Dr. Bruno Colicchio Profile

Dr. Bruno Colicchio

at Univ de Haute Alsace

SPIE Involvement:

Author

Publications (10)

Proceedings Article | 30 March 2020 Presentation + Paper

Mirror-effect based tomographic diffraction microscopy

Nicolas Verrier, Ludovic Foucault, Matthieu Debailleul, Jean-Baptiste Courbot, Bruno Colicchio, Bertrand Simon, Laurent Vonna, Olivier Haeberlé

Proceedings Volume 11351, 1135106 (2020) https://doi.org/10.1117/12.2559200

KEYWORDS: Microscopy, Tomography, Diffraction

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Proceedings Article | 14 May 2017 Paper

Interests of refocused images calibrated in depth with a multi-view camera for control by vision

Cécile Riou, Bruno Colicchio, Jean-Philippe Lauffenburger, Christophe Cudel

Proceedings Volume 10338, 1033807 (2017) https://doi.org/10.1117/12.2264237

KEYWORDS: Imaging systems, Machine vision, Image sensors, Coded apertures, Fourier transforms, Fermium, Frequency modulation, Calibration, Cameras, Measurement devices

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Proceedings Article | 29 April 2016 Paper

3D high- and isotropic resolution in tomographic diffractive microscopy by illumination angular scanning, specimen rotation and improved data recombination

Jonathan Bailleul, Bertrand Simon, Bruno Colicchio, Matthieu Debailleul, Olivier Haeberlé

Proceedings Volume 9896, 98960M (2016) https://doi.org/10.1117/12.2227058

KEYWORDS: Image resolution, Time division multiplexing, Spatial resolution, 3D image processing, Image resolution, 3D acquisition, Digital holography, Microscopes, 3D displays

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SPIE Journal Paper | 22 December 2015

Calibration and disparity maps for a depth camera based on a four-lens device

Cécile Riou, Bruno Colicchio, Jean-Philippe Lauffenburger, Olivier Haeberlé, Christophe Cudel

JEI, Vol. 24, Issue 06, 061108, (December 2015) https://doi.org/10.1117/12.10.1117/1.JEI.24.6.061108

KEYWORDS: Calibration, Cameras, Image fusion, 3D metrology, Lawrencium, Lenses, Distortion, Image processing, Instrument modeling, Detection and tracking algorithms

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Proceedings Article | 30 April 2015 Paper

A four-lens based plenoptic camera for depth measurements

Cécile Riou, Zhiyuan Deng, Bruno Colicchio, Jean-Philippe Lauffenburger, Sophie Kohler, Olivier Haeberlé, Christophe Cudel

Proceedings Volume 9534, 95340V (2015) https://doi.org/10.1117/12.2182815

KEYWORDS: Cameras, Calibration, Lenses, Distortion, Image sensors, 3D metrology, Copper, Prototyping, Stereoscopic cameras, Corner detection

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Proceedings Article | 25 April 2008 Paper

Fast deconvolution with non-invariant PSF for 3-D fluorescence microscopy

Elie Maalouf, Bruno Colicchio, Alain Dieterlen

Proceedings Volume 7000, 70001K (2008) https://doi.org/10.1117/12.781400

KEYWORDS: Point spread functions, Deconvolution, Convolution, Luminescence, Microscopes, Image processing, Fourier transforms, Microscopy, 3D image processing, Objectives

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Proceedings Article | 22 April 2008 Paper

Recent advances in 3-D fluorescence microscopy: tomography as a source of information

A. Dieterlen, M. Debailleul, A. De Meyer, B. Simon, V. Georges, B. Colicchio, O. Haeberlé, V. Lauer

Proceedings Volume 7008, 70080S (2008) https://doi.org/10.1117/12.796995

KEYWORDS: Point spread functions, Luminescence, Microscopes, Microscopy, Deconvolution, Tomography, 3D image processing, Image processing, 3D acquisition, Optical microscopy

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3-D optical fluorescent microscopy becomes now an efficient tool for volume investigation of living biological samples. Developments in instrumentation have permit 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. If the goal is 3-D quantitative analysis, whether you improve the instrument capabilities, or (and) you restore the data. These last is until now the main task in our laboratory. Based on the knowledge of the optical Transfer Function of the microscope, deconvolution algorithms were adapted to automatic determine the regularisation threshold in order to give less subjective and more reproducible results. 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 system PSF 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. We have pointed out that automating the choice of the regularization level; if it facilitates the use, it also greatly improves the reliability of the measurements. 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 pre-filtering stabilizes the deconvolution process. We have shown that Zernike polynomials can be used to reconstruct experimental PSF, preserving system characteristics and removing the noise contained in the PSF. Fluorescent microscopes suffer from limitations; photobleaching and phototoxicity effects, or influence of the sample optical properties to 3-D observation. Amplitude and phase of the object can be reached with optical tomography based on a combination of microholography with a tomographic illumination. So indices cartography of the specimen can be obtained, and combined with fluorescence information it will open new possibilities in 3-D optical microscopy.

Proceedings Article | 14 June 2006 Paper

Image processing tools dedicated to quantification in 3D fluorescence microscopy

A. Dieterlen, A. De Meyer, B. Colicchio, S. Le Calvez, O. Haeberlé, S. Jacquey

Proceedings Volume 6254, 62540O (2006) https://doi.org/10.1117/12.679922

KEYWORDS: Point spread functions, Deconvolution, Luminescence, Microscopes, Image processing, 3D acquisition, 3D image processing, Microscopy, Image acquisition, Algorithm development

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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.

Proceedings Article | 14 April 2006 Paper

Contribution of data pre-processing to deconvolution of 3-D fluorescence microscopy images

Arnaud De Meyer, Bruno Colicchio, Jan De Mey, Georges Jung, Alain Dieterlen, Olivier Haeberlé, Serge Jacquey

Proceedings Volume 6191, 61910G (2006) https://doi.org/10.1117/12.662953

KEYWORDS: Point spread functions, Deconvolution, Luminescence, Microscopy, 3D image processing, Data acquisition, 3D acquisition, Signal to noise ratio, Optical microscopy, Spindles

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Proceedings Article | 4 June 2004 Paper

Identification and restoration in 3D fluorescence microscopy

Alain Dieterlen, Chengqi Xu, Olivier Haeberle, Nicolas Hueber, R. Malfara, B. Colicchio, Serge Jacquey

Proceedings Volume 5477, (2004) https://doi.org/10.1117/12.559754

KEYWORDS: Point spread functions, Deconvolution, Microscopy, Luminescence, 3D acquisition, Image processing, 3D image processing, Microscopes, Optical microscopy, Image acquisition

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Showing 5 of 10 publications

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