Beam Hardening and Scattering Correction for a Quantitative X-Ray Inspection of Fuel Rod Welds

In radiography the value of each pixel is related to the material thickness crossed by the X-Rays. Using this relationship, a defect in an object can be located and furthermore characterized by parameters such as depth, surface and volume

Detection of regular boundaries in noisy and textured images : a multiscale active contour approach

This article deals with the detection of edges in noisy and textured images . After an overview of the shortcomings of local operators, we assess active contour models, which constrain the detection using a priori information . The implementation of active contours is difficult due to several problems . We suggest two methods which make this global approach more robust and easier to use . On one hand, the parameters are set using a calibration algorithm which relies on geometrical a priori . The value of the parameters depends upon the maximum curvature . On the other hand, a multiscale strategy reduces the need for an accurate initialization of active contours . The convergence is robust at coarse scales and the localization of edges is enhanced at fine scales . Several efficient algorithms are proposed to track contours over decreasing scale, using a prediction on the distorsion of boundaries caused by gaussian blurring . The experiments show the validity of our approach and the relevance of a cooperation between segmentation processes, especially to solve the initialization problem .Cet article traite de la détection de contours dans des images fortement bruitées et texturées. Après avoir exposé les limitations des opérateurs locaux, nous suggérons de contraindre la détection grâce aux modèles de contours actifs, qui introduisent des informations a priori sur la géométrie et la régularité des objets cherchés. La mise en oeuvre des contours actifs est difficile en raison de nombreux problèmes pratiques. Nous proposons deux techniques rendant cette approche globale plus robuste et plus facile d'emploi. D'une part, nous facilitons le choix des paramètres en adaptant un algorithme de calibrage ayant une explication géométrique : les paramètres dépendent de la valeur de la courbure maximale. L'association des contours actifs et d'une représentation multiéchelle permet d'autre part de réduire la dépendance vis-à-vis de l'initialisation. Après une convergence robuste vers une solution grossière, la localisation des contours est améliorée en diminuant progressivement l'échelle d'analyse. Nous proposons alors plusieurs techniques efficaces de suivi des contours dans l'espace-échelle, s'appuyant sur une prédiction du déplacement des frontières sous l'effet du lissage gaussien. Les résultats expérimentaux montrent la validité de notre approche, et mettent en évidence l'apport d'une collaboration entre processus de segmentation, en particulier pour automatiser l'initialisation

Reconstruction en imagerie gamma à partir d'acquisitions multi-énergie

Le problème traité concerne la reconstruction de la distribution 3D de sources radioactives lors d'examens scintigraphiques. Nous proposons une approche de reconstruction permettant d'exploiter les informations issues de capteurs spectrométriques et en particulier les informations portées par le rayonnement diffusé pour améliorer la qualité des images. La communication présente les résultats obtenus par une approche d'inversion s'appuyant sur un modèle précis de la physique de formation des projections en imagerie gamma. Cette méthode a été baptisée SCARECO (scatter recovery)

Lensfree diffractive tomography for the imaging of 3D cell cultures

International audienceNew microscopes are needed to help realize the full potential of 3D organoid culture studies. In order to image large volume of 3D organoid cultures while preserving the ability to catch every single cell, we propose a new imaging platform based on lensfree microscopy. We have built a lensfree diffractive tomography setup performing multi-angle acquisitions of 3D organoid culture embedded in Matrigel ® and developed a dedicated 3D holographic reconstruction algorithm based on the Fourier diffraction theorem. With this new imaging platform, we have been able to reconstruct a 3D volume as large as 21.5 mm 3 of a 3D organoid culture of prostatic RWPE1 cells showing the ability of these cells to assemble in 3D intricate cellular network at the mesoscopic scale. Importantly, comparisons with 2D images show that it is possible to resolve single cells isolated from the main cellular structure with our lensfree diffractive tomography setup

Time-resolved diffuse optical tomography for non-invasive flap viability assessment: Pre-clinical tests on rats

We present a new setup for time-resolved diffuse optical tomography based on multiple source-detector acquisitions analysed by means of the Mellin-Laplace transform. The proposed setup has been used to perform pre-clinical measurements on rats in order to show its suitability for non-invasive assessment of flap viability

SOLUS: Multimodal System Combining Ultrasounds and Diffuse Optics for Tomographic Imaging of Breast Cancer

An innovative multimodal system for breast imaging was developed combining in a single probe B-mode ultrasound, shear-wave elastography and multi-wavelength time-domain diffuse optical tomography. The clinical validation is ongoing aiming at improving the diagnostic specificity

Breast lesion classification based on absorption and composition parameters: a look at SOLUS first outcomes

A machine learning classification algorithm is applied to the SOLUS database to discriminate benign and malignant breast lesions, based on absorption and composition properties retrieved through diffuse optical tomography. The Mann-Whitney test indicates oxy-hemoglobin (p-value = 0.0007) and lipids (0.0387) as the most significant constituents for lesion classification, but work is in progress for further analysis. Together with sensitivity (91%), specificity (75%) and the Area Under the ROC Curve (0.83), special metrics for imbalanced datasets (27% of malignant lesions) are applied to the machine learning outcome: balanced accuracy (83%) and Matthews Correlation Coefficient (0.65). The initial results underline the promising informative content of optical data

Breast lesion classification based on absorption and composition parameters: a look at SOLUS first outcomes

A machine learning classification algorithm is applied to the SOLUS database to discriminate benign and malignant breast lesions, based on absorption and composition properties retrieved through diffuse optical tomography. The Mann-Whitney test indicates oxy-hemoglobin (p-value = 0.0007) and lipids (0.0387) as the most significant constituents for lesion classification, but work is in progress for further analysis. Together with sensitivity (91%), specificity (75%) and the Area Under the ROC Curve (0.83), special metrics for imbalanced datasets (27% of malignant lesions) are applied to the machine learning outcome: balanced accuracy (83%) and Matthews Correlation Coefficient (0.65). The initial results underline the promising informative content of optical data