بهبود سرعت "انطباق مبتنی بر روش برش گراف" جهت انطباق غیر صلب تصاویر تشدید مغناطیسی مغز

Image processing methods, which can visualize objects inside the human body, are of special interests. In clinical diagnosis using medical images, integration of useful data from separate images is often desired. The images have to be geometrically aligned for better observation. The procedure of mapping points from the reference image to corresponding points in the floating image is called Image Registration. It is a spatial transform. These images might be different because they were taken at different times or applied by using different devices. By the nature of this image transformation, image registration can be classified into rigid registration and non-rigid registration. The freedom’s degree in a rigid transformation is relatively low and the methods of rigid image registration are becoming mature. In contrast, non-rigid image registration is still a challenging problem because of its high degree of freedom. One of the non-rigid image registration methods is turning the registration problem into an optimization problem and obtaining the optimal value as the result of registration. An example of these methods is the graph-cuts based registration. The basic technique is to construct a specialized graph for the energy function to be minimized in a way that the minimum cut on this graph also minimizes the energy. Given that our focus in this research, is on the medical image registration, and time is one of the critical factors in medical applications. It seems that improvement of this method in terms of run time will be helpful for its clinical and medical applications. In order to achieve this goal, in this research, with modifying the energy function, we proposed a method that significantly reduces the run time of registration process. The implementation results of our proposed method on the images with artificial deformations which are similar to the most pessimistic possible deformation modes in real image data, show that the proposed algorithm is about three times faster than the existing algorithm, while the average amount of SAD criterion will be increased from 0.7 to 1.