Research On Medical Image Registration Algorithm

Image registration has been the most important and rapid developed technique in the field of image processing in recent years. Its application covers the areas of medicine, military affairs, remote sensing, computer vision, etc. However, there are still some difficult issues need to be studied further, especially for medical image registration, which are the main topics of this paper.Firstly, in multi-modality rigid registration algorithm based on mutual information(MI), interpolation operations result in non-smooth objective function, which makes optimization process get stuck into local extremes easily, thus wrong registration parameters are obtained. Aiming at this problem, a new registration algorithm is presented in this paper. The main idea of the new algorithm is to combine the genetic algorithm with multi-resolution strategy and the parameters of genetic algorithm are adapted along with the resolution level of images. The new algorithm can efficiently avoid registration process to get stuck into local extremes and obtain sub-voxel registration accuracy.Secondly, to solve the problem that MI-based rigid registration method gets stuck into local extremes easily, a new registration method based on mixed mutual information is proposed in this paper. This method uses different mutual information as similarity metric utilizing their characteristics of Renyi's entropy and Shannon's one. Moreover, the optimization method combining PSO with Powell is used to find the optimum. The proposed registration method can obtain more exact registration results than traditional mutual information based on Shannon's entropy.Thirdly, for elastic registration problem based on landmark points, to obtain the corresponding landmark points presenting local geometric deformation of image precisely, a method is proposed in this paper. The method extracts landmark points through computing the regional similarity between two images. Furthermore, multilevel B-splines interpolation is applied to these landmark points to balance between the smoothness and accuracy of registration transformation.Fourthly, based on the successful application of maximization of mutual information in rigid multi-modality image registration, an improved"Demons"algorithm for elastic multi-modality images is proposed in this paper. The method adds additional external force defined as the gradient of mutual information between two images with respect to the deformation fields to drive the floating image to deform. In this way, the misregistration problem resulted by the original algorithm when transformation direction can not be determined due to the lack of intensity gradient information can be overcome.Fifthly, for intensity-based elastic registration problem, Gaussian smoothing is used to constrain the transformation to be smooth and thus preserve the topology of image. Aiming at the insufficiency of the uniform Gaussian filtering of the deformation fields, an automatic and accurate elastic image registration method based on B-splines approximation is proposed in this paper. In this approach, the regularization strategy is adopted by using multi-level B-splines approximation to regularize the displacement fields in a coarse-to-fine manner. Moreover, it assigns the different weights to the estimated displacements in terms of their reliability. In this way, the level of regularity can be adapted locally, so the estimated transformation is restricted to deformation satisfying the real-world property of matter.