STATISTICAL SEPARABILITY OF LOCAL MOTIONS IN VOLUMETRIC IMAGES

Measurement of local differences in the 3D motions of dynamic body organs (captured by volumetric scanners) is of increasing interest in biomedical imaging applications. Estimation methods of 3D optical flow in those images have been studied in recent years. However, theoretical limits of 3D optical flow-based motion estimation and segmentation are yet to be analysed. In this paper, a novel criterion is proposed to statistically predict the separability of local 3D motions. Simulation results demonstrate how the proposed approach works in principle to predict separability of two motions in terms of the amount of relative motion and the scale of noise.