Yann Gaudeau
Centre national de la recherche scientifique
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Publication
Featured researches published by Yann Gaudeau.
cluster computing and the grid | 2005
Cécile Germain; Vincent Breton; Patrick Clarysse; Yann Gaudeau; Tristan Glatard; Emmanuel Jeannot; Yannick Legré; Charles Loomis; Johan Montagnat; Jean-Marie Moureaux; Angel Osorio; Xavier Pennec; Romain Texier
Grids have emerged as a promising technology to handle the data and compute intensive requirements of many application areas. Digital medical image processing is a promising application area for grids. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. The research project AGIR (Grid Analysis of Radiological Data) presented in this paper addresses this challenge through a combined approach: on one hand, leveraging the grid middleware through core grid medical services which target the requirements of medical data processing applications; on the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical applications.
international conference on image processing | 2005
Ludovic Guillemot; Yann Gaudeau; Jean-Marie Moureaux
In this paper, we present a new bit allocation procedure based on the approximation of the rate distortion (R-D) functions provided by our efficient lattice vector quantizer with pyramidal dead zone (DZLVQ). Here, we show that DZLVQ R-D functions can be efficiently fitted by an exponential model. This property leads to an analytical solution to the bit allocation problem which reduces significantly the complexity of our compression scheme. Furthermore, our method is highly parallelizable. Finally we show that it keeps the very good results in terms of visual quality of DZLQV, as it better preserves fine structures with respect to SPIHT and JPEG2000 at low rates.
IEEE Transactions on Image Processing | 2008
Ludovic Guillemot; Yann Gaudeau; Saïd Moussaoui; Jean-Marie Moureaux
Entropy-coded lattice vector quantization (ECLVQ) with codebooks dedicated to independent identically distributed (i.i.d.) generalized Gaussian sources have proven their high coding performances in the wavelet domain. It is well known that wavelet coefficients with high magnitude (corresponding to edges and textures) tend to be clustered in a few amount of vectors. In this paper, we first show that this property has a major influence on the performances of ECLVQ schemes. Since this clustering property cannot be taken into account by the classical i.i.d. assumption, our first proposal is to model the joint distribution of vectors by a multidimensional mixture of generalized Gaussian (MMGG) densities. The main outcome of this MMGG model is to provide a theoretical framework to simply derive from i.i.d. - models, the corresponding MMGG - models. In a second part, a new codebook better suited to wavelet coding is proposed: the so-called dead zone lattice vector quantizers (DZLVQ). It consists of generalizing the scalar dead zone to vector quantization by thresholding vectors according to their energy. We show that DZLVQ improves the rate-distortion tradeoff. Experimental results are provided for the pyramidal LVQ scheme under the assumption of a multidimensional mixture of Laplacian (MML) densities. Results performed on a set of real life images show the precision of the analytical - curves and the efficiency of the DZLVQ scheme.
Healthcare technology letters | 2017
Meriem Ben Abdallah; Marie Blonski; Sophie Wantz-Mézières; Yann Gaudeau; Luc Taillandier; Jean-Marie Moureaux
Management of diffuse low-grade glioma (DLGG) relies extensively on tumour volume estimation from MRI datasets. Two methods are currently clinically used to define this volume: the commonly used three-diameters solution and the more rarely used software-based volume reconstruction from the manual segmentations approach. The authors conducted an initial study of inter-practitioners’ variability of software-based manual segmentations on DLGGs MRI datasets. A panel of 13 experts from various specialties and years of experience delineated 12 DLGGs’ MRI scans. A statistical analysis on the segmented tumour volumes and pixels indicated that the individual practitioner, the years of experience and the specialty seem to have no significant impact on the segmentation of DLGGs. This is an interesting result as it had not yet been demonstrated and as it encourages cross-disciplinary collaboration. Their second study was with the three-diameters method, investigating its impact and that of the software-based volume reconstruction from manual segmentations method on tumour volume. They relied on the same dataset and on a participant from the first study. They compared the average of tumour volumes acquired by software reconstruction from manual segmentations method with tumour volumes obtained with the three-diameters method. The authors found that there is no statistically significant difference between the volumes estimated with the two approaches. These results correspond to non-operated and easily delineable DLGGs and are particularly interesting for time-consuming CUBE MRIs. Nonetheless, the three-diameters method has limitations in estimating tumour volumes for resected DLGGs, for which case the software-based manual segmentation method becomes more appropriate.
european signal processing conference | 2006
Ludovic Guillemot; Yann Gaudeau; Saïd Moussaoui; Jean-Marie Moureaux
Archive | 2009
Cécile Germain-Renaud; Vincent Breton; Patrick Clarysse; Bertrand Delhay; Yann Gaudeau; Tristan Glatard; Emmanuel Jeannot; Yannick Legré; Johan Montagnat; Jean-Marie Moureaux; Angel Osorio; Xavier Pennec; Joël Schaerer; Romain Texier
IEEE Journal of Biomedical and Health Informatics | 2018
Meriem Ben Abdallah; Marie Blonski; Sophie Wantz-Mézières; Yann Gaudeau; Luc Taillandier; Jean-Marie Moureaux; Amélie Darlix; Nicolas Menjot de Champfleur; Hugues Duffau
Modélisation biostatistique et biomathématique des données d'imagerie en cancérologie | 2016
Meriem Ben Abdallah; Marie Blonski; Sophie Wantz-Mézières; Yann Gaudeau; Luc Taillandier; Jean-Marie Moureaux
18e Colloque Compression et Représentation des Signaux Audiovisuels, CORESA 2016 | 2016
Meriem Ben Abdallah; Marie Blonski; Sophie Wantz-Mézières; Yann Gaudeau; Luc Taillandier; Jean-Marie Moureaux
XXVe Colloque GRETSI Traitement du Signal & des Images, GRETSI 2015 | 2015
Yann Gaudeau; Frédéric Lefevre; Jean-Marie Moureaux