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3-D optical fluorescent microscopy now becomes an efficient tool for the volume investigation of living biological samples. Developments in instrumentation have permitted to beat off the conventional Abbe limit. In any case the recorded image can be described by the convolution equation between the original object and the Point Spread Function (PSF) of the acquisition system. Due to the finite resolution of the instrument, the original object is recorded with distortions and blurring, and contaminated by noise. This induces that relevant biological information cannot be extracted directly from raw data stacks. If the goal is 3-D quantitative analysis, then to assess optimal performance of the instrument and to ensure the data acquisition reproducibility, the system characterization is mandatory. The PSF represents the properties of the image acquisition system; we have proposed the use of statistical tools and Zernike moments to describe a 3-D PSF system and to quantify the variation of the PSF. This first step toward standardization is helpful to define an acquisition protocol optimizing exploitation of the microscope depending on the studied biological sample. Before the extraction of geometrical information and/or intensities quantification, the data restoration is mandatory. Reduction of out-of-focus light is carried out computationally by deconvolution process. But other phenomena occur during acquisition, like fluorescence photo degradation named "bleaching", inducing an alteration of information needed for restoration. Therefore, we have developed a protocol to pre-process data before the application of deconvolution algorithms. A large number of deconvolution methods have been described and are now available in commercial package. One major difficulty to use this software is the introduction by the user of the "best" regularization parameters. We have pointed out that automating the choice of the regularization level; also greatly improves the reliability of the measurements although it facilitates the use. Furthermore, to increase the quality and the repeatability of quantitative measurements a pre-filtering of images improves the stability of deconvolution process. In the same way, the PSF prefiltering stabilizes the deconvolution process. We have shown that Zemike polynomials can be used to reconstruct experimental PSF, preserving system characteristics and removing the noise contained in the PSF.

Split Spectrum Processing (SSP) improves the detection of ultrasonic echoes. Generally, this method uses an algorithm named minimization. One limitation of this processing is the fact that the spectrum of echoes must be known and invariant. That restricts the field of SSP when ultrasound waves are attenuated. In this work, we propose a new scheme for the minimization process, allowing the ultrasonic investigation in absorbing material. We give an example of this method applied to a thermal spray polymer.

International audience; 3-D optical fluorescent microscopy becomes nowadays an efficient tool for volumic investigation of living biological samples. Using optical sectioning technique, a stack of 2-D images is obtained. However, due to the nature of the system optical transfer function and non-optimal experimental conditions, acquired raw data usually suffer from some distortions. In order to carry out biological analysis, raw data have to be restored by deconvolution. The system identification by the point-spread function is useful to obtain the knowledge of the actual system and experimental parameters, which is necessary to restore raw data. It is furthermore helpful to precise the experimental protocol. In order to facilitate the use of image processing techniques, a multi-platform-compatible software package called VIEW3D has been developed. It integrates a set of tools for the analysis of fluorescence images from 3-D wide-field or confocal microscopy. A number of regularisation parameters for data restoration are determined automatically. Common geometrical measurements and morphological descriptors of fluorescent sites are also implemented to facilitate the characterisation of biological samples. An example of this method concerning cytogenetics is presented.

International audience; In this paper a fusion algorithm for the detection of specular echoes blurred by coloured Gaussian noise is proposed. The phase and energy criteria, characteristic of these echoes, are handled separately. First, the detection capabilities of split spectrum (phase) and short time Fourier transform spectrogram (energy) are compared theoretically and experimentally. Second, the new algorithm based on mathematical morphology operators using both time-frequency representations is proposed. In the last part our method is shown to be more robust than absolute minimisation split spectrum processing.

International audience; Except for blind methods, deconvolution of 3-D data sets acquired from a fluorescence microscope requires the knowledge of the point spread (PSF) of the instrument. Unsing the XCOSM package, we show first with simulations and then with recorded data that it is possible to recover from an experimental PSF some parameters, which are very difficult or impossible to measure during the acquisition, like the specimen depth or the immersion medium refractive index. Doing so, we can precise the acquisition protocol, which helps to use the instrument under optimal conditions. Furthermore, the knowledge of the actual acquisition condtions permits to use fo the deconvolution process a computed PSF, which is noiseless and as close as possible to the actual PSF. This helps to reduce errors in quantitative measurements after deconvolution, as shown with computations.

International audience; A split spectrum processing technique using a novel moving bandwidth minimization (MBM) method was developed to detect multiple specular targets having different spectral characteristics. Mathematical morphology (MM) algorithms were also implemented in order to compare the results. An experimental approach to optimal parameter determination is described. These non-linear filtering methods are applied to medical in vivo imaging to illustrate specular detection and signal to noise ratio (SNR) enhancement.