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 three-dimensional 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 the 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 potential for image-based quantitative burn scar assessment.

Skin detection is an important process in many of computer vision algorithms. It usually is a process that starts at a pixel-level, and that involves a pre-process of colorspace transformation followed by a classification process. A colorspace transformation is assumed to increase separability between skin and non-skin classes, to increase similarity among different skin tones, and to bring a robust performance under varying illumination conditions, without any sound reasonings. In this work, we examine if the colorspace transformation does bring those benefits by measuring four separability measurements on a large dataset of 805 images with different skin tones and illumination. Surprising results indicate that most of the colorspace transformations do not bring the benefits which have been assumed.

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.

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 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, accuracy, and noise sensitivity. 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 that the new method does not require a grid and can adapt the model to available object features.

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 the work by Tsap et al. (1998). We demonstrate the higher efficiency of the adaptive mesh in the context of estimating burn scare 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.

In this paper we propose a method for increasing precision and reliability of elasticity analysis in complicated burn scar cases. The need for a technique that would help physicians by objectively assessing elastic properties of scars, motivated our original algorithm. This algorithm successfully employed active contours for tracking and finite element models for strain analysis. However, the previous approach considered only one normal area and one abnormal area within the region of interest, and scar shapes which were somewhat simplified. Most burn scars have rather complicated shapes and may include multiple regions with different elastic properties. Hence, we need a method capable of adequately addressing these characteristics. The new method can split the region into more than two localities with different material properties, select and quantify abnormal areas, and apply different forces if it is necessary for a better shape description of the scar. The method also demonstrates the application of scale and mesh refinement techniques in this important domain. It is accomplished by increasing the number of Finite Element Method (FEM) areas as well as the number of elements within the area. The method is successfully applied to elastic materials and real burn scar cases. We demonstrate all of the proposed techniques and investigate the behavior of elasticity function in a 3-D space. Recovered properties of elastic materials are compared with those obtained by a conventional mechanics-based approach. Scar ratings achieved with the method are correlated against the judgments of physicians.

We propose new algorithms for accurate nonrigid motion tracking. Given an initial model representing general knowledge of the object, a set of sparse correspondences, and incomplete or missing information about geometry or material properties, we can recover dense motion vectors using finite element models. The method is based on the iterative analysis of the differences between the actual and predicted behaviors. Unknown parameters are recovered using an iterative descent search for the best nonlinear finite element model that approximates nonrigid motion of the given object. During this search process, we not only estimate material properties, but also infer dense point correspondences from our initial set of sparse correspondences. Thus, during tracking, the model is refined which, in turn, improves tracking quality. Experimental results demonstrate the success of the proposed algorithm. Our work demonstrates the possibility of accurate quantitative analysis of nonrigid motion in range image sequences with objects consisting of multiple materials and 3D volumes.

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 details of their geometry, and to a hand motion analysis application.

We propose a new method for accurate nonrigid motion analysis when point correspondence data is not available. We construct nonlinear finite element models by integrating range data and prior knowledge about an object's properties. We attempt to recover the motion sequence given an initial alignment of the model with the first frame of the sequence. The main idea of the method is to find the forces that are responsible for the motion or shape deformation of the given object. The task is broken into subtasks of finding the forces for each frame. Both absolute values and directions of these forces are taken into consideration and iteratively varied not only for each frame, but also between the frames. Our work demonstrates the possibility of accurate nonrigid motion analysis and force recovery from range image sequences containing nonrigid objects and large motion without interframe point correspondences.

In this paper a method for the objective assessment of burn scars is proposed. The quantitative measures developed in this research provide an objective way to calculate elastic properties of burn scars relative to the surrounding areas. The approach combines range data and the mechanics and motion dynamics of human tissues. Active contours are employed to locate regions of interest and to find displacements of feature points using automatically established correspondences. Changes in strain distribution over time are evaluated. Given images at two time instances and their corresponding features, the finite element method is used to synthesize strain distributions of the underlying tissues. This results in a physically based framework for motion and strain analysis. Relative elasticity of the burn scar is then recovered using iterative descent search for the best nonlinear finite element model that approximates stretching behavior of the region containing the burn scar. The results from the skin elasticity experiments illustrate the ability to objectively detect differences in elasticity between normal and abnormal tissue. These estimated differences in elasticity are correlated against the subjective judgments of physicians that are presently the practice.