Retour à l'index des publications
Export 156 results:
Warning on RTF export
×
If you export in RTF and open your file with microsoft Word, you need to choose "Occidental (Windows Latin 1)" encoding.

Journal Article

  1. [QUET-23]“On punctate white matter lesions in preterm infants: is ultrasound diagnosis feasible?”, European Journal of Paediatric Neurology, Submitted.
  2. [VIND-23a]“Guided Deep Embedded Clustering regularization for multi-feature medical signal classification”, Pattern Recognition, vol. 143, 2023.PDF icon Vindas-23_PR_Guided deep embedded clustering regularization for multifeature classif.pdf (3.01 MB)
  3. [VIND-22a]“Semi-automatic data annotation based on feature-space projection and local quality metrics: an application to cerebral emboli characterization”, Medical Image Analysis, 2022.PDF icon Vindas-22_Media_Semi_supervised_Data_Annotation.pdf (13.46 MB)
  4. [GIRA-21b]“Hyperquaternion Conformal Groups”, Advances in Applied Clifford Algebras, 2021.PDF icon Girard2021 AACA Dual Hyperquaternion Conformal Groups_revised.pdf (449.89 KB)
  5. [MART-21]“Automatic Segmentation and Location Learning of Neonatal Cerebral Ventricles in 3D Ultrasound Data Combining CNN and CPPN”, Computers in Biology and Medicine, 2021.PDF icon Martin-21_automatic segmentation and location learning of neonatal cerebral ventricles in 3D cUS.pdf (4.07 MB)
  6. [GIRA-21a]“Dual Hyperquaternion Poincaré Groups”, Advances in Applied Clifford Algebras, vol. 31, 2021.PDF icon Girard2020 AACA Dual Hyperquaternion Poincare Groups.pdf (319.43 KB)
  7. [NGUY-19a]“Multi-grid phase field skin tumor segmentation in 3D ultrasound images”, IEEE Image Processing, 2019.PDF icon Nguyen 19 IEEE IP Multi-grid phase field skin tumor segmentation.pdf (6.74 MB)
  8. [NGUY-19b]“Multiple relaxation time lattice Boltzmann models for multi-grid phase field segmentation of tumors in 3D ultrasound images”, SIAM Journal on Imaging Sciences, 2019.PDF icon Nguyen pre-print MRT LBM for multi-grid phase field segmentation of tumors in 3D ultrasound images.pdf (4.26 MB)
  9. [WANG-19]“A gradient-based optical-flow cardiac motion estimation method for cine and tagged MR images”, Medical Image Analysis, 2019.PDF icon Wang-19 MIA preprint A gradient-based optical-flow cardiac motion.pdf (994.22 KB)
  10. [SCIO-18]“Joint segmentation and characterization of the dermis in 50 MHz ultrasound 2D and 3D images of the skin”, Computers in Biology and Medicine, vol. 103, pp. 277-286, 2018.PDF icon Sciolla 2018_CBM_preprint joint segmentation and characterization.pdf (5.12 MB)
  11. [GUEP-18]“Sequential Emboli Detection from Ultrasound Outpatient Data”, IEEE Journal of Biomedical and Health Informatics, 2018.PDF icon Guepie-18 jbhi Sequential Emboli Detection from Ultrasound Outpatient Data.pdf (1.66 MB)
  12. [GIRA-18]“Hyperquaternions: a New Tool for Physics”, Advances in Applied Clifford Algebras, vol. 28, 2018.PDF icon Girard2018 AACA Hyperquaternions new tool for physics (Research Gate).pdf (226.29 KB)
  13. [FARO-18]“Ultrasound spatiotemporal despeckling via Kronecker wavelet-Fisz thresholding”, Signal, Image and Video Processing, 2018.PDF icon Farouj-18 Ultrasound spatiotemporal despeckling via Kronecker wavelet-Fisz thresholding.pdf (564.21 KB)
  14. [SCIO-17c]“Improved Boundary Segmentation of Skin Lesions in High-Frequency 3D Ultrasound”, Computers in Biology and Medicine, vol. 87, pp. 302-310, 2017.PDF icon CBM2017.pdf (5.17 MB)
  15. [FARO-17]“Hyperbolic Wavelet-Fisz denoising for a model arising in ultrasound imaging”, IEEE Transactions on Computational Imaging, vol. 3, pp. 1-10, 2017.
  16. [SCIO-17a]“Segmentation of Skin Tumors in High-Frequency 3D Ultrasound Images”, Ultrasound in Medecine and Biology, vol. 43, pp. 227–238, 2017.PDF icon UMB2016.pdf (2.08 MB)
  17. [GUEP-17]“Discrimination between emboli and artifacts for outpatient transcranial Doppler ultrasound data”, Medical & Biological Engineering & Computing, pp. 1-11, 2017.PDF icon Medical_&_Biological_Engineering_&_Computing_2016.pdf (1.54 MB)
  18. [Gao-16]“Estimation of Cardiac Motion in Cine-MRI Sequences by Correlation Transform Optical Flow of Monogenic Features Distance”, Physics in Medicine and Biology, vol. 61, pp. 8640-8663, 2016.
  19. [WANG-15a]“Analytic signal phase-based myocardial motion estimation in tagged MRI sequences by a bilinear model and motion compensation”, Medical Image Analysis, vol. 24, pp. 149-162, 2015.
  20. [HOLS-13]K. Holst, Liebgott, H., Wilhjelm, J. E., Nikolov, S., Torp-Pedersen, T., Delachartre, P., and Jensen, J. A., “Internal Strain Estimation for Quantification of Human Heel Pad Elastic Modulus: a Phantom Study”, Ultrasonics, vol. 53, pp. 439–446, 2013.
    http://www.sciencedirect.com/science/article/pii/S0041624X1200162X?v=s5
  21. [CLAR-11]P. Clarysse, Tafazzoli, J., Delachartre, P., and Croisille, P., “Simulation based evaluation of cardiac motion estimation methods in tagged-MR Image sequences”, Journal of Cardiovascular Magnetic Resonance, vol. 13, p. P360, 2011.
    http://jcmr-online.com/content/13/S1/P360
  22. [BASA-11a]A. Basarab, Clarysse, P., Arts, T., Cachard, C., Croisille, P., and Delachartre, P., “Estimation de mouvement par décalage de phase et maillage déformable appliquée à des séquences cardiaques d'IRM marquées”, Traitement du Signal, vol. 6, pp. 643-663, 2011.
  23. [MARI-10]A. Marion, Aoudi, W., Basarab, A., Delachartre, P., and Vray, D., “Blood flow evaluation in high-frequency, 40 MHz imaging: a comparative study of four vector velocity estimation methods”, Elsevier Ultrasonics, vol. 50, pp. 683-690, 2010.
  24. [LIEB-10a]H. Liebgott, Basarab, A., Gueth, P., Friboulet, D., and Delachartre, P., “Transverse oscillations for tissue motion estimation”, Ultrasonics, vol. 50, pp. 548-555, 2010.
  25. [TOUI-10]B. Touil, Basarab, A., Delachartre, P., Bernard, O., and Friboulet, D., “Analysis of motion tracking in echocardiographic image sequences: influence of system geometry and point-spread function”, Ultrasonics, vol. 50, pp. 373-386, 2010.
  26. [BASAR-09a]A. Basarab, Liebgott, H., and Delachartre, P., “Analytic estimation of subsample spatial shift using the phases of multidimensional analytic signals”, IEEE Trans Image Proces, vol. 18, pp. 440-447, 2009.
  27. [BASA-09c]A. Basarab, Lyshchik, A., Grava, C., Buzuloiu, V., and Delachartre, P., “Ultrasound image sequence registration and its application for thyroid nodular disease”, J Signal Proces Syst, vol. 55, pp. 127-137, 2009.
  28. [BASAR-09b]A. Basarab, Gueth, P., Liebgott, H., and Delachartre, P., “Phase-based block matching applied to motion estimation with unconventional beamforming strategies”, IEEE Trans Ultrason Ferroelectr Freq Control, vol. 56, pp. 945-957, 2009.
  29. [DUBO-09]F. Duboeuf, Basarab, A., Liebgott, H., Brusseau, E., Delachartre, P., and Vray, D., “Investigation of PVA cryogel Young's modulus stability with time, controlled by a simple reliable technique”, J Med Phys, vol. 36, pp. 656–661, 2009.
  30. [BASAR-08a]A. Basarab, Liebgott, H., Morestin, F., Lyshchik, A., Higashi, T., Asato, R., and Delachartre, P., “A method for vector displacement estimation with ultrasound images and its application for thyroid nodular disease”, Med Image Anal, vol. 12, pp. 259-274, 2008.
  31. [LIEB-08a]H. Liebgott, Basarab, A., Gueth, P., Cachard, C., and Delachartre, P., “Lateral RF image synthesis using a synthetic aperture imaging technique”, IEEE Trans Ultrason Ferroelectr Freq Control, vol. 55, pp. 2097-2103, 2008.
  32. [FROM-07]J. Fromageau, Liebgott, H., Brusseau, E., Vray, D., and Delachartre, P., “Estimation of Time-Scaling Factor for Ultrasound Medical Images Using the Hilbert Transform”, Eurasip J Adv Signal Proces, vol. 2007, p. Article ID 80735, 13 pages, 2007.
  33. [LIEB-07a]H. Liebgott, Wilhjelm, J. E., Jensen, J. A., Vray, D., and Delachartre, P., “PSF dedicated to estimation of displacement vectors for tissue elasticity imaging with ultrasound”, IEEE Trans Ultrason Ferroelectr Freq Control, vol. 54, pp. 746–756, 2007.
  34. [DELA-06a]P. Delachartre, Liebgott, H., Lacouture, F., Morestin, F., Lyshchik, A., Higashi, T., and Asato, R., “Modélisation bilinéaire et estimation du déplacement pour l'imagerie de l'élasticité appliquée au cancer de la thyroïde”, Traitement du Signal, vol. 23, pp. 235-245, 2006.
  35. [BALO-06a]S. Balocco, Basset, O., Courbebaisse, G., Delachartre, P., Tortoli, P., and Cachard, C., “3D dynamical ultrasonic model of pulsating vessel walls”, Ultrasonics Journal, Elsevier, vol. 44, pp. pp. e179-e183, 2006.
  36. [LIEB-05]H. Liebgott, Fromageau, J., Wilhjelm, J. E., Vray, D., and Delachartre, P., “Beamforming scheme for 2D displacement estimation in ultrasound imaging”, Eurasip J Appl Signal Process, vol. 8, pp. 1212–1220, 2005.
  37. [FROM-03a]J. Fromageau, Brusseau, E., Vray, D., Gimenez, G., and Delachartre, P., “Characterization of PVA cryogel for intravascular ultrasound elasticity imaging”, IEEE Trans Ultrason Ferroelectr Freq Control, vol. 50, pp. 1318-1324, 2003.
  38. [BRUS-02c]E. Brusseau, Fromageau, J., Rognin, N., Delachartre, P., and Vray, D., “Investigating elastic properties of soft biological tissues”, IEEE Eng Med Biol, vol. 21, pp. 86–94, 2002.
  39. [BRUS-01a]E. Brusseau, Fromageau, J., Finet, G., Delachartre, P., and Vray, D., “Axial strain imaging of intravascular data : results on polyvinyl alcohol cryogel phantoms and carotid artery”, Ultrasound Med Biol, vol. 27, pp. 1631–1642, 2001.
  40. [GUER-00b]G. Guerault, Delachartre, P., Finet, G., and Magnin, I. E., “Modélisation et segmentation d'images échographiques endovasculaires”, Traitement du Signal, vol. 17, pp. 517-529, 2000.
  41. [BRUS-00a]E. Brusseau, Perrey, C., Delachartre, P., Vogt, M., Vray, D., and Ermert, H., “Axial strain imaging using a local estimation of the scaling factor from RF ultrasound signals”, Ultrasonic Imaging, vol. 22, pp. 95–107, 2000.
  42. [BASS-00a]O. Basset, Buquet, B., Abouelkaram, S., Delachartre, P., and Culioli, J., “Application of texture image analysis for the classification of bovine meat”, J Food Chem, vol. 69, pp. 437–445, 2000.
  43. [ABOU-00]S. Abouelkaram, Suchorski, K., Buquet, B., Berge, P. H., Culioli, J., Delachartre, P., and Basset, O., “Effects of muscle texture on ultrasonic measurements”, J Food Chem, vol. 69, pp. 447–455, 2000.
  44. [DELA-99a]P. Delachartre, Cachard, C., Finet, G., Gerfault, L., and Vray, D., “Modelling geometric artefacts in intravascular ultrasound imaging”, Ultrasound Med Biol, vol. 25, pp. 567-575, 1999.
  45. [FINE-98a]G. Finet, Cachard, C., Delachartre, P., Maurincomme, E., and Beaune, J., “Artifacts in intravascular ultrasound imaging during coronary artery stent implantation”, Ultrasound Med Biol, vol. 26, pp. 793-802, 1998.
  46. [MA-97]N. Ma, Vray, D., Delachartre, P., and Gimenez, G., “Time-frequency representation of multicomponent chirp signals”, Signal Proces, vol. 56, pp. 149-155, 1997.
  47. [ANDR-95]N. Andrieux, Delachartre, P., Vray, D., Gimenez, G., and Dziedzic, A., “Lake-bottom recognition using a wideband sonar system and time-frequency analysis”, J Acoust Soc Am, vol. 98, pp. 552-559, 1995.

Conference Paper

  1. [ESTE-23a]“PWML Detection in 3D Cranial Ultrasound Volumes using Over-segmentation and Classification with Deep Neural Networks”, in IEEE International Symposium on Biomedical Imaging, 2023, pp. 1-4.PDF icon Estermann-23_ISBI_PWML Detection in 3D Cranial Ultrasound Volumes using Over-segmentation and Classification with Deep Neural Networks.pdf (533.05 KB)
  2. [VIND-23c]“Deep Embedded Clustering regularization for supervised imbalanced cerebral emboli classification using transcranial Doppler ultrasound”, in 31st European Signal Processing Conference (EUSIPCO 2023), september 4-8, Helsinki, Finland, 2023.PDF icon Vindas-23_Eusipco_DEC regularization for supervised imbalanced cerebral emboli classification using TDC.pdf (392.52 KB)
  3. [VIND-23b]“Soft-labels noise tolerant loss functions for transcranial Doppler ultrasound signal classification”, in IEEE Ultrasonics symposium, 2023, pp. 1-4.PDF icon Vindas-23_IEEE-IUS_Soft labels noise tolerant loss functions for TCD signal classification.pdf (632.81 KB)
  4. [ESTE-23b]“Vision Transformer and Multi-View Classification for Lesion Detection in 3D Cranial Ultrasound”, in IEEE Ultrasonics symposium, 2023, pp. 1-4.PDF icon Estermann-23_IEEE-IUS_ViT_and_Multi_View_Classification_for_Lesion_Detection_in_3DcUS.pdf (861.68 KB)
  5. [VIND-22b]“An hybrid CNN-Transformer model based on multi-feature extraction and attention fusion mechanism for classification of medical signals”, in Machine Learning for Healthcare, 2022.
    https://static1.squarespace.com/static/59d5ac1780bd5ef9c396eda6/t/62e97636b9962e1079555986/1659467321416/43+Paper-43_CameraReady.pdf
  6. [GIRA-22]“Hyperquaternions and Physics”, in 34th International Colloquium on Group Theoretical Methods in Physics, Strasbourg, 18-22 July, 2022, pp. 1-8.PDF icon Girard-22_Proceedings G34.pdf (151.92 KB)
  7. [WANG-21]“3D Clifford Analytic Signal for 3D Envelope Detection on Ultrasound Volume”, in IEEE International Ultrasonics Symposium, 2021.
  8. [VIND-21]“Semi-supervised annotation of Transcranial Doppler ultrasound micro-embolic data”, in IEEE International Ultrasonics Symposium, 2021.PDF icon Vindas-21_IUS_Semi-supervised annotation of Transcranial Doppler.pdf (1.84 MB)
  9. [BLAN-20]“Quantitative Comparison of 3D Freehand Ultrasound and MRI Images of the Neonatal Brain”, in IEEE International Ultrasonics Symposium, 2020.PDF icon Blanchard-20 IUS Quantitative Comparison of 3D Freehand US and MRI.pdf (2.11 MB)
  10. [ERBA-20]“Priority U-Net: Detection of Punctuate White Matter Lesions in Preterm Neonate in 3D Cranial Ultrasonography”, in Medical Imaging with Deep Learning - MIDL, 2020.
    https://proceedings.mlr.press/v121/erbacher20a.html
  11. [DELA-20]“Automatic classification of brain emboli using transcranial Doppler and convolutional neural networks”, in Forum Acusticum, Dec 2020, Lyon, France, 2020, pp. 2693-2694.
    https://hal.science/hal-03242473/document
  12. [GIRA-19]“Hyperquaternions: An Efficient Mathematical Formalism for Geometry”, in 4th conference on Geometric Science of Information, 2019.PDF icon Girard-19 preprint GSI Hyperquaternions An Efficient Mathematical Formalism for Geometry.pdf (314.59 KB)
  13. [MART-19]“Training database elaboration strategies for preterm neonate cerebral ventricular system segmentation with 2D convolutional neural network in 3D US data”, in IEEE International Ultrasonics Symposium, 2019.PDF icon Martin_IUS_2019.pdf (974.69 KB)
  14. [MART-18]“Automatic segmentation of the cerebral ventricle in neonates using deep learning with 3D reconstructed freehand ultrasound imaging”, in IEEE International Ultrasonics Symposium, 2018.PDF icon Martin-18 IEEE IUS Automatic segmentation of the cerebral ventricle.pdf (1.04 MB)
  15. [DELA-17]“Segmentation of High Frequency Ultrasound Skin Data”, in RITS, 2017.
  16. [SCIO-17b]“Apprentissage pour la segmentation de la prostate en IRM : architectures multi-échelle résiduelles”, in CAp, Grenoble, 2017.
  17. [GIRA-17a]“Hyperquaternion Symmetry Groups”, in 11th International Conference on Clifford Algebras and Their Applications in Mathematical Physics, ICCA11, Ghent, Belgique, 2017.
  18. [GIRA-17b]“Hyperquaternions and Physics: Historical Outlook”, in 11th International Conference on Clifford Algebras and Their Applications in Mathematical Physics", ICCA11, Ghent, Belgique, 2017.
  19. [CHAN-16]“Vessel Segmentation in High-Frequency 2D/3D Ultrasound Images”, in IEEE International Ultrasonics Symposium, 2016.PDF icon IUS2016a.pdf (3.81 MB)
  20. [SCIO-16]“Segmentation of the lateral ventricles in 3D ultrasound images of the brain in neonates”, in IEEE International Ultrasonics Symposium, 2016.PDF icon IUS2016b (1.09 MB)
  21. [GUEP-15a]B. K. Guépié, Almar, M., Guibert, B., and Delachartre, P., “Détection de micro-emboles par une approche de classification supervisée”, in GRETSI, France, 2015.PDF icon guepie_gretsi2015.pdf (63.84 KB)
  22. [SCIO-15a]B. Sciolla, Ceccato, P., Cowell, L., Dambry, T., Guibert, B., and Delachartre, P., “Segmentation of Inhomogeneous Skin Tissues in High-frequency 3D Ultrasound Images, the Advantage of Non-parametric Log-likelihood Methods”, in Physics Procedia, 2015, vol. 70, pp. 1177 - 1180.
    Proceedings of the 2015 İCU International Congress on Ultrasonics, Metz, France
    http://www.sciencedirect.com/science/article/pii/S1875389215009943
  23. [SCIO-15d]B. Sciolla, Ceccato, P., Dambry, T., Guibert, B., and Delachartre, P., “A comparison of non-parametric segmentation methods”, in GRETSI, Lyon, France, 2015.PDF icon Sciolla-2015_GRETSI.pdf (532.42 KB)
    https://hal.archives-ouvertes.fr/hal-01307318
  24. [FARO-15]Y. Farouj, Navarro, L., Clausel, M., and Delachartre, P., “Débruitage de séquences d'images dynamiques par ondelettes espace-temps hyperboliques”, in Gretsi, 2015.
  25. [SCIO-15c]B. Sciolla, Cowell, L., Dambry, T., Guibert, B., and Delachartre, P., “Multigrid Level-Set Segmentation Of High-Frequency 3D Ultrasound Images Using The Hellinger Distance”, in 9th International Symposium on Image and Signal Processing and Analysis (ISPA), Zagreb, Croatie, 2015, pp. 165-169.PDF icon Sciolla_2015_ISPA.pdf (1.46 MB)
  26. [GUEP-15b]B. K. Guépié, Sciolla, B., Millioz, F., Ramassamy, R., Pinatel, E., Almar, M., Guibert, B., and Delachartre, P., “Time-frequency Blood Flow Parameters Estimation for Micro-emboli Detection”, in IEEE ISPA , Zagreb, Croatie, 2015.PDF icon article_ipsa2015.pdf (1.24 MB)
  27. [WANG-14a]L. Wang, Liu, Z., Clarysse, P., Croisille, P., P. R. Girard, Liu, W. Y., and Delachartre, P., “Myocardial Motion Estimation Using Optical Flow with Multiple Constraint Equations”, in IEEE International Conference on Signal Processing (ICSP14), HangZhou, 2014, pp. 1066-1071.PDF icon Liang-14_IEEE-ICSP_0211.pdf (1.04 MB)
  28. [LIN-14b]F. Lin, Saglio, C., Almar, M., Guibert, B., and Delachartre, P., “Micro-embolic signal characterization based on long time Doppler time-frequency image processing and statistics”, in IEEE International Conference on Signal Processing, Hangzhou, China, 2014, pp. 1046-1051.
  29. [GIRA-14]P. R. Girard, Clarysse, P., Pujol, R., Wang, L., and Delachartre, P., “Biquaternion Clifford Algebra and Grassmann Differential Geometry”, in 8th International Conference, Curves and Surfaces, Paris, France, 2014, pp. 216-242.
  30. [FARO-14b]Y. Farouj, Navarro, L., Clausel, M., and Delachartre, P., “A VARIATIONAL SHEARLET-BASED MODEL FOR AORTIC STENT DETECTION”, in In Proc. IEEE Conf. on Signal Processing (ICSP'14), HangZhou, China, 2014, pp. 1052-1056.
  31. [FARO-14a]Y. Farouj, Wang, L., Clarysse, P., Navarro, L., Clausel, M., and Delachartre, P., “Cardiac Motion Analysis using wavelet projections from Tagged MR sequences”, in IEEE International Conference on Image Processing (ICIP'14), Paris, 2014, p. Accepted.
  32. [MAUR-13b]R. L. Maurice, Delachartre, P., Dubernard, G., Huissoud, C., and Caloone, J., “Cervix elastography: a new method for the prediction of the preterm delivery?”, in 12th International Tissue Elasticity Conference, London, UK, 2013.
  33. [WANG-12a]L. Wang, P. R. Girard, Clarysse, P., and Delachartre, P., “Phase extraction of the Analytic Video Signal in Clifford Algebra”, in 5th conference on Applied Geometric Algebras in Computer Science and Engineering, La Rochelle, France, 2012.PDF icon Upload_Version -pdf CameraReady_Article_AGACSE_2012v2.2-modify21092012.pdf (561.29 KB)
    http://hal.archives-ouvertes.fr/hal-00839287
  34. [WANG-12c]L. Wang, P. R. Girard, Bernard, A., Liu, Z., Clarysse, P., and Delachartre, P., “3-D biquaternionic analytic signal and application to envelope detection in 3-D ultrasound imaging”, in 3D Imaging (IC3D), 2012 International Conference on, Liège, Belgium, 2012.PDF icon 56.pdf (1.45 MB)
    http://dx.doi.org/10.1109/IC3D.2012.6615144
  35. [WANG-12b]L. Wang, Liu, Z., P. R. Girard, Clarysse, P., and Delachartre, P., “Extension of Analytic Signal conception on Dynamic Image Sequences”, in 9ème Manifestation des Jeunes Chercheurs en Sciences et Technologies de l’Information et de la Communication, Lille, France, 2012.
    http://hal.inria.fr/hal-00780356
  36. [DELA-11]P. Delachartre, Clarysse, P., Goutte, R., and P. R. Girard, “Mise en œuvre du signal analytique dans les algèbres de Clifford”, in GRETSI, Bordeaux, France, 2011.
  37. [GIRA-11a]P. R. Girard, Pujol, R., Clarysse, P., Marion, A., Goutte, R., and Delachartre, P., “Analytic Video (2D+T) Signals by Clifford Fourier Transforms in Multiquaternion Grassmann-Hamilton-Clifford Algebras”, in 9th International Conference on Clifford Algebras and Their Applications in Mathematical Physics, Editor, K. Gürlebeck. , Weimar, Germany, 2011.
  38. [BASA-11b]A. Basarab, Vidal, F., Abbal, R., Delachartre, P., Vayssiere, C., Léguevaque, P., and Kouamé, D., “Motion estimation in ultrasound imaging applied to the diagnostic of pelvic floor disorders”, in IEEE Engineering in Medicine and Biology Society Conference (EMBC 2011), Boston, USA, 30/08/2011-03/09/2011, 2011, pp. 8058 - 8061.
  39. [GUET-10b]P. Gueth, Delachartre, P., and Liebgott, H., “Motion estimation using prebeamformed ultrasound signals”, in IEEE International Ultrasonic Symposium, San Diego, USA, 2010, pp. 2004-2007.
  40. [OLLI-10]J. Ollivier, Lyshchik, A., Basarab, A., and Delachartre, P., “A 2D least square differentiation filter for tensorial elastography”, in IEEE International Ultrasonics Symposium, San Diego, USA, 2010, pp. 1624 - 1627.
  41. [MALT-10]T. Maltaverne, Delachartre, P., and Basarab, A., “Motion estimation using the monogenic signal applied to ultrasound elastography”, in IEEE Engineering in Medicine and Biology Society Conference (EMBC 2010), Buenos Aires, Argentina, 2010, pp. 33-36.
  42. [ZAHN-09]G. Zahnd, Basarab, A., Liebgott, H., Basset, O., and Delachartre, P., “Real-time specific beamforming applied to motion trajectory estimation in ultrasound imaging”, in IEEE International Ultrasonics Symposium, Roma, Italy, 2009, pp. 1342-1345.
  43. [LIEB-09]H. Liebgott, Ben Salem, A., Basarab, A., Gao, H., Claus, P., D'hooge, J., Delachartre, P., and Friboulet, D., “Tangential sound field oscillations for 2D motion estimation in echocardiography”, in IEEE International Ultrasonics Symposium, Roma, Italy, 2009, pp. 498–501.
  44. [GUET-09]P. Gueth, Liebgott, H., and Delachartre, P., “Formation de champ de déplacements à partir des signaux bruts. Application à l\'imagerie ultrasonore en synthèse d\'ouverture”, in XXIIe Colloque GRETSI - Traitement du Signal et des Images (GRETSI'09), Dijon, France, 2009, p. ID341.
  45. [BASA-08c]A. Basarab, Lyshchik, A., and Delachartre, P., “Multi-frame motion estimation for freehand elastography and its application to thyroid tumor imaging”, in IEEE ISBI, Paris, France, 2008, pp. 532-535.
  46. [DUBO-07]F. Duboeuf, Liebgott, H., Basarab, A., Brusseau, E., Delachartre, P., and Vray, D., “Static mechanical assessment of elastic Young's modulus of tissue mimicking materials used for medical imaging”, in Proc. 29th Int. IEEE EMBC 2007, Lyon, France, 2007, pp. 3450-3453.
  47. [BASA-07b]A. Basarab, Grava, C., Buzuloiu, V., and Delachartre, P., “Estimation de décalages subpixéliques par ajustement de la phase des signaux complexes”, in Gretsi, Troyes, France, 2007, pp. 149-152.
  48. [BASA-07c]A. Basarab, Aoudi, W., Liebgott, H., Vray, D., and Delachartre, P., “Parametric deformable block matching for ultrasound imaging”, in IEEE International Conference on Image Processing ICIP'07, San Antonio, USA, 2007, p. II-429-II-432.
  49. [BASAR-07a]A. Basarab, Gueth, P., Liebgott, H., and Delachartre, P., “Two-dimensional least-squares estimation for motion tracking in ultrasound elastography”, in IEEE EMBC, Lyon, France, 2007, pp. 2155-2158.
  50. [GUET-07]P. Gueth, Basarab, A., Liebgott, H., and Delachartre, P., “Beamforming techniques for motion estimation in ultrasound elastography”, in IEEE International Ultrasonics Symposium, New-York, USA, 2007, pp. 1953-1956.
  51. [BALO-06d]S. Balocco, Basset, O., Courbebaisse, G., Delachartre, P., Tortoli, P., and Cachard, C., “3D dynamical ultrasonic model of pulsating wall vessels”, in Ultrasonics International (UI’05) and World Congress on Ultrasonics (WCU) , Beijing, P.R. China, 2006, pp. e179-e183.
  52. [BASA-06]A. Basarab, Liebgott, H., Grava, C., and Delachartre, P., “Two-dimensional sub-sample estimation using plane phase fitting”, in IEEE ICASSP, Toulouse, France, 2006, vol. 2, pp. 1152-1155.
  53. [LIEB-06]H. Liebgott, Basarab, A., Loizeau, D., Wilhjelm, J. E., Jensen, J. A., and Delachartre, P., “Improved Beamforming for Lateral Oscillations in Elastography Using Synthetic Aperture Imaging”, in IEEE International Ultrasonics Symposium, Vancouver, Canada, 2006, pp. (2168-2171).

Pages