Mechanical based rigid registration of 3D objects: application to multimodal medical images

The registration of 3-D objects is an important problem in computer vision and especially in medical imaging. It arises when data acquired by different sensors and/or at different times have to be fused. Under the basic assumption that the objects to be registered are rigid, the problem is to recover the six parameters of a rigid transformation. If landmarks or common characteristics are not available, the problem has to be solved by an iterative method . However such methods are inevitably attracted to local minima. This paper presents a novel iterative method designed for the rigid registration of 3-D objects . Its originality lies in its physical basis : instead of minimizing an energy function with respect to the parameters of the rigid transformation (the classical approach) the minimization is achieved by studying the motion of a rigid object in a potential field. In particular we consider the kinetic energy of the solid during the registration process, which allows it to "jump over" some local maxima of the potential energy and so avoid some local minima of that energy. We present extensive experimental results on real 3-D medical images. In that particular application, we perform the matching process with the whole segmented volumes .La mise en correspondance d'objets 3D est un problème important dans le domaine du traitement d'image. Il apparaît lorsque des données acquises par différents capteurs, à des moments ou/et des instants différents doivent être fusionnées. Si l'on suppose que les objets à mettre en correspondance sont rigides, nous avons a retrouver les paramètres d'une transformation rigide. Lorsque l'utilisatin d'amers ou de caractéristiques communes n'est pas possible pour résoudre cette tache, une méthode itérative peut êre utilisée avec profit. Cet article présente une méthode itérative générale pour la mise en correspondance d'objets 3D. Son originalité réside dans ses fondements mecaniques: plutôt que de minimiser une énergie potentielle par rapport aux paramètres de la transformation rigide, qui est l'approche classique, nous étudions le mouvement d'un objet rigide, c'est-à-dire un solide, dans un champ de potentiel. Cette approche particulière prend en compte l'énergie cinétique du solide, ce qui permet de «sauter» certains maxima locaux de l'énergie potentielle et donc d'en éviter certains minima locaux. Nous montrons que notre approche, si l'on considère l'énergie cinétique toujours nulle, est équivalente à une méthode de descente de gradient, l'introduction de la vitesse permet donc d'en accélérer la convergence. En outre, nous montrons que notre méthode se laisse moins facilement «piéger» par les minima locaux de l'énergie que les méthodes classiques de minimisation. L'article est illustré par l'application de la méthode au recalage d'images médicales réelles, ou nous utilisons la totalité du volume segment

Longitudinal changes in functional connectivity of cortico-basal ganglia networks in manifests and premanifest huntington's disease

Huntington's disease (HD) is a genetic neurological disorder resulting in cognitive and motor impairments. We evaluated the longitudinal changes of functional connectivity in sensorimotor, associative and limbic cortico-basal ganglia networks. We acquired structural MRI and resting-state fMRI in three visits one year apart, in 18 adult HD patients, 24 asymptomatic mutation carriers (preHD) and 18 gender- and age-matched healthy volunteers from the TRACK-HD study. We inferred topological changes in functional connectivity between 182 regions within cortico-basal ganglia networks using graph theory measures. We found significant differences for global graph theory measures in HD but not in preHD. The average shortest path length (L) decreased, which indicated a change toward the random network topology. HD patients also demonstrated increases in degree k, reduced betweeness centrality bc and reduced clustering C. Changes predominated in the sensorimotor network for bc and C and were observed in all circuits for k. Hubs were reduced in preHD and no longer detectable in HD in the sensorimotor and associative networks. Changes in graph theory metrics (L, k, C and bc) correlated with four clinical and cognitive measures (symbol digit modalities test, Stroop, Burden and UHDRS). There were no changes in graph theory metrics across sessions, which suggests that these measures are not reliable biomarkers of longitudinal changes in HD. preHD is characterized by progressive decreasing hub organization, and these changes aggravate in HD patients with changes in local metrics. HD is characterized by progressive changes in global network interconnectivity, whose network topology becomes more random over time. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc

Do personality traits affect productivity? Evidence from the lab

While survey data supports a strong relationship between personality and labor market outcomes, the exact mechanisms behind this association remain unexplored. In this paper, we take advantage of a controlled laboratory set-up to test whether this relationship operates through productivity, and isolate this mechanism from other channels such as bargaining ability or self-selection into jobs. Using a gender neutral real-effort task, we analyse the impact of the Big Five personality traits on performance. We find that more neurotic subjects perform worse, and that more conscientious individuals perform better. These findings are in line with previous survey studies and suggest that at least part of the effect of personality on labor market outcomes operates through productivity. In addition, we find evidence that gender and university major affect the impact of the Big Five personality traits on performance

Decoherence by engineered quantum baths

We introduce, and determine decoherence for, a wide class of non-trivial quantum spin baths which embrace Ising, XY and Heisenberg universality classes coupled to a two-level system. For the XY and Ising universality classes we provide an exact expression for the decay of the loss of coherence beyond the case of a central spin coupled uniformly to all the spins of the baths which has been discussed so far in the literature. In the case of the Heisenberg spin bath we study the decoherence by means of the time-dependent density matrix renormalization group. We show how these baths can be engineered, by using atoms in optical lattices.Comment: 4 pages, 4 figure

Observational Limits on Machos in the Galactic Halo

We present final results from the first phase of the EROS search for gravitational microlensing of stars in the Magellanic Clouds by unseen deflectors (machos: MAssive Compact Halo Objects). The search is sensitive to events with time scales between 15 minutes and 200 days corresponding to deflector masses in the range 1.e-7 to a few solar masses. Two events were observed that are compatible with microlensing by objects of mass of about 0.1 Mo. By comparing the results with the expected number of events for various models of the Galaxy, we conclude that machos in the mass range [1.e-7, 0.02] Mo make up less than 20% (95% C.L.) of the Halo dark matter.Comment: 4 pages, 3 Postscript figures, to be published in Astronomy & Astrophysic

Electromagnetic duality symmetry and helicity conservation for the macroscopic Maxwell's equations (previously "Experimental demonstration of electromagnetic duality symmetry breaking")

Modern physics is largely devoted to study conservation laws, such as charge, energy, linear momentum or angular momentum, because they give us information about the symmetries of our universe. Here, we propose to add the relationship between electromagnetic duality and helicity to the toolkit. Generalized electromagnetic duality symmetry, broken in the microscopic Maxwell's equations by the empirical absence of magnetic charges, can be restored for the macroscopic Maxwell's equations. The restoration of this symmetry is shown to be independent of the geometry of the problem. These results provide a simple and powerful tool for the study of light-matter interactions within the framework of symmetries and conservation laws. We apply such framework to the experimental investigation of helicity transformations in cylindrical nanoapertures, and we find that the transformation is significantly enhanced by the coupling to surface modes, where electromagnetic duality is strongly broken.Comment: 26 pages, 4 figure

Extraction of Knowledge Rules for the Retrieval of Mesoscale Oceanic Structures in Ocean Satellite Images

The processing of ocean satellite images has as goal the detection of phenomena related with ocean dynamics. In this context, Mesoscale Oceanic Structures (MOS) play an essential role. In this chapter we will present the tool developed in our group in order to extract knowledge rules for the retrieval of MOS in ocean satellite images. We will describe the implementation of the tool: the workflow associated with the tool, the user interface, the class structure, and the database of the tool. Additionally, the experimental results obtained with the tool in terms of fuzzy knowledge rules as well as labeled structures with these rules are shown. These results have been obtained with the tool analyzing chlorophyll and temperature images of the Canary Islands and North West African coast captured by the SeaWiFS and MODIS-Aqua sensors

Bias in data-driven artificial intelligence systems - An introductory survey

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues