The Research Of Medical Image Registration And Image Mosaic Based On Mutual Information

With the rapid development of medical imaging technology, a variety of new imaging equipment have arisen. such as Computed tomography (CT), magnetic resonance imaging (MRI),digital subtraction angiography (DSA), single photon emission computed tomography (SPECT),positron emission tomography (PET) and so on. These imaging techniques can obtain the body of information in some areas, but each imaging technique can not provide information about all aspects of the human body, because of differences in imaging principle. Different imaging techniques can provide different aspects information of the patient. People want to obtain the utilization information of multiple imaging or multi-mode imaging, to more comprehensive and accurate of clinical treatment. Researchers began looking for the technology, that the image can shows functional information and anatomical structure of the patient. The demand of image registration is anatomical structure accord with each other in spatial location,The goal of image registration is to obtain a transformation, so that the points in one image can be related to their corresponding points in the other. Registration process is essentially a multi-parameter optimization problem. It is the basis of image analysis and the prerequisite for image fusion. Medical image registration has a wide range of applications in surgical navigation, radiation therapy, assessment of therapeutic effects. Therefore, the research of medical image registration has important academic significance and application value.Collignon and Maes, Viola and Wells propose the registration method using maximum mutual information, it does not require any pre-processed image and limit the relationship between the gray, the method can be used image registration in almost any different modal, so it has been widely applied in multi-modal medical image registration. Image registration include feature extraction, space transform, spatial search and similarity measure. The feature space is the gray information when image registration based on mutual information.Rigid image registration may introduce new intensity values, leading to unpredictable changes in joint histogram. In the calculation of mutual information, people often use partial volume interpolation to update the joint histogram for each pixel pair to avoid the introduction of the new gray value. However, this method may cause some local extreme values when the image is translated by integer point, leading to many errors in image registration. This paper propose to use the new algorithm which calculate the joint histogram with the Gaussian function. The smoothness of the Gaussian function can avoid statistical errors of the image joint histogram. The best optimization parameters are obtained using the Powell optimization method. Experimental results which are complete using CT-PET experimental data, indicate the proposed algorithm effectively eliminate the local extremum and improve the accuracy of medical image registration. Moreover, this algorithm is also applicable to noise image registration. The method meets multimodal image registration, overcome the lack of traditional method, improve the accuracy of results. However, a lot of medical image registration algorithms only concentrate on rigid transformation; Elastic registration algorithm has been made a number of methods, but rigid registration is more sophisticated. In addition, the elastic registration algorithm can not meet the real-time clinical needs, In addition, because of diversity and complexity, even though a lot of elastic registration algorithm has been proposed, no registration method can be reached at all aspects of clinical needs. This means that the method has certain limitations, including lack of effective real-time restrict and less than automatic nature, those disadvantages extent the restrict of medical image registration algorithm in the actual clinical application, therefore elastic registration of medical image have a wide range of clinical applications, and is also a hot spots in the field of medical processing research.In this paper, the medical image registration is effectively implemented using the model of free-form deformation(FFD) based on B-spline and maximum mutual information. This paper choose the three B-spline as the variable model. Three B-spline deformation model has characteristics of a good local control, the neighborhood was caused when each control points changes; Control surfaces grid spacing can be used to control the degree of deformation, when the control surfaces have more dense grid, the deformation models tend to describe the local deformation; Control surface have reduced grid, the deformation of tend to describe the local deformation. So in the paper, only calculating the mutual information of the neighborhood around the two images corresponding control points, to reduce computational purposes.Image mosaic has a wide range of applications in the medical field. With the development of medical technology, clinical medical propose some new requirements to x-ray images; People obtain a complete anatomical images, the measurement of anatomical diameter wire and angle in Long bones such as spine and orthopedic diagnostic. It has important value in preoperative diagnosis, intraoperative monitoring and postoperative evaluation. Therefore, the image mosaic has important clinical significance.Image mosaic include search overlapping sub-regional, image registration, gray mapping, image fusion and other parts. In the paper, the error of experimental data is translation, so the area-based gray methods is used. The method search the target image by the gray correlation, to find the matching template and obtain the panoramic picture. Image mosaic may appear a simple belt phenomenon, because of the large gray difference between adjacent images. The paper proposed gray mapping method, that statistical joint histogram of the overlapping area, calculate new gray mapping values. The gray difference of panoramic image was eliminated by mapping an image to another image. Experimental results which are complete using CR experimental data, indicate the proposed algorithm improve the matching accuracy of image mosaic by the maximum mutual information.