensional (3D+t) image data. It extends our previous work by being able to detect with high accuracy multiple biological objects moving in three-dimensional space and by incorporating the possibility to follow moving spots switching between different dynamics charac-teristics. Our method is based on a two step procedure: first, the objects are detected in the image stacks thanks to a procedure based on a three-dimensional wavelet transform; then the tracking is performed within a Bayesian framework where each object is represented by a state vector evolving according to biologically realistic dynamic models. The main advantage of wavelet-based detection is to be robust to the local variation of contrast and to the imaging noise. The Bayesian tracking allows to predict the new position of a spot knowing its past positions and increases the reliability of the data association step.
Color image analysis in histology
To characterise histopathological images stained by different colorations, we have developed a system to analyse colour images. Images are segmented by a split and merge approach and by colour quantization, to reduce colour classes. We have also defined a criterion to choose the best colour space. This method has been applied successfully to the quantification of interstitial fibrosis quantification in chronic allograft nephropathy of renal biopsy. The proportion of green to total pixels in the biopsy was then calculated and used as an index of interstitial fibrosis. The results are correlated to the values of quantification realised by an expert.
Zhang, B., Zerubia, J., and Olivo-Marin, J.-C. (2007) Gaussian approximations of fluorescence microscope point-spread function models, Applied Optics, 46, 10, pp. 1819-1829
Servais, A., Meas-Yedid, V., Buchler, M., Morelon, E., Olivo-Marin, J.-C., Lebranchu, Y., Legendre, C. and Thervet, E. (2007) Quantification of interstitial fibrosis by image analysis on routine renal biopsy in patients receiving cyclosporine, Transplantation, 84, 12, pp. 1595-1601
Arhel, N., Genovesio, A., Kim, K-A., Miko, S., Perret, E., Olivo-Marin, J.-C., Shorte, S. and Charneau, P. (2006) Quantitative four-dimensional tracking of cytoplasmic and nuclear HIV-1 complexes, Nature Methods, 3, 10, pp. 817-824
Genovesio, A., Liedl, T., Emiliani, V., Parak, W., Coppey-Moisan, M. and Olivo-Marin, J.-C. (2006) Multiple particle tracking in 3D+t microscopy : method and application to the tracking of endocytozed Quantum Dots, IEEE Trans. Image Processing, 15, 5, pp. 1062-1070
Cabal, G., Genovesio, A., Rodriguez-Navarro, S., Zimmer, C., Gadal, O., Lesne, A., Buc, H., Feuerbach-Fournier, F., Olivo-Marin, J.-C., Hurt, E.C., and Nehrbass, U. (2006) SAGA interacting factors confine sub-diffusion of transcribed genes to the nuclear envelope, Nature, 441, pp. 770-773
Zimmer, C. and Olivo-Marin, J.-C (2005) Coupled parametric active contours, IEEE Trans. Pattern Analysis and Machine Intelligence, 27, 11, pp. 1838-1842
Activity Reports 2007 - Institut Pasteur
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