La compétence en matière contractuelle selon le règlement 44/2001 "Bruxelles I" et la Convention de Lugano révisée à la Suite de l'arrêt CJCE Color Drack

Ce volume recueille les actes du colloque qui sest tenu le 16 mars 2007 à lInstitut suisse de droit comparé (Lausanne) à loccasion de la 19e Journée de droit international privé. Le processus de révision de la Convention de Lugano, instrument dimportance centrale dans le contentieux international impliquant la Suisse, aura duré plus de dix ans. La signature de la Convention révisée est intervenue, pour le compte de la Suisse, le 30 octobre 2007. Le texte a subi un remodelage à laune du droit européen, tout en consacrant sans bouleversement majeur les acquis de la jurisprudence. Limportance de cet événement a incité une dizaine dexperts suisses et étrangers, spécialistes chevronnés et jeunes chercheurs à faire le point sur les quelques innovations du texte ainsi quà rappeler les solutions originelles qui ont résisté au temps.

Grapheme-color synaesthesia is associated with a distinct cognitive style

In this study we investigated whether synaesthesia is associated with a particular cognitive style. Cognitive style refers to preferred modes of information processing, such as a verbal style or a visual style. We reasoned that related to the enriched world of experiences created by synaesthesia, its association with enhanced verbal and visual memory, higher imagery and creativity, synaesthetes might show enhanced preference for a verbal as well as for a visual cognitive style compared to non-synaesthetes. In Study 1 we tested a large convenience sample of 1046 participants, who classified themselves as grapheme-color, sound-color, lexical-gustatory, sequence-space, or as non-synaesthetes. To assess cognitive style, we used the revised verbalizer-visualizer questionnaire (VVQ), which involves three independent cognitive style dimensions (verbal style, visual-spatial style, and vivid imagery style). The most important result was that those who reported grapheme-color synaesthesia showed higher ratings on the verbal and vivid imagery style dimensions, but not on the visual-spatial style dimension. In Study 2 we replicated this finding in a laboratory study involving 24 grapheme-color synaesthetes with objectively confirmed synaesthesia and a closely matched control group. Our results indicate that grapheme-color synaesthetes prefer both a verbal and a specific visual cognitive style. We suggest that this enhanced preference, probably together with the greater ease to switch between a verbal and a vivid visual imagery style, may be related to cognitive advantages associated with grapheme color synaesthesia such as enhanced memory performance and creativity.

Robust Denoising using Feature and Color Information

We propose a method that robustly combines color and feature buffers to denoise Monte Carlo renderings. On one hand, feature buffers, such as per pixel normals, textures, or depth, are effective in determining denoising filters because features are highly correlated with rendered images. Filters based solely on features, however, are prone to blurring image details that are not well represented by the features. On the other hand, color buffers represent all details, but they may be less effective to determine filters because they are contaminated by the noise that is supposed to be removed. We propose to obtain filters using a combination of color and feature buffers in an NL-means and cross-bilateral filtering framework. We determine a robust weighting of colors and features using a SURE-based error estimate. We show significant improvements in subjective and quantitative errors compared to the previous state-of-the-art. We also demonstrate adaptive sampling and space-time filtering for animations.

Female preference for male color is necessary and sufficient for assortative mating in 2 cichlid sister species

A critical step for speciation in the face of gene flow is the origination of reproductive isolation. The evolution of assortative mating greatly facilitates this process. Assortative mating can be mediated by one or multiple cues across an array of sensory modalities. We here explore possible cues that may underlie female mate choice in a sympatric species pair of cichlid fish from Lake Victoria, Pundamilia pundamilia and Pundamilia nyererei. Previous studies identified species-specific female preferences for male coloration, but effects of other cues could not be ruled out. Therefore, we assessed female choice in a series of experiments in which we manipulated visual (color) and chemical cues. We show that the visibility of differences in nuptial hue (i.e., either blue or red) between males of the 2 species is necessary and sufficient for assortative mating by female mate choice. Such assortment mediated by a single cue may evolve relatively quickly, but could make reproductive isolation vulnerable to environmental changes. These findings confirm the important role of female mate choice for male nuptial hue in promoting the explosive speciation of African haplochromine cichlids.

Hierarchical segmentation-assisted multimodal registration for MR brain images

Information theory-based metric such as mutual information (MI) is widely used as similarity measurement for multimodal registration. Nevertheless, this metric may lead to matching ambiguity for non-rigid registration. Moreover, maximization of MI alone does not necessarily produce an optimal solution. In this paper, we propose a segmentation-assisted similarity metric based on point-wise mutual information (PMI). This similarity metric, termed SPMI, enhances the registration accuracy by considering tissue classification probabilities as prior information, which is generated from an expectation maximization (EM) algorithm. Diffeomorphic demons is then adopted as the registration model and is optimized in a hierarchical framework (H-SPMI) based on different levels of anatomical structure as prior knowledge. The proposed method is evaluated using Brainweb synthetic data and clinical fMRI images. Both qualitative and quantitative assessment were performed as well as a sensitivity analysis to the segmentation error. Compared to the pure intensity-based approaches which only maximize mutual information, we show that the proposed algorithm provides significantly better accuracy on both synthetic and clinical data.

Integrated Segmentation of Brain Tumor Images for Radiotherapy and Neurosurgery

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

Fully automatic X-ray image segmentation via joint estimation of image displacements.

We propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. Our algorithm works by estimating the displacements from image patches to the (unknown) landmark positions and then integrating them via voting. The fundamental contribution is that, we jointly estimate the displacements from all patches to multiple landmarks together, by considering not only the training data but also geometric constraints on the test image. The various constraints constitute a convex objective function that can be solved efficiently. Validated on three challenging datasets, our method achieves high accuracy in landmark detection, and, combined with statistical shape model, gives a better performance in shape segmentation compared to the state-of-the-art methods.

Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs.

PURPOSE    Segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs is required to create a three-dimensional model of the hip joint for use in planning and treatment. However, manually extracting the femoral contour is tedious and prone to subjective bias, while automatic segmentation must accommodate poor image quality, anatomical structure overlap, and femur deformity. A new method was developed for femur segmentation in AP pelvic radiographs. METHODS    Using manual annotations on 100 AP pelvic radiographs, a statistical shape model (SSM) and a statistical appearance model (SAM) of the femur contour were constructed. The SSM and SAM were used to segment new AP pelvic radiographs with a three-stage approach. At initialization, the mean SSM model is coarsely registered to the femur in the AP radiograph through a scaled rigid registration. Mahalanobis distance defined on the SAM is employed as the search criteria for each annotated suggested landmark location. Dynamic programming was used to eliminate ambiguities. After all landmarks are assigned, a regularized non-rigid registration method deforms the current mean shape of SSM to produce a new segmentation of proximal femur. The second and third stages are iteratively executed to convergence. RESULTS    A set of 100 clinical AP pelvic radiographs (not used for training) were evaluated. The mean segmentation error was [Formula: see text], requiring [Formula: see text] s per case when implemented with Matlab. The influence of the initialization on segmentation results was tested by six clinicians, demonstrating no significance difference. CONCLUSIONS    A fast, robust and accurate method for femur segmentation in digital AP pelvic radiographs was developed by combining SSM and SAM with dynamic programming. This method can be extended to segmentation of other bony structures such as the pelvis.

Robust Proximal Femur Segmentation in Conventional X-Ray Images via Random Forest Regression on Multi-resolution Gradient Features

In this paper, we propose a fully automatic, robust approach for segmenting proximal femur in conventional X-ray images. Our method is based on hierarchical landmark detection by random forest regression, where the detection results of 22 global landmarks are used to do the spatial normalization, and the detection results of the 59 local landmarks serve as the image cue for instantiation of a statistical shape model of the proximal femur. To detect landmarks in both levels, we use multi-resolution HoG (Histogram of Oriented Gradients) as features which can achieve better accuracy and robustness. The efficacy of the present method is demonstrated by experiments conducted on 150 clinical x-ray images. It was found that the present method could achieve an average point-to-curve error of 2.0 mm and that the present method was robust to low image contrast, noise and occlusions caused by implants.

Fully Automatic Segmentation of AP Pelvis X-rays via Random Forest Regression and Hierarchical Sparse Shape Composition

Knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic and robust approach for landmarking and segmentation of both pelvis and femur in a conventional AP X-ray. Our approach is based on random forest regression and hierarchical sparse shape composition. Experiments conducted on 436 clinical AP pelvis x-rays show that our approach achieves an average point-to-curve error around 1.3 mm for femur and 2.2 mm for pelvis, both with success rates around 98%. Compared to existing methods, our approach exhibits better performance in both the robustness and the accuracy.

Mapping material distribution in a heterogeneous sand tank by image analysis

Water flow and solute transport through soils are strongly influenced by the spatial arrangement of soil materials with different hydraulic and chemical properties. Knowing the specific or statistical arrangement of these materials is considered as a key toward improved predictions of solute transport. Our aim was to obtain two-dimensional material maps from photographs of exposed profiles. We developed a segmentation and classification procedure and applied it to the images of a very heterogeneous sand tank, which was used for a series of flow and transport experiments. The segmentation was based on thresholds of soil color, estimated from local median gray values, and of soil texture, estimated from local coefficients of variation of gray values. Important steps were the correction of inhomogeneous illumination and reflection, and the incorporation of prior knowledge in filters used to extract the image features and to smooth the results morphologically. We could check and confirm the success of our mapping by comparing the estimated with the designed sand distribution in the tank. The resulting material map was used later as input to model flow and transport through the sand tank. Similar segmentation procedures may be applied to any high-density raster data, including photographs or spectral scans of field profiles.

Different dark conformations function in color-sensitive photosignaling by the sensory rhodopsin I-HtrI complex.

The haloarchaeal phototaxis receptor sensory rhodopsin I (SRI) in complex with its transducer HtrI delivers an attractant signal from excitation with an orange photon and a repellent signal from a second near-UV photon excitation. Using a proteoliposome system with purified SRI in complex with its transducer HtrI, we identified by site-directed fluorescence labeling a site (Ser(155)) on SRI that is conformationally active in signal relay to HtrI. Using site-directed spin labeling of Ser(155)Cys with a nitroxide side chain, we detected a change in conformation following one-photon excitation such that the spin probe exhibits a splitting of the outer hyperfine extrema (2A'(zz)) significantly smaller than that of the electron paramagnetic resonance spectrum in the dark state. The dark conformations of five mutant complexes that do not discriminate between orange and near-UV excitation show shifts to lower or higher 2A'(zz) values correlated with the alterations in their motility behavior to one- and two-photon stimuli. These data are interpreted in terms of a model in which the dark complex is populated by two conformers in the wild type, one that inhibits the CheA kinase (A) and the other that activates it (R), shifted in the dark by mutations and shifted in the wild-type SRI-HtrI complex in opposite directions by one-photon and two-photon reactions.

Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.