Non-rigid registrations based on image characteristics and optical flows

As the non-rigid image registration methods can not meet the requirements of registration accuracy and registration time simultaneously, three kinds of improved non-rigid registration methods are proposed based on image characteristics and image gray. These non-rigid registration methods were based on the Circle Descripto increases Feature (CDF), Dynamic Driving Force Demons (DDFD) and image characteristics and optical flow, respectively. In CDF method, feature points were extracted from the images, and the circle descriptor is used in the method instead of square descriptor in classical methods, by which the rotation invariance was maintained and the speed of the registration was increased. In DDFD method, the driving force was changed by introducing the driving force coefficient, so that the registration time and registration accuracy were improved effectively. In registration methods based on image characteristics and optical flow, the feature points were extracted from a float image and a reference image by using registration method based on image characteristics, and these extracted feature points were used to get a coarse registered image (feature level registration); then the optical-flow method was used to register accurately (pixel level registration) for the coarse registered image and to achieves the purpose of taking account of the registration accuracy and registration time. The experiments on checkboard images, natural images, brain MR images and liver CT images were performed and the results show that the proposed methods are better than the classical methods such as Scale-invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Demons, Active Demons and Total Variation Regularization/L¹ norm (TV-L¹) in registration time, registration accuracy and adaptability for large-deformation images.