Optical flow estimation in cardiac CT images using the steered Hermite transform

This paper describes a new method to estimate the heart's motion in computer tomography images with the inclusion of a bio-inspired image representation model. Our proposal is based on the polynomial decomposition of each of the images using the steered Hermite transform as a representation of the local characteristics of images from an perceptual approach within a multiresolution scheme. The Hermite transform is a model that incorporates some of the more important properties of the first stages of the human visual system, such as the overlapping Gaussian receptive fields, the Gaussian derivative model of early vision and the multiresolution analysis. We propose an approach for optical flow estimation that incorporates image structure information extracted from the steered Hermite coefficients, that is later used as local motion constraints in a differential estimation method that involves several of the constraints seen in the current differential methods, which allows obtaining accurate flows. Considering the importance of understanding the movement of certain structures such as left ventricular and myocardial wall for better medical diagnosis, our main goal is to find an estimation method useful to assist diagnosis tasks in computer tomography images.