In this paper, we propose a novel two-level regression method for computing body mass distribution from a database of X-ray images, without scanning the new subject. Our approach first selects a suitable sample from the image database by minimizing a distance function based on the relationships between the new subject's body measurements and those of sample subjects. The X-ray image of the new subject is then predicted from the sample image using a feature-based transformation. Body mass distribution is computed directly from the predicted X-ray image. Our results surpass the accuracy of commonly used mass distribution regression methods in biomechanics literatures. In addition, by not scanning the new subject, we avoid all the radiation and cost involved in X-ray absorptiometry.
['Sang N. Le', 'Mei Kay Lee', 'Anthony C. Fang']
Two-level regression of body mass distribution from X-ray image database