|PDF Version||Quantitative Image Analysis - URA CNRS non encore connue|
|Director : Olivo-Marin Jean-Christophe (email@example.com)|
The Quantitative Image Analysis Unit develops image processing methods and programs for the automatic analysis and quantitation of microscopic images. Our main research topics include dynamic object segmentation, spot and particle tracking in dynamic microscopy, fluorescence quantification and color image analysis.
Shape and motion analysi (C. Zimmer, E. Glory, V. Meas-Yedid, J.-C. Olivo-Marin)
We are adapting the approach of active contours for the segmentation and tracking of deformable objects in motion from large image sequences. In this model, the object outlines are obtained from a parametric curve, by minimising an energy functional that depends on the curve's geometry and on the image data in its neighbourhood. We use the non-potential force field provided by the gradient vector flow model, which reduces the sensitivity of segmentation to initial conditions. Combined with a dilation of the initial contours, this modification increases convergence robustness. However, the lack of control on the topology changes in this approach often leads to undesirable contour fusion when previously distinct objects enter in close contact. To overcome this limitation, we have proposed to modulate the image by a ``ridge'' which discourages contour motion towards neighbouring objects, thus inhibiting contour fusion. This project is part of the PTR Study of celllular polarity by image analysis. The targeted applications are: (i) the study of motility and morphology changes in amoeba, in collaboration with the "Unité Pathogénie Microbienne Moléculaire" and (ii) the quantitative analysis of morphological changes undergone by T lymphocytes in the early stages of the immune response, in collaboration with the "Unité Biologie des Interactions Cellulaires".
Tracking of spots in dynamic microscopy images (A. Genovesio, B. Zhang, J.-C. Olivo-Marin)
Our goal is to detect and to track multiple moving biological objects in image sequences acquired through fluorescence video microscopy. The method that we developed enables the analysis of video microscopy image sequences in order to obtain reliable quantitative data such as number, position, speed and movement phases. The method consists of three stages: i) object detection; ii) prediction of the state of each detected spot in the next frame using a Kalman filter and an adapted model; iii) data association which constructs the tracks and refines the filters.
Spot analysis in immunofluorescence images (B. Zhang, A. Genovesio, J.-C. Olivo-Marin)
Automatic quantification of immunofluorescence images relies either on the detection and counting of spots superimposed on biological structures, usually immersed in a non-uniform background, or on the outlining of larger cellular compartments. We have developed methods for spot detection and characterisation that allow a fast and reproducible quantitative analysis of these images. From an algorithmic point of view, the problem of spot detection is seen as a generation-recombination process of multi-resolution response elements obtained from a wavelet representation of the image. In the generation step, our algorithm retains at each resolution level the significant responses of a compact support detail filter only, followed by adaptive thresholding. In the recombination step, a local correlation coefficient is computed from the filtered wavelet coefficients at each location in the image. This method detects spots both in 2D and in 3D images.
Quantification of multi-modal images by active contours (V. Meas-Yedid, E. Glory, C. Zimmer, F. Cloppet, G. Stamon, J.-C. Olivo-Marin)
Our purpose is to develop automatic or semi-automatic tools able to characterise the morphological changes of cells and to quantify the cytoskeleton molecules involved in cell interactions. The algorithm draws on several complementary information sources provided by different modes of microscopy, specifically fluorescence and phase contrast. In the first step, cell outlines are extracted from a phase contrast image with the help of active contours, thus providing a description of their morphology. In the second step, the fluorescence image is used to quantify cytoskeleton molecules in specific compartments defined by the contours extracted in the first step, via a sequence of thresholdings and Boolean operators. This project is part of the PTR (Transverse Research Project) Study of celllular polarity by image analysis, performed in collaboration with the "Unité de Biologie des Interactions Cellulaires" and the "Laboratoire des Systèmes Intelligents de Perception" (SIP) of University Paris V.
Keywords: image processing, motility, shape, microscopy, color image analysis, active contours, wavelets, Kalman filter
|Publications of the unit on Pasteur's references database|
|Office staff||Researchers||Scientific trainees||Other personnel|
|Isabelle DULIEU, IP,firstname.lastname@example.org||Christophe ZIMMER, post-doc,email@example.com||Estelle GLORY, Doctorante
Auguste GENOVESIO, Doctorant
Bo ZHANG, ENST
Marion FERAL, DEA/ENST
|Vannary MEAS-YEDID, Ingénieur IP,firstname.lastname@example.org|