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 colour image analysis.

Shape and motion analysis

(C. Zimmer, A. Dufour, S. Berlemont, J.-C. Olivo-Marin)

We have continued our efforts to adapt active contour models to the needs of biological image processing, and have concentrated our efforts to coupled parametric active contours, 3D coupled active surfaces and quasi-circular object detection with level sets. Standard parametric active contour methods are suited to tracking individual isolated objects, but fail to track multiple objects whenever these come close to each other. This is a major limitation for tracking cells, which undergo frequent contacts. In previous work we have proposed a solution based on coupled non-parametric (level set) active contours, which helps to track touching cells, but is too slow for processing large image sequences. We have now introduced the idea of coupling active contours within the parametric active contour approach. This allows to combine the ability to track touching cells with the computational efficiency of parametric active contours. In addition, we now use a significantly more robust scheme to define cell outlines, enabling segmentation of fuzzy cells in low signal fluorescence microscopy.

Cell motility and morphology are increasingly studied in 3D natural or artificial environments. In order to track and segment cells from 3D+time image data, we have developed a method based on coupled active surfaces. This technique extends our previous work from 2D to 3D and takes advantage of cell volume homeostasis to further improve the handling of touching cells.

In images presenting large variability of intensity, color or textures, it is helpful or necessary to rely on shape in order to correctly detect and outline significant objects (e.g. cells, or nodules in histology images). Shape constraints can be incorporated into active contour attached to single objects, but extending this to an unknown number of objects is a difficult and unresolved goal. We have made a step in this direction for the special but important case of nearly circular objects. The proposed, fully automatic scheme, allows better identification of round structures in histology images where color and texture information are unreliable.

All these topics continue to find applications in collaboration projects with S. Blazquez and E. Labruyère (BCP, N. Guillén) and V. Shinin (CSD, S. Tajbaksh).

Tracking of spots in 4D microscopy images

(A. Genovesio, F. Ollivier, J.-C. Olivo-Marin)

The study of cell and pathogen motility in biology requires computerised methods to enable objective quantitative analysis of large amounts of data. We have developed a method to detect and track multiple moving biological objects showing different kind of dynamics in image sequences acquired through fluorescence video microscopy. It allows for the first time to perform the detection and the tracking of microscopic objects directly from three dimensional image data. It enables the extraction and analysis of information such as number, position, speed, movement and diffusion phases of, e.g., endosomal and viral particles. The method consists of several stages. After a detection stage performed by an undecimated wavelet transform, we compute, for each detected spot, several predictions of its future state in the next frame. This is accomplished thanks to an algorithm which includes several models corresponding to different movement types. Tracks are constructed thereafter by a data association algorithm based on the maximisation of the likelihood of each filter. The last stage consists of updating the filters in order to compute final estimations for the present image and to improve predictions for the next one. Trajectories are analysed in terms of different parameters relevant for the motility analysis of biological objects. The performances of the method have been validated on synthetic image data and on image sequences in collaboration with several biological groups.

Spot analysis in immunofluorescence images

(A. Genovesio, B. Zhang, 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 and is used by several biological scientists to quantify their images.

Color image segmentation in histology and cytology

(V. Meas-Yedid, E. Glory, J.-C. Olivo-Marin)

Due to the improvement of devices, the number of color biological images that need to be quantified increases every day and the automation of their analyse is required. For each application, a segmentation method has to be chosen to provide the most relevant quantitative information. The difficulty of working with microscopic color images is that results strongly depend on the selected color spaces. We have tested criteria designed by Liu and Borsotti to automatically evaluate the quality of a color segmentation. As they do not correctly solve our microscopic image problems, we have proposed two modified criteria adapted to two different biological applications. Penalizing inhomogeneity, numerous small regions and misclassified regions, our modified criteria help to select the segmentation method and the color space adapted to speficied biological goals. These criteria have been applied successfully to histological and cytological problems.

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 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 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 was 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 ans is now being extended to new clinical studies.

To characterize culture parameters of human muscular cells, an automatic tool based on image analysis has been developed. The acquisition system consists in an inverted microscope, a colour camera and a motorized stage with autofocus. Colour images are segmented by a patented algorithm which automatically and objectively quantifies cell number. Data are analysed to characterise the growth of cellular population (doubling time, size and morphology of nuclei). This tool is in use in collaboration with the start-up company Cellogos.

Keywords: image processing, motility, shape, microscopy, color image analysis, active contours, level sets, wavelets, Kalman filter, probabilistic data association filters

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