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17 results on '"Jérémie Pescatore"'

11. Spatio-Temporal Multiscale Denoising of Fluoroscopic Sequence

Author

C. Girard, Michel Desvignes, Jérémie Pescatore, Carole Amiot, Jocelyn Chanussot, Thales Electron Devices, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), General Electric Medical Systems [Buc] (GE Healthcare), General Electric Medical Systems, and GIPSA - Communication Information and Complex Systems (GIPSA-CICS)

Subjects
Image quality, Noise reduction, Physics::Medical Physics, 02 engineering and technology, Signal-To-Noise Ratio, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Motion, 0302 clinical medicine, Wavelet, Spatio-Temporal Analysis, 0202 electrical engineering, electronic engineering, information engineering, Curvelet, Humans, Computer vision, Electrical and Electronic Engineering, ComputingMilieux_MISCELLANEOUS, Mathematics, Radiological and Ultrasound Technology, business.industry, Wavelet transform, Signal Processing, Computer-Assisted, Filter (signal processing), Non-local means, Image Enhancement, Spine, Computer Science Applications, Fluoroscopy, 020201 artificial intelligence & image processing, Video denoising, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Software, Algorithms
Abstract

In the past 20 years, a wide range of complex fluoroscopically guided procedures have shown considerable growth. Biologic effects of the exposure (radiation induced burn, cancer) lead to reduce the dose during the intervention, for the safety of patients and medical staff. However, when the dose is reduced, image quality decreases, with a high level of noise and a very low contrast. Efficient restoration and denoising algorithms should overcome this drawback. We propose a spatio-temporal filter operating in a multi-scales space. This filter relies on a first order, motion compensated, recursive temporal denoising. Temporal high frequency content is first detected and then matched over time to allow for a strong denoising in the temporal axis. We study this filter in the curvelet domain and in the dual-tree complex wavelet domain, and compare those results to state of the art methods. Quantitative and qualitative analysis on both synthetic and real fluoroscopic sequences demonstrate that the proposed filter allows a great dose reduction.

Published
2016
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12. Hyperspectral imaging applied to microbial categorization in an automated microbiology workflow

Author

Pierre Imbaud, Guillaume Perrin, Jérémie Pescatore, Rony Midahuen, and Denis F. Leroux

Subjects
Categorization, Computer science, Multispectral image, Radiance, Hyperspectral imaging, Image processing, Image resolution, Subpixel rendering, Remote sensing, Spectral purity, Microbiology
Abstract

Hyperspectral imaging (HSI) is being evaluated as a pre-selection tool to categorize and localize populations of microbial colonies directly onto their culture medium, in order to facilitate the microbiology workflow downstream the incubation step. The categorization criteria were here limited to the diffuse radiance spectra acquired mostly in the visible region between 400 and 900 nm. Although the diffuse radiance signal is much broader than the one acquired using vibrational techniques such as Raman and IR and limited to chromophores absorbing in the visible region, it can be acquired very quickly allowing to perform hyperspectral imaging of large objects (i.e. Petri dishes) with throughputs that are compatible with the needs of a clinical laboratory workflow. Moreover, additional cost reduction could possibly be achieved using application-specific multispectral systems. Furthermore, recent research has shown that good power of discrimination, at the species level, could be achieved at least for a low level of species. In our work, we test different culture media, with and without a strong light absorption in the visible region, and report categorization results obtained when selecting end-member spectra according to a multi-parametric study (colonies, agar type). Results of categorization (e.g. at the species level) are presented using two types of supervised-categorization algorithms providing that they deliver subpixel fractional abundance information (Linear Spectral Unmixing type) or not such as Spectral Angle Mapping (SAM) and Euclidian Distance (ED) type. Interestingly the performance between the two classes of algorithms is dramatically different, a trend which is not always observed. An interpretation is proposed on the basis of the agar interference and the spectral purity of end-member spectra.

Published
2015
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13. Curvelet Based Contrast Enhancement in Fluoroscopic Sequences

Author

Jérémie Pescatore, Jocelyn Chanussot, Carole Amiot, Michel Desvignes, C. Girard, Thales Electron Devices, GIPSA - Communication Information and Complex Systems (GIPSA-CICS), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), and GIPSA - Signal Images Physique (GIPSA-SIGMAPHY)

Subjects
Noise reduction, 02 engineering and technology, Radiation Dosage, 030218 nuclear medicine & medical imaging, Image (mathematics), 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, Curvelet, Image Processing, Computer-Assisted, X-ray imaging and computed tomography, Computer vision, Electrical and Electronic Engineering, Mathematics, Image enhancement/restoration, Sequence, Radiological and Ultrasound Technology, Spatial filter, business.industry, Contrast (statistics), Computer Science Applications, Visualization, Curvelets, Fluoroscopy, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 020201 artificial intelligence & image processing, Artificial intelligence, Noise (video), business, Tomography, X-Ray Computed, Software, Algorithms
Abstract

International audience; —Image guided interventions have seen growing interest in recent years. The use of X-rays for the procedure impels limiting the dose over time. Image sequences obtained thereby exhibit high levels of noise and very low contrasts. Hence, the development of efficient methods to enable optimal visualization of these sequences is crucial. We propose an original denoising method based on the curvelet transform. First, we apply a recursive temporal filter to the curvelet coefficients. As some residual noise remains, a spatial filtering is performed in the second step, which uses a magnitude-based classification and a contextual comparison of curvelet coefficients. This procedure allows to denoise the sequence while preserving low-contrasted structures, but does not improve their contrast. Finally, a third step is carried out to enhance the features of interest. For this, we propose a line enhancement technique in the curvelet domain. Indeed, thin structures are sparsely represented in that domain, allowing a fast and efficient detection. Quantitative and qualitative evaluations performed on synthetic and real low-dose sequences demonstrate that the proposed method enables a 50% dose reduction.

Published
2015
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14. Image denoising using contextual modeling of curvelet coefficients

Author

Jérémie Pescatore, Razmig Kéchichian, Michel Desvignes, Jocelyn Chanussot, Carole Amiot, C. Girard, GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), THALES, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), and University of Iceland [Reykjavik]

Subjects
business.industry, image denoising, curvelets, Quantitative Evaluations, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, Thresholding, Image (mathematics), Computer Science::Computer Vision and Pattern Recognition, 0202 electrical engineering, electronic engineering, information engineering, Curvelet, Maximum a posteriori estimation, 020201 artificial intelligence & image processing, Computer vision, MAP estimation, Artificial intelligence, Image denoising, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Mathematics, statistical image modeling
Abstract

International audience; We propose an image denoising method which takes curvelet domain inter-scale, inter-location and inter-orientation dependencies into account in a maximum a posteriori labeling of the curvelet coefficients of a noisy image. The rationale is that generalized neighborhoods of curvelet coefficients contain more reliable information on the true image than individual coefficients. Based on the labeling of coefficients and their magnitudes, a smooth thresholding functional produces denoised coefficients from which the denoised image is reconstructed. We also outline a faster approach to labeling and thresholding, relying on contextual comparisons of coefficients. Quantitative and qualitative evaluations on natural and X-ray images show that our method outperforms related multiscale approaches and compares favorably to the state-of-art BM3D method on X-ray data while executing faster.

Published
2014
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16. Model of a Vascular C-Arm for 3D Augmented Fluoroscopy in Interventional Radiology

Author

Yves Trousset, M-O. Berger, Luc Picard, Jérémie Pescatore, René Anxionnat, Erwan Kerrien, Sebastien Gorges, General Electric Medical Systems [Buc] (GE Healthcare), General Electric Medical Systems, Visual Augmentation of Complex Environments (MAGRIT), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Département de neuroradiologie diagnostique et thérapeutique [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), and James Duncan and Guido Gerig

Subjects
medicine.medical_specialty, medicine.diagnostic_test, business.industry, Calibration (statistics), [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Reprojection error, Interventional radiology, Context (language use), 02 engineering and technology, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, Medicine, Fluoroscopy, 020201 artificial intelligence & image processing, Medical physics, Rigid motion, Clinical case, business
Abstract

International audience; This paper deals with the modeling of a vascular C-arm to generate 3D augmented fuoroscopic images in an interventional radiology context. A methodology based on the use of a multi-image calibration is proposed to assess the physical behavior of the C-arm. From the knowledge of the main characteristics of the C-arm, realistic models of the acquisition geometry are proposed. Their accuracy was evaluated and experiments showed that the C-arm geometry can be predicted with a mean 2D reprojection error of 0.5 mm. The interest of 3D augmented uoroscopy is also assessed on a clinical case.

Published
2005
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