Papers by Thierry BROUARD
Springer eBooks, 2008
We propose a new hybrid protocol for cryptographically secure biometric authentication. The main ... more We propose a new hybrid protocol for cryptographically secure biometric authentication. The main advantages of the proposed protocol over previous solutions can be summarised as follows: (1) potential for much better accuracy using different types of biometric signals, including behavioural ones; and (2) improved user privacy, since user identities are not transmitted at any point in the protocol execution. The new protocol takes advantage of state-of-the-art identification classifiers, which provide not only better accuracy, but also the possibility to perform authentication without knowing who the user claims to be. Cryptographic security is based on the Paillier public key encryption scheme.
Local probabilistic atlases and a posteriori correction for the segmentation of heart images
HAL (Le Centre pour la Communication Scientifique Directe), Sep 1, 2017
Atlas-based segmentation is a well-known method for segmentation of medical images. In particular... more Atlas-based segmentation is a well-known method for segmentation of medical images. In particular, this method could be used in an efficient way to automatically segment heart structures in MRI or CT scans. We propose, in this paper a more adaptive and interactive atlas-based segmentation method. The model presented combines several local probabilistic atlases with a topological graph. The local atlases provide more refined information about the structures’ shape while the spatial relationships between the atlases are learned and stored in a graph. Hence, local registrations need less computational time and the image segmentation can be guided by the user in an incremental way. Following this step, a pixel classification is performed with a hidden Markov random field that integrates the learned a priori information with the pixel intensities that originate from different modalities. Finally, an a posteriori correction is performed using Adaboost classifiers in order to correct voxels in the border of the seek region and improve the precision of the results. The proposed method is tested on CT scan and MRI images of the heart coming from the MM-WHS challenge.
Sciyo eBooks, Aug 18, 2010
We present a new approach for recognition of complex graphic symbols in technical documents. Grap... more We present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our system we represent symbols by their graph based signatures: a graphic symbol is vectorized and is converted to an attributed relational graph, which is used for computing a feature vector for the symbol. This signature corresponds to geometry and topology of the symbol. We learn a Bayesian network to encode joint probability distribution of symbol signatures and use it in a supervised learning scenario for graphic symbol recognition. We have evaluated our method on synthetically deformed and degraded images of presegmented 2D architectural and electronic symbols from GREC databases and have obtained encouraging recognition rates.
Pattern Recognition, Feb 1, 2013
Structural pattern recognition approaches offer the most expressive, convenient, powerful but com... more Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition. We extract the topological, structural and attribute information from a graph and encode numeric details by fuzzy histograms and symbolic details by crisp histograms. The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector. Experimentation on standard public graph datasets shows that our method outperforms the state-of-the-art methods of graph embedding for richly attributed graphs.
Procédé de reconnaissance de caractères
HAL (Le Centre pour la Communication Scientifique Directe), Apr 4, 2011
Procede de reconnaissance de caracteres, mis en œuvre par un dispositif de reconnaissance de cara... more Procede de reconnaissance de caracteres, mis en œuvre par un dispositif de reconnaissance de caracteres, comprenant : - une etape (E1) de segmentation d'une image memorisee, comprenant, pour chaque pixel de l'image memorisee, la determination et la memorisation d'au moins une donnee de segmentation (cForme , cFond , cIndecision ) de l'image memorisee, et - au moins une etape (E2, E3, E4, E5) d'analyse ulterieure effectuee en fonction des donnees de segmentation (cForme , cFond , cIndecision ) memorisees.
Can artificial intelligence help decision-making in arthroscopy? Part 2: The IA-RTRHO model – a decision-making aid for long head of the biceps diagnoses in small rotator cuff tears
Orthopaedics & Traumatology: Surgery & Research
Can artificial intelligence help decision-making in arthroscopy? Part 1: Use of a standardized analysis protocol improves inter-observer agreement of arthroscopic diagnostic assessments of the long head of biceps tendon in small rotator cuff tears
Orthopaedics & Traumatology: Surgery & Research
Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Automatic or interactive segmentation tools for 3D medical images have been developed to help the... more Automatic or interactive segmentation tools for 3D medical images have been developed to help the clinicians. Atlas-based methods are one of the most usual techniques to localized anatomical structures. They have shown to be efficient with various types of medical images and various types of organs. However, a registration step is needed to perform an atlas-based segmentation which can be very time consuming. Local atlases coupled with spatial relationships have been proposed to solve this issue. Local atlases are defined on a sub-part of the image enabling a fast registration step. The positioning of these local atlases on the whole image can be done automatically with learned spatial relationships or interactively by a user when the automatic positioning is not well performed. In this article, different classification methods possibly included in local atlases segmentation methods are compared. Human brain and sheep brain MRI images have been used as databases for the experiments. Depending on the choice of the method, segmentation quality and computation time are very different. Graph-cut or CNN segmentation methods have shown to be more suitable for interactive segmentation because of their low computation time. Multi-atlas based methods like local weighted majority voting are more suitable for automatic process.
Personal identification by hand recognition
International audienceA new methodology for the person identification and verification using hand... more International audienceA new methodology for the person identification and verification using hand features is presented. The features are extracted from gray level hand images, which are scanned by an ordinary commercial scanner. Contrary to other bimodal biometric systems, the palmprint and hand geometry features are acquired from the same image. On their individual performances, these features are grouped into four different feature vectors. A k-NN classifier based on majority vote rule and distance-weighted rule is employed to establish four classifiers. Dempster-Shafer evidence theory is then used to combine these classifiers in case of identification. Besides, for verification step a simple majority rule was found robust for our system. Dempster-Shafer theory has proved to be much more efficient than fusion by others methods like majority vote rule and Borda count metho
Visual and structural feature combination in an interactive machine learning system for medical image segmentation
Machine Learning with Applications
Détermination évolutionnaire de classes d'équivalences de structures de réseaux bayésiens
HAL (Le Centre pour la Communication Scientifique Directe), 2007
International audienc
Vers une méthode de protection de données biométriques à l'aide de réseaux bayésiens
HAL (Le Centre pour la Communication Scientifique Directe), Nov 22, 2006
International audienc
Local Probabilistic Atlases and a Posteriori Correction for the Segmentation of Heart Images
Lecture Notes in Computer Science, 2018
Atlas-based segmentation is a well-known method for segmentation of medical images. In particular... more Atlas-based segmentation is a well-known method for segmentation of medical images. In particular, this method could be used in an efficient way to automatically segment heart structures in MRI or CT scans. We propose, in this paper a more adaptive and interactive atlas-based segmentation method. The model presented combines several local probabilistic atlases with a topological graph. The local atlases provide more refined information about the structures’ shape while the spatial relationships between the atlases are learned and stored in a graph. Hence, local registrations need less computational time and the image segmentation can be guided by the user in an incremental way. Following this step, a pixel classification is performed with a hidden Markov random field that integrates the learned a priori information with the pixel intensities that originate from different modalities. Finally, an a posteriori correction is performed using Adaboost classifiers in order to correct voxels in the border of the seek region and improve the precision of the results. The proposed method is tested on CT scan and MRI images of the heart coming from the MM-WHS challenge.
Apprentissage et exploitation d’un graphe topologique d’atlas probabilistes locaux pour la segmentation d’images IRM
International audienc
Two metrology applications in medical imaging
International audienc
Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2017
Atlas-based segmentation is a widely used method for Magnetic Resonance Imaging (MRI) segmentatio... more Atlas-based segmentation is a widely used method for Magnetic Resonance Imaging (MRI) segmentation. It is also a very efficient method for the automatic segmentation of brain structures. In this paper, we propose a more adaptive and interactive atlas-based method. The proposed model allows to combine several local probabilistic atlases with a topological graph. Local atlases can provide more precise information about the structure's shape and the spatial relationships between each of these atlases are learned and stored inside a graph representation. In this way, local registrations need less computational time and image segmentation can be guided by the user in an incremental way. Pixel classification is achieved with the help of a hidden Markov random field that is able to integrate the a priori information with the intensities coming from different modalities. The proposed method was tested on the OASIS dataset, used in the MICCAI'12 challenge for multi-atlas labeling.
Medical Image Analysis, 2019
Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart se... more Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be arduous due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, a set of training data is generally needed for constructing priors or for training. In addition, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets were limited. A number of them also reported poor results in the blinded evaluation, probably due to overfitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan. edu.cn/zhuangxiahai/0/mmwhs/).
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008
We propose a new hybrid protocol for cryptographically secure biometric authentication. The main ... more We propose a new hybrid protocol for cryptographically secure biometric authentication. The main advantages of the proposed protocol over previous solutions can be summarised as follows: (1) potential for much better accuracy using different types of biometric signals, including behavioural ones; and (2) improved user privacy, since user identities are not transmitted at any point in the protocol execution. The new protocol takes advantage of state-of-the-art identification classifiers, which provide not only better accuracy, but also the possibility to perform authentication without knowing who the user claims to be. Cryptographic security is based on the Paillier public key encryption scheme.
Algorithmes hybrides d'apprentissage de chaînes de Markov cachées: conception et applications à la reconnaissance des formes= Genetic hybridization of hidden …
La problématique de ce travail repose sur la qualité de modélisation de données (appelées observa... more La problématique de ce travail repose sur la qualité de modélisation de données (appelées observations) faite par des chaînes de Markov cachées (CMC). Notre objectif est alors de proposer des algorithmes permettant d'améliorer cette qualité. Le critère retenu pour quantifier la ...
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Papers by Thierry BROUARD