In this paper we propose new algorithms for accurate nonrigid motion tracking. Given only a set of sparse correspondences and incomplete or missing information about geometry or material properties, we recover dense motion vectors using nonlinear finite element models. The method is based on the iterative analysis of the differences between the actual and predicted behavior. Large differences indicate that an object's properties are not captured properly by the model. Feedback from the images during the motion allows the refinement of the model by minimizing the error between the expected and true position of the object's points. Unknown parameters are recovered using an iterative descent search for the best model that approximates nonrigid motion of the given object. Thus, during tracking the model is refined which, in turn, improves tracking quality. The method was applied successfully to man-made elastic materials and human skin to recover unknown elasticity, to complex 3-D objects to find...

In our previous work we used finite element models to determine nonrigid motion parameters and recover unknown local properties of objects given correspondence data recovered with snakes or other tracking models. In this paper we present a novel multiscale approach to recovery of nonrigid motion from sequences of registered intensity and range images. The main idea of our approach is that a finite element (FEM) model incorporating material properties of the object can naturally handle both registration and deformation modeling using a single model-driving strategy. The method includes a multiscale iterative algorithm based on analysis of the undirected Hausdorff distance to recover correspondences. The method is evaluated with respect to speed and accuracy. Noise sensitivity issues are addressed. Advantages of the proposed approach are demonstrated using man-made elastic materials and human skin motion. Experiments with regular grid features are used for performance comparison with a conventional approach (separate snakes and FEM models). It is shown, however, that the new method does not require a sampling/correspondence template and can adapt the model to available object features. Usefulness of the method is presented not only in the context of tracking and motion analysis, but also for a burn scar detection application.

The success of nonrigid motion analysis using physical finite element model is dependent on the mesh that characterizes the object's geometric structure. We suggest a deformable mesh adapted to the natural features of images. The adaptive mesh requires much fewer number of nodes than the fixed mesh which was used in our previous work. We demonstrate the higher efficiency of the adaptive mesh in the context of estimating burn scar elasticity relative to normal skin elasticity using the observed 2D image sequence. Our results show that the scar assessment method based on the physical model using natural feature adaptive mesh can be applied to images which do not have artificial markers.

Abstract — A modeling approach is presented for quantitative burn scar assessment. Emphases are given to: (1) constructing a finite element model from natural image features with an adaptive mesh, and (2) quantifying the Young’s modulus of scars using the finite element model and regularization method. A set of natural point features is extracted from the images of burn patients. A Delaunay triangle mesh is then generated that adapts to the point features. A 3D finite element model is built on top of the mesh with the aid of range images providing the depth information. The Young’s modulus of scars is quantified with a simplified regularization functional, assuming that the knowledge of scar’s geometry is available. The consistency between the Relative Elasticity Index and the physician’s rating based on the Vancouver Scale (a relative scale used to rate burn scars) indicates that the proposed modeling approach has high potentials for image-based quantitative burn scar assessment. Index Terms — Physical model, elastic property, burn scar, natural feature, finite element, regularization. I.

This paper presents a physical model-based method for recovering and tracking nonrigid motion of elastic objects. The proposed method recovers the motion in terms of actual physical parameters (Young's modulus) that characterize the dynamics of the objects. The tracking scheme synthesizes the motion of the points inside the object from the boundary observations, constrained by the physical parameters. Experiments on three image sequences show that using the recovered physical parameters as constraints can greatly improve the tracking quality.

Abstract—This paper presents a constrained genetic approach for reconstructing the material properties of elastic objects. The considered reconstruction problem is ill-posed and must be constrained properly so that a unique and stable numerical solution can be obtained. Qualitative prior information is incorporated using a rank-based scheme to constrain the admissible solutions. Experiments show that the proposed approach is robust when presented with noisy data and can reconstruct the elastic property accurately and reliably. In a comparison study with the deterministic Gauss–Newton methods, the constrained genetic approach also shows very consistent performance. Index Terms—Constrained genetic algorithm (CGA), elastic property, finite-element model, inverse problem. I.

Performing regular mammographic screening and comparing corresponding mammograms taken from multiple views or at different times are necessary for early detection and treatment evaluation of breast cancer, which is key to successful treatment. However, mammograms taken at different times are often obtained under different compression, orientation, or body position. A temporal pair of mammograms may vary significantly due to the spatial disparities caused by the variety in acquisition environments, including 3D position of the breast, the amount of pressure applied, etc. Such disparities can be corrected through the process of temporal registration. We propose to use a 3D finite element model for temporal registration of digital mammography. In this paper, we apply patient specific 3D breast model constructed from MRI data of the patient, for cases where lesions are detectable in multiple mammographic views across time. The 3D location of the lesion in the breast model is computed through a breast deformation simulation step presented in our earlier work. Lesion correspondence is established by using a nearest neighbor approach in the uncompressed breast volume. Our experiments show that the use of a 3D finite element model for simulating and analyzing breast deformation contributes to good accuracy when matching suspicious regions in temporal mammograms.