with respect to the lights and by spatial variation in light intensity to ensure accurate optical flow estimation we have adapted the method of sun et al d sun s roth and m black u201c secrets of optical flow estimation and their principles u201d in computer vision and pattern recognition cvpr 2010 pp 2432 2439 to correct for variations in lighting and to enforce smooth and continuous optical flow a limitation of optical flow techniques is their sensitivity to lighting variation d sun s roth and m black u201c secrets of optical flow estimation and their principles u201d in computer vision and pattern recognition cvpr 2010 pp 2432 2439 without prior knowledge of the scene methods such as the rudin osher fatemi method l i rudin s osher and e fatemi u201c nonlinear total variation based noise removal algorithms u201d physica d nonlinear phenomena vol 60 no 1 pp 259 268 1992 are often applied to reduce the effects of lighting variation and image noise the lighting variation is spatially smooth on the breast surface so lighting levels across the breast are estimated by convolving the image with a large gaussian kernel we then correct for lighting variation by scaling each pixel so that the lighting across the breast is uniform an overview of this process is given in fig5 once the images have been corrected the next phase is to analyze the images given two images i 1 i 2 with grey or color values i 1 x and i 2 x at a pixel location x optical flow algorithms seek to estimate a 2d vector field u with value u x at each pixel with the properties that.