Search

Your search keyword '"Razmig Kéchichian"' showing total 21 results
Start Over Author "Razmig Kéchichian"
21 results on '"Razmig Kéchichian"'

7. Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks.

Author

Oscar Alfonso Jiménez del Toro, Henning Müller, Markus Krenn, Katharina Gruenberg, Abdel Aziz Taha, Marianne Winterstein, Ivan Eggel, Antonio Foncubierta-Rodríguez, Orcun Goksel, András Jakab, Georgios Kontokotsios, Georg Langs, Bjoern H. Menze, Tomas Salas Fernandez, Roger Schaer, Anna Walleyo, Marc-André Weber, Yashin Dicente Cid, Tobias Gass, Mattias P. Heinrich, Fucang Jia, Fredrik Kahl, Razmig Kéchichian, Dominic Mai, Assaf B. Spanier, Graham Vincent, Chunliang Wang, Daniel Wyeth, and Allan Hanbury

Published
2016
Full Text
View/download PDF

16. Automatic Multiorgan Segmentation via Multiscale Registration and Graph Cut

Author

Michel Desvignes, Razmig Kéchichian, Sébastien Valette, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Images et Modèles, Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), GIPSA - Communication Information and Complex Systems (GIPSA-CICS), 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]), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), KECHICHIAN, Razmig, Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL)

Subjects
Radiography, Abdominal, Databases, Factual, Registration, Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, spatial prior, Feature extraction, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, 02 engineering and technology, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Segmentation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Histogram, Cut, Abdomen, 0202 electrical engineering, electronic engineering, information engineering, Image Processing, Computer-Assisted, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, X-ray imaging and computed tomography, Magnetic resonance imaging (MRI), Electrical and Electronic Engineering, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, thorax, graph cut, Radiological and Ultrasound Technology, business.industry, Probabilistic logic, Pattern recognition, Atlases, Image segmentation, Magnetic Resonance Imaging, Graph, Computer Science Applications, Graph (abstract data type), Adjacency list, 020201 artificial intelligence & image processing, Radiography, Thoracic, Artificial intelligence, keypoints, business, Tomography, X-Ray Computed, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Software, Algorithms
Abstract

International audience; We propose an automatic multiorgan segmentation method for 3D radiological images of different anatomical content and modality. The approach is based on a simultaneous multilabel Graph Cut optimization of location, appearance and spatial configuration criteria of target structures. Organ location is defined by target-specific probabilistic atlases (PA) constructed from a training dataset using a fast (2+1)D SURF-based multiscale registration method involving a simple 4-parameter transformation. PAs are also used to derive target-specific organ appearance models represented as intensity histograms. The spatial configuration prior is derived from shortest-path constraints defined on the adjacency graph of structures. Thorough evaluations on Visceral project benchmarks and training dataset, as well as comparisons with the state of the art confirm that our approach is comparable to and often outperforms similar approaches in multiorgan segmentation, thus proving that the combination of multiple suboptimal but complementary information sources can yield very good performance.

Published
2018
Full Text
View/download PDF

17. Automatic Multiorgan Segmentation Using Hierarchically Registered Probabilistic Atlases

Author

Razmig Kéchichian, Sébastien Valette, and Michel Desvignes

Subjects
Computer science, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Probabilistic logic, 020207 software engineering, Pattern recognition, 02 engineering and technology, Image (mathematics), Computer Science::Computer Vision and Pattern Recognition, Cut, 0202 electrical engineering, electronic engineering, information engineering, Graph (abstract data type), Adjacency list, 020201 artificial intelligence & image processing, Segmentation, Artificial intelligence, Cluster analysis, business, Voronoi diagram
Abstract

We propose a generic method for the automatic multiple-organ segmentation of 3D images based on a multilabel graph cut optimization approach which uses location likelihood of organs and prior information of spatial relationships between them. The latter is derived from shortest-path constraints defined on the adjacency graph of structures and the former is defined by probabilistic atlases learned from a training dataset. Organ atlases are mapped to the image by a fast (2+1)D hierarchical registration method based on SURF keypoints. Registered atlases are also used to derive organ intensity likelihoods. Prior and likelihood models are then introduced in a joint centroidal Voronoi image clustering and graph cut multiobject segmentation framework. Qualitative and quantitative evaluation has been performed on contrast-enhanced CT and MR images from the VISCERAL dataset.

Published
2017
Full Text
View/download PDF

18. Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms : VISCERAL Anatomy Benchmarks

Author

Oscar Jimenez-del-Toro, Dominic Mai, Anna Walleyo, Abdel Aziz Taha, Antonio Foncubierta-Rodríguez, Daniel Wyeth, Georg Langs, Mattias P. Heinrich, Chunliang Wang, Henning Müller, Yashin Dicente Cid, Fredrik Kahl, Razmig Kéchichian, Ivan Eggel, Roger Schaer, Orcun Goksel, Markus Krenn, Tomas Salas Fernandez, Marianne Winterstein, Bjoern H. Menze, Georgios Kontokotsios, Katharina Gruenberg, Fucang Jia, Marc-André Weber, Andras Jakab, Assaf B. Spanier, Tobias Gass, G.R. Vincent, and Allan Hanbury

Subjects
Male, Computer science, landmark detection, Evaluation framework, organ segmentation, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Scale-space segmentation, Computed tomography, 030218 nuclear medicine & medical imaging, Set (abstract data type), 03 medical and health sciences, 0302 clinical medicine, medicine, Medical imaging, Image Processing, Computer-Assisted, Humans, Segmentation, Electrical and Electronic Engineering, Aged, Landmark, Radiological and Ultrasound Technology, medicine.diagnostic_test, Medicinsk bildbehandling, Magnetic resonance imaging, Image segmentation, Anatomy, Middle Aged, Magnetic Resonance Imaging, Computer Science Applications, Data set, Medical Image Processing, Tomography x ray computed, Female, Anatomic Landmarks, Tomography, X-Ray Computed, Algorithm, 030217 neurology & neurosurgery, Software, Algorithms
Abstract

Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud-based evaluation framework is presented in this paper including results of benchmarking current state-of-the-art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the cloud where participants can only access the training data and can be run privately by the benchmark administrators to objectively compare their performance in an unseen common test set. Overall, 120 computed tomography and magnetic resonance patient volumes were manually annotated to create a standard Gold Corpus containing a total of 1295 structures and 1760 landmarks. Ten participants contributed with automatic algorithms for the organ segmentation task, and three for the landmark localization task. Different algorithms obtained the best scores in the four available imaging modalities and for subsets of anatomical structures. The annotation framework, resulting data set, evaluation setup, results and performance analysis from the three VISCERAL Anatomy benchmarks are presented in this article. Both the VISCERAL data set and Silver Corpus generated with the fusion of the participant algorithms on a larger set of non-manually-annotated medical images are available to the research community. QC 20170104

Published
2016

19. A simulation framework for spectral X-Ray imaging : application to the quantification of iodine in a thorax phantom

Author

Nicolas Ducros, Razmig Kéchichian, Sébastien Valette, Philippe Douek, Françoise Peyrin, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Images et Modèles, Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales (MOTIVATE), Frouin, Frédérique, Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDV.IB] Life Sciences [q-bio]/Bioengineering, color CT, Spectral X-ray imaging, material decomposition, [SDV.IB]Life Sciences [q-bio]/Bioengineering
Abstract

Congrès sous l’égide de la Société Française de Génie Biologique et Médical (SFGBM).; National audience; Thanks to the recent development in spectral detectors, X-Ray spectral imaging has received increasing attention. This technique permits the quantification of the chemical components in an object. We present material decompositions obtained from realistic numerical simulations that account for the spectral response function of the detector.

Published
2015

20. 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
Full Text
View/download PDF

21. Shortest-path constraints for 3D multiobject semiautomatic segmentation via clustering and Graph Cut

Author

Sébastien Valette, Michel Desvignes, Razmig Kéchichian, Rémy Prost, Valette, Sébastien, Images et Modèles, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), 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), Région Rhone Alpes (SIMED), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Scale-space segmentation, 02 engineering and technology, Sensitivity and Specificity, 030218 nuclear medicine & medical imaging, Pattern Recognition, Automated, 03 medical and health sciences, 0302 clinical medicine, Imaging, Three-Dimensional, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Artificial Intelligence, Cut, Image Interpretation, Computer-Assisted, 0202 electrical engineering, electronic engineering, information engineering, Segmentation, Cluster analysis, Mathematics, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, business.industry, Segmentation-based object categorization, Reproducibility of Results, Pattern recognition, Numerical Analysis, Computer-Assisted, Image segmentation, Image Enhancement, Computer Graphics and Computer-Aided Design, Region growing, 020201 artificial intelligence & image processing, Artificial intelligence, business, Connected-component labeling, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Software, Algorithms
Abstract

International audience; We derive shortest-path constraints from graph models of structure adjacency relations and introduce them in a joint centroidal Voronoi image clustering and Graph Cut multiobject semiautomatic segmentation framework. The vicinity prior model thus defined is a piecewise-constant model incurring multiple levels of penalization capturing the spatial configuration of structures in multiobject segmentation. Qualitative and quantitative analyses and comparison with a Potts prior-based approach and our previous contribution on synthetic, simulated, and real medical images show that the vicinity prior allows for the correct segmentation of distinct structures having identical intensity profiles and improves the precision of segmentation boundary placement while being fairly robust to clustering resolution. The clustering approach we take to simplify images prior to segmentation strikes a good balance between boundary adaptivity and cluster compactness criteria furthermore allowing to control the trade-off. Compared with a direct application of segmentation on voxels, the clustering step improves the overall runtime and memory footprint of the segmentation process up to an order of magnitude without compromising the quality of the result.

Published
2013
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results