Feature Extraction from 2D Images for Body Composition Analysis

Body volume and body shape have been used in the estimation of body composition in clinical research. However, the determination of body volume typically requires sophisticated and expensive equipment. Similarly, the use of body shape to predict body composition is limited by rater biases as well as reproducibility. In this paper, we aim to introduce simple yet relatively accurate techniques for body volume and body shape representation that reduce limitations of traditional approaches. We propose an automated method to construct a 3D model of the body by accumulating ellipse-like slices formed by using the length and width features sampled from the back and side profile images. Body volume is represented in pixels by adding up the areas of the slices. Apart from representing body volume in pixels, we also aim to extract shape features from the 2D images and to create clusters of individuals according to their body shape. The body volume representation and the proposed shape features together with other meta-information including age, sex, race, height, and weight, could be effectively used in body composition prediction. Our study results indicate that the body volume calculated by the proposed method is reasonably accurate and the extracted shape clusters provide important information when estimating body composition.