Multimodal medical case retrieval using the Dezert-Smarandache theory.

International audienceMost medical images are now digitized and stored with semantic information, leading to medical case databases. They may be used for aid to diagnosis, by retrieving similar cases to those in examination. But the information are often incomplete, uncertain and sometimes conflicting, so difficult to use. In this paper, we present a Case Based Reasoning (CBR) system for medical case retrieval, derived from the Dezert-Smarandache theory, which is well suited to handle those problems. We introduce a case retrieval specific frame of discernment theta, which associates each element of theta with a case in the database; we take advantage of the flexibility offered by the DSmT's hybrid models to finely model the database. The system is designed so that heterogeneous sources of information can be integrated in the system: in particular images, indexed by their digital content, and symbolic information. The method is evaluated on two classified databases: one for diabetic retinopathy follow-up (DRD) and one for screening mammography (DDSM). On these databases, results are promising: the retrieval precision at five reaches 81.8% on DRD and 84.8% on DDSM

Multimedia data mining for automatic diabetic retinopathy screening

International audience— This paper presents TeleOphta, an automatic sys-tem for screening diabetic retinopathy in teleophthalmology networks. Its goal is to reduce the burden on ophthalmologists by automatically detecting non referable examination records, i.e. examination records presenting no image quality problems and no pathological signs related to diabetic retinopathy or any other retinal pathology. TeleOphta is an attempt to put into practice years of algorithmic developments from our groups. It combines image quality metrics, specific lesion detectors and a generic pathological pattern miner to process the visual content of eye fundus photographs. This visual information is further combined with contextual data in order to compute an abnormality risk for each examination record. The TeleOphta system was trained and tested on a large dataset of 25,702 examination records from the OPHDIAT screening network in Paris. It was able to automatically detect 68% of the non referable examination records while achieving the same sensitivity as a second ophthalmologist. This suggests that it could safely reduce the burden on ophthalmologists by 56%

Exudate detection in color retinal images for mass screening of diabetic retinopathy

International audienceThe automatic detection of exudates in colour eye fundus images is an important task in applications such as diabetic retinopathy screening. The presented work has been undertaken in the framework of the TeleOphta project, whose main objective is to auto-matically detect normal exams in a tele-ophthalmology network, thus reducing the burden on the readers. A new clinical database, e-ophtha EX, containing precisely manually contoured exudates, is introduced. As opposed to previously available databases, e-ophtha EX is very heterogeneous. It contains images gathered within the OPHDIAT telemedicine network for diabetic retinopathy screening. Image definition, quality, as well as patients condition or the retinograph used for the acquisition, for example, are subject to important changes between different examinations. The proposed exudate detection method has been designed for this complex situation. We propose new preprocessing methods, which perform not only normalization and denoising tasks, but also de-tect reflections and artifacts in the image. A new candidates segmentation method, based on mathematical morphology, is proposed. These candidates are characterized using classical features, but also novel contextual features. Finally, a random forest algorithm is used to detect the exudates among the candidates. The method has been validated on the e-ophtha EX database, obtaining an AUC of 0.95. It has been also validated on other databases, obtaining an AUC between 0.93 and 0.95, outperforming state-of-the-art methods

Indexation et fusion multimodale pour la recherche d'information par le contenu. Application aux bases de données d'images médicales.

site web / webpage : http://latim.univ-brest.fr ; http://pagesperso.univ-brest.fr/~quellec/In this Ph.D. thesis, we study methods for information retrieval in databases made up of multimedia documents. Our objective is to select in a database documents similar to a query document. The aimed application is computer aided diagnosis in a medical framework: the database is made up of several images together with clinical contextual information about the patient. We first try to characterize each image in the patient file individually. We have thus proposed two original indexing methods derived from the wavelet transform of images: 1) a global method, modeling the distribution of wavelet coefficients in the image, 2) a local method, based on the extraction of lesions. Once images are characterized, we try to fuse all the information in the file to retrieve the closest patient files. In addition to the heterogeneity of the data, with have to cope with missing information in patient files. We propose three new approaches, derived from data mining and information fusion theory. The first approach is based on decision trees, the second one on Bayesian networks and the third one on the Dezert-Smarandache theory (DSmT). The results obtained on two multimodal medical databases are satisfying and superior to existing methods. Thus, the mean precision at five reaches 81.78% on a retinal image database and 92.90% on a mammography database.Dans cette thèse, nous étudions des méthodes pour la recherche d'information dans des bases de données constituées de documents multimédia. Notre objectif est de sélectionner dans la base des documents similaires à un document proposé en requête. L'application visée est l'aide au diagnostic dans un cadre médical : la base est constituée de dossiers patients contenant plusieurs images et des informations cliniques contextuelles à propos du patient. Dans un premier temps, nous cherchons à caractériser individuellement chaque image du dossier patient. Nous avons ainsi proposé deux méthodes originales d'indexation à partir de la transformée en ondelettes des images : 1) une méthode globale, modélisant la distribution des coefficients d'ondelette dans l'image, 2) une méthode locale, basée sur l'extraction de lésions. Une fois les images caractérisées, nous cherchons à fusionner l'ensemble des informations du dossier pour sélectionner les dossiers patients les plus proches. Outre le problème de l'hétérogénéité des données, nous devons résoudre le problème de l'incomplétude des dossiers patients. Nous proposons trois nouvelles approches, inspirées de la fouille de données et de la fusion d'information. La première est basée sur les arbres de décision, la deuxième sur les réseaux bayésiens et la troisième sur la théorie de Dezert-Smarandache (DSmT). Les résultats que nous obtenons pour deux bases de données médicales multimodales sont très satisfaisants et supérieurs aux méthodes classiques. Ainsi, la précision moyenne pour une fenêtre de cinq cas atteint 81,78% pour une base d'images rétiniennes et 92,90% pour une base de mammographies

Procédé d'analyse sémantique d'un flux vidéo en cours d'acquisition, terminal, produit programme d'ordinateur et medium correspondant

Copropriétaire : UBODate de dépôt : 24/10/2014N° de dépôt : PCT/EP2014/07289

Procédé d'analyse sémantique d'un flux vidéo en cours d'acquisition, terminal, produit programme d'ordinateur et medium correspondant

Copropriétaire : UBODate de dépôt : 24/10/2014N° de dépôt : PCT/EP2014/07289

Content-Based Image Retrieval in Homomorphic Encryption Domain

International audienceIn this paper, we propose a secure implementation of a content-based image retrieval (CBIR) method that makes possible diagnosis aid systems to work in externalized environment and with outsourced data as in cloud computing. This one works with homomorphic encrypted images from which it extracts wavelet based image features next used for subsequent image comparison. By doing so, our system allows a physician to retrieve the most similar images to a query image in an outsourced database while preserving data confidentiality. Our Secure CBIR is the first one that proposes to work with global image features extracted from encrypted images and does not induce extra communications in-between the client and the server. Experimental results show it achieves retrieval performance as good as if images were processed non-encrypted

Secure content based image retrieval in medical databases

National audienceIn this paper, we propose an implementation in the encrypted domain of a content based image retrieval (CBIR) method. It allows a physician to retrieve the most similar images to a query image in an outsourced database while preserving data confidentiality. Image retrieval is based on image signatures we build in the hormomorphically encrypted wavelet transform domain. Experimental results show it is possible to achieve retrieval performance as good as if images were processed nonencrypted