Ellipsoid-constrained robust fitting of quadrics with application to the 3D morphological characterization of articular surfaces.

This paper addresses the ellipsoid-type-specified fitting of quadratic surfaces, in the scope of model-based global feature extraction within scattered 3D point clouds. At characterizing articular bone surfaces, the quadrics estimated indicate useful overall-symmetry-related intrinsic centers and axes in joints. A constrained weighted least-squares minimization of algebraic residuals is used, with a robust and bias-corrected metric. With only one quadratic constraint involved, every step produces closed-form eigenvector solutions. To guarantee that an ellipsoid is output, we originally exploit a 2D representation called the Quadric Shape Map (QSM) by carrying out a visual study of the influence of shape constraints. The identified ellipsoid guarantee is needed to extract the center and axes in a wrist joint data stemming from 3D medical images.

Shoulder motion analysis using simultaneous skin shape registration.

A new non-invasive approach is proposed to study joint motions. It is based on dynamic tracking of the skin shape. A robust simultaneous registration algorithm (Iterative Median Closest Point) is used to follow the evolving shape and compute the rigid motion of the underlying bone structures. This new method relies on the differentiation of the rigid and elastic parts of the shape motion. A skin marker network is tracked by a set of infrared cameras. Unlike usual techniques, the algorithm tracks the instantaneous polyhedral shape embedding this network. This innovating approach is expected to minimize bias effect of skin sweeps and give some new information about the underlying soft tissue activities. Current application addresses the motion of the shoulder complex (humerus, clavicle and scapula). It is compared with two marker-based methods published in the literature. Preliminary results show significant differences between these three approaches. The new approach measurements give rise to greater rotations.

Modified data fidelity speed in anisotropic diffusion.

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.

Genetic algorithms for a robust 3-D MR-CT registration.

The aim of this paper is to present an original usage of genetic algorithms as a robust search space sampler in application to 3-D medical image elastic registration. An overview of the standard steps of a registration algorithm is given. We focus on the genetic algorithms use and particularly on the problem of extraction of the optimal solution among the final genetic population. We provide an original encoding scheme relying on a structural approach of point matching and then point out the need for a local optimization process. We then illustrate the algorithm with a concrete registration example and assert the results with a direct multivolume rendering tool. Finally, the algorithm is applied on the vanderbilt medical image database to assert the robustness and in order to compare it with other techniques.

A direct multi-volume rendering method aiming at comparisons of 3-D images and models.

We present a new method for direct volume rendering of multiple three-dimensional (3-D) functions using a density emitter model. This work aims at obtaining visual assessment of the results of a 3-D image registration algorithm which operates on anisotropic and non segmented medical data. We first discuss the fundamentals associated with direct, simultaneous rendering of such datasets. Then, we recall the fuzzy classification and fuzzy surface rendering theory within the density emitter model terminology, and propose an extension of standard direct volume rendering that can handle the rendering of two or more 3-D functions; this consists of the definition of merging rules that are applied on emitter clouds. The included rendering applications are related on one hand, to volume-to-volume registration, and on the other hand, to surface-to-volume registration: the first case is concerned with global elastic registration of CT data, and the second one presents fitting of an implicit surface over a CT data subset. In these two medical imaging application cases, our rendering scheme offers a comprehensive appreciation of the relative position of structural information.