Unit: Quantitative Image Analysis - URA CNRS 2582
Director: Olivo-Marin Jean-Christophe
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 analysis (C. Zimmer, L. AitAlil, B. Zhang, J.-C. Olivo-Marin)
We have further improved our algorithms based on the active contour approach for tracking and segmenting cells in video microscopy. As a first step, we have extended our tool to the segmentation of fuzzy fluorescent cells by using image region statistics in addition to the intensity gradient. We have then made an important step by adopting the level set methodology in addition to the previously used parametric approach. This technique allows fully automatic processing, because arbitrary numbers of cells can be detected without requiring manual outlining on the first frame. It also enabled us to implement a preliminary tool for the volume segmentation of 3D data (image stacks), but considerable optimizations will be required to make it operational. We have proposed a novel technique of coupling multiple level set functions in order to maintain the identity of touching cells. Finally, we have started an effort to introduce shape information in the active contour segmentation process. In collaboration with the group of Nancy Guillén, we have computed and analyzed the trajectories of amoebae E. histolytica in chemotaxis experiments. These analyses have shown the existence of a chemokinetic effect. In collaboration with F. Frischknecht (BGP, R. Ménard), we have quantified the intermittent motion of Plasmodium falciparum sporozoites inside the salivary duct of mosquitoes.
Tracking of spots in dynamic 3D microscopy images (A. Genovesio, B. Zhang, J.-C. Olivo-Marin)
We have developed a method which allows for the first time to perform the detection and the tracking of microscopic objetcs directly from three dimensional image data. It enables to analyse biological moving objects in three dimensional fluorescence image sequences coming from biological immunomicroscopy experiments, and get quantitative data such as the number of objects, their position, movement phases and speed. After a detection step is performed through the multiscale analysis of images using a shift-invariant wavelet transform, the tracking is achieved using a Kalman filter and an association which enable the position of the moving objects to be predicted, refined and updated. Trajectories are analysed in terms of different parameters relevant for the motility analysis of biological objects.
Spot analysis in immunofluorescence images (B. Zhang, L. Pénard, 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. based on a multiscale approach that uses a shift invariant discrete wavelet transform(SI-DWT) and on the selective filtering of wavelet coefficients. This scheme allows to separate and characterize objects of different sizes by selecting only a vicinity of detail images with corresponding scales adapted to the size of the spots. The extraction step consists in retaining the significant responses of the locally supported detail signal filters to the desired features, at the different scales of the wavelet representation. This is accomplished through a denoising technique using a threshold value which is image and level dependent and which can be computed automatically from the data. The program detects spots both in 2D and in 3D images.
Color image segmentation in cytology (E. Glory, V. Meas-Yedid, J.-C. Olivo-Marin)
To characterize culture parameters of human muscular cells, an automatic tool based on image analysis has been developed. Devices which make up the acquisition system are an inverted microscope, a colour camera and motorized stage and autofocus. Colour images are segmented by a simple algorithm which automatically and objectively quantifies cell number (patented). Data are treated to describe the growth of cellular population (doubling time, size and morphology of nuclei). This tool is already used and more than 75 000 images coming from 400 different culture conditions have been analyzed.
Color image segmentation in histology (V. Meas-Yedid, E. Glory, J.-C. Olivo-Marin)
In order 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 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 two applications: quantification of immune cells in mouse livers and interstitial fibrosis quantification in chronic allograft nephropathy of renal biopsy. In the first application, as the immune cells are stained in red colour, we have quantified the total red area over the total area of the liver. In the second application, the program automatically extracts areas with the green colour class that uniquely characterizes interstitial fibrosis. The system has been designed to automatically eliminate obsolescent glomeruli, renal capsule and basal membrane of tubular cells. 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. This project is part of the PTR (Transverse Research Project) Quantitative image analysis in kidney transplant, performed in collaboration with the "kidney transplant Unit ", (Dr E. Morellon) at the Necker Hospital in Paris.
Keywords: image processing, motility, shape, microscopy, color image analysis, active contours, level sets, wavelets, Kalman filter, probabilistic data association filters