The objective of this chapter includes analysis of soft tissue motion descriptors not easily recoverable from visual observations. These descriptors include strain and initially unknown (or hard to observe) local material properties. Such object-related issues are very important because their correctness affects not only the quality of tracking and motion analysis, but also better understanding of the object. This understanding leads to improvement of the model and, therefore, contributes to comprehension of the motion or deformation process. This is a very significant outcome for medical imaging applications since it provides the insight often required by physicians.
In this chapter we describe methods for human tissue motion analysis from range image sequences using the nonlinear Finite Element Method (FEM). The approach combines range data, mechanics of human tissues, and dynamics of their motion using nonlinear finite element models. We are able to evaluate the changes in strain distribution over time. Given images at two time instances and their corresponding features, we use FEM to synthesize intermediate images not only of the displacement fields, but also of the strains of the underlying tissues. This results in a physically-based framework for motion and strain analysis. The results from the skin and neck motion experiments illustrate detecting differences in elasticity between normal and abnormal tissue. The results from the hand motion experiments indicate the level of strain that could be causing Repetitive Stress Injury (RSI). The quantitative measures developed in the described burn scar assessment technique provide an objective way to calculate elastic properties of burn scars relative to the surrounding areas. These estimated differences in elasticity are correlated against the subjective judgments of physicians, which is presently the practice. The recovered burn scar properties are incorporated into the model of the hand to investigate the influence of burn scars on the hand motion.
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