The performances of parametric or non-parametric Hypothesis Testing (HT) for nonlinearity detection are fairly weak for small time series (typically between 128 and 512 samples). A natural idea to improve the results is to merge several HT to make a more robust decision. In this paper, we inspect the topology to perform this fusion. However three steps are needed before optimizing this fusion process. The first one is a rigorous estimate of the robustness of the 12 selected HT in order to only keep the more robust ones. The second one is the validation of the surrogate data method to estimate the index pdf under H0 (i.e. the observed time series is "linear"). In fact, this pdf is necessary to define the threshold to accept/reject the null hypothesis of linearity. The last step is also an estimate of the mutual information between the indices involved in the fusion process, since the fusion of close indices cannot improve the decision. Numerical results show that the method of fusion changes when the time series length increases

In this paper, a new method is presented to study the feasibility of the pose and the position estimation of bone structures using a low dose radiographic system, the EOS system (designed by Eos-Imaging Company). This method is based on a 2D 3D registration of EOS bi-planar X-ray images with an EOS 3D reconstruction. This technique is relevant to such an application thanks to the EOS ability to simultaneously make acquisitions of frontal and sagittal radiographs, and also to produce a 3D surface reconstruction with its attached software. In this paper, the pose and position of a bone in radiographs is estimated through the link between 3D and 2D data. This relationship is established in the frequency domain using the Fourier central slice theorem. To estimate the pose and position of the bone, we define a distance between the 3D data and the radiographs and use an iterative optimization approach to converge towards the best estimation. In this paper, we give the mathematical details of the method. We also show the experimental protocol and the results, which validate our approach.

This work proposes an image segmentation model based on active contours. For a better handling of regions where anatomical structures are poorly contrasted and/or missing, we propose to incorporate a priori shape information in a variational formulation. Based on a level set approach, the proposed functional is composed of four terms. The first one makes the level set keep the important signed distance function property, which is necessary to guarantee the good level set evolution. Doing so results in avoiding the classical re-initialization process, contrary to most existing works where a partial differential equation is used instead. The second energy term contains the a priori information about admissible shapes of the target object, the latter being integrated in the level set evolution. An energy that drives rapidly the level set towards objects of interest is defined in the third term. A last term is defined on prior shapes thanks to a complete and modified Mumford-Shah model. The segmentation model is derived by solving the Euler-Lagrange equations associated to the functional minimization. Efficiency and robustness of our segmentation model are validated on synthetic images, digitally reconstructed images, and real image radiographs. Quantitative evaluations of segmentation results are also provided, which also show the importance of prior shapes in the context of image segmentation.

Optoelectronic systems are widely used in 3D motion capture. However, the reliability of themotion estimation depends on soft tissue artefacts and should therefore be validated. Two different sets of humeral markers were studied on four subjects. Anatomical and kinematic measurements were combined and the plausibility of the relative position of the bones in the glenohumeral joint duringmotionwas evaluated using a new coherence index.Our findings showthat an identical protocol leads to a large variability of the articular coherence for the subjects. However, the use of an extra marker on the distal part of the humerus improves the humeral kinematics for three of the four subjects. Scientists and clinicians using 3D systems should remain aware of the influence of subject-specificmorphology on the accuracy of themeasure.Differences with a reference group may come from clinical reasons but also from measurement errors due to the inter-individual morphological differences.