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 entrepreneurial operating system (designed by EOS-Imaging Company). This method is based on a 2-D-3-D registration of EOS bi-planar X-ray images with an EOS 3-D 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 3-D surface reconstruction with its attached software. In this paper, the pose and position of a bone in radiographs is estimated through the link between 3-D and 2-D 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 3-D data and the radiographs, and use an iterative optimization approach to converge toward 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.

In this paper, we use an anisotropic diffusion in a level set framework for low-level segmentation of necrotic femoral heads. Our segmentation is based on three speed terms. The first one includes an adaptive estimation of the contrast level. We use the entropy for evaluating our diffusion on synthetic 3D data. We notice that using the data fidelity term in the last iterations excessively penalizes the diffusion process. To provide better segmentation results, we propose some modifications in the data fidelity speed: we propose to build its reference data term from previous iterations results and hence lessening influence of initial noisy data.

The use of remote sensors (thermometers and cameras) to analyse crop water status in field conditions is fraught with several difficulties. In particular, average canopy temperature measurements are affected by the mixture of soil and green regions, the mutual shading of leaves and the variability of absorbed radiation. The aim of the study was to analyse how the selection of different 'regions of interest' (ROI) in canopy images affect the variability of the resulting temperature averages. Using automated image segmentation techniques we computed the average temperature in four nested ROI of decreasing size, from the whole image down to the sunlit fraction of a leaf located in the upper part of the canopy. The study was conducted on maize (Zea mays L.) at the flowering stage, for its large leaves and well structured canopy. Our results suggest that, under these conditions, the ROI comprising the sunlit fraction of a leaf located in the upper part of the canopy should be analogous to the single leaf approach (in controlled conditions) that allows the estimation of stomatal conductance or plant water potential.

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