Selective diffusion for oriented pattern extraction: Application to tagged cardiac MRI enhancement

Anisotropic regularization PDE’s (Partial Differential Equation) raised a strong interest in the field of image processing. The benefit of PDE-based regularization methods lies in the ability to smooth data in a nonlinear way, allowing the preservation of important image features (contours, corners or other discontinuities). In this article, a selective diffusion approach based on the framework of Extreme Physical Information theory is presented. It is shown that this particular framework leads to a particular regularization PDE which makes the integration of prior knowledge possible within the diffusion scheme. As a proof of feasibility, results of oriented pattern extractions are first presented on ad hoc images and second on a particular medical application: Tagged cardiac MRI (Magnetic Resonance Imaging) enhancement

Exploitation de données oculométriques pour une modélisation du processus d’interprétation d’examens TEP/SCAN

National audienc

Compression des images médicales avec et sans perte : Aspects techniques et validation clinique

De nombreuses méthodes de compression ont été développées afin de résoudre les problèmes d’archivage et de transmission des images numériques. Ces méthodes peuvent être dédiées 2D (JPEG-LS, JPEG, JPEG2000, SPlHT), 2D+T (MPEGlet2) ou 3D (SPIHT3D). Les normes de compression avec perte, telles que JPEG basée sur la transformation en cosinus et JPEG2000 basée sur la transformation en ondelettes, répondent aux exigences d’interopérabilité et de pérennité mais introduisent des distorsions (effet de bloc, flou) susceptibles d’entraîner des modifications dans l’interprétation diagnostique de l’image. Une évaluation subjective, voire objective, permettra de définir les taux de compression acceptables pour chaque type d’image

Diagnostic quality assessment of medical images: Challenges and trends

With medical imaging technologies growth, the question of their assessment on the impact and benefit on patient care is rising. Development and design of those medical imaging technologies should take into account the concept of image quality as it might impact the ability of practicians while they are using image information. Towards that goal, one should consider several human factors involved in image analysis and interpretation, e.g. image perception issues, decision process, image analysis pipeline (detection, localization, characterization...). While many efforts have been dedicated to objectively assess the value of imaging system in terms of ideal decision process, new trends have recently emerged to deal with human observer perfomances. This task effort is huge considering the variability of imaging acquisition methods and the possible pathologies. This paper proposes a survey of some key issues and results associated to this effort. We first outline the wide range of medical images with their own specific features. Next, we review the main methodologies to evaluate diagnostic quality of medical images from subjective assessment including ROC analysis, and diagnostic criteria quality analysis, to objective assessment including metrics based on the HVS, and model observers. At last, we present another evaluation method: eye-tracking studies to gain basic understanding of the visual search and decision-making process