A novel approach to image alignment or registration for three-dimensional reconstruction of medical images is introduced. The scheme is based on recent advances in computer vision. A point-to-point disparity analysis is used in the alignment algorithm. It is assumed that a sequence of images to be aligned is related by a linear affine transformation. Based on this model, a disparity function is defined by considering the image shape structure or the image intensity changes. The function parameters are estimated simultaneously from the computation of shape or intensity disparity. The decomposition of the disparity function parameters reveals the object rotation, deformation, scaling, and translation individually in a 3D space. These parameters provide enough information for image alignment as well as image matching. Experiments on 3D reconstruction of autoradiographic images demonstrate the effectiveness of the disparity analysis method.