Research On Algorithms Of Medical Image Registration

As a key technology of medical image processing and analysis, image registration is very important in clinical applications. It not only can be applied in the diagnosis of disease, but also can help in clinical operations by tracking the focus part and in accessing the treatment. Nowadays, medical image registration is widely applied in image fusion, three-dimensional image reconstruction and surgical navigation to locate images.This dissertation mainly focuses on the registration of medical images, especially the rigid images, series rigid images and non-rigid images. As far as the processing of very large volume data in series and nonrigid image registration applications is concerned, the global optimization method and multi-resolution analysis based registration scheme are also discussesed. The main contents of the dissertation are as follows:Applying the nonlinear correlation metrics and downhill simplex searching technique, the dissertation proposes the rigid image registration method based on the maximization of Nonlinear Correlation Coefficient (NCC). As an improved version of Mutual Information (MI), NCC can quantitatively describe the nonlinear correlation degree between two variables using a value in the closed interval [0, 1]. Therefore, it is more intuitionistic and suitable to compare and analyze the correlation degree among images. The extremum of NCC can help us to adopt the upper and lower thresholds of the cost function to revise the downhill searching process for the optimal, and overcome the local minimal problem induced by the nonlinearity of NCC. The introduction of variant accuracy tolerance will reduce the iterations at the local minima in the searching process, and then, enhance the speed of the algorithm. Simulations verify that the proposed method can be applied in multi-modal image registration and its performance will not be affected by the contrast differences between the floating and reference images.According to the different characteristics of the inner geometry connection of series image, the dissertation proposes two types of metrics to register the series rigid image. For series image with known inner geometry connection, its registration can be carried out by selecting the first image in the series as the floating image, which will be registered to the reference image. The second image in the series is registered to the reference image and the registered first image simultaneously. The rest of the series image may be deduced by analogy. A new correlation metric, Shared Chain Mutual Information (SCMI), is proposed for this registration model to ensure the series image to be accurately registered. SCMI can quantitatively describe the correlation degree between the nth image with the pre-registered n-1 images. Experiments have shown that SCMI can be used to register series rigid image with known inner geometry connections, and can achieve sub-pixel registration accuracy. Comparisons with the accuracy of two image registrations further prove the advantages of SCMI on registering the series image. For registration of unknown-inner geometry connections, Nonlinear Correlation Information Entropy (NCIE) is proposed as the registration metric, which can use a value in the closed interval [0, 1] to estimate the general relationship among multi-variables, and may not be affected by the order of images. Simulations on the images with rotation and translate transformations have been conducted to verify the performance of NCIE as a registration metric, and the results prove the effectiveness of NCIE to series rigid image with unknown-inner geometry connections.Under the nonrigid image registration frame, the dissertation proposes to use Wendland compactly support radial basis function as a parameterized transformation model. As the parameters are numerous in non-rigid registration model, the particle swarm optimization algorithm is selected to achieve exploration and exploitation in the whole feasible space accurately and fleetly for the unknown parameters of nonrigid transformation. Particle swarm optimization algorithm has the characteristic of fast global searching for the optimal, but it is restricted in applications by the inclination of its particles falling into premature convergence. The dissertation improves the basic particle swarm optimization algorithm and proposes the revised Variable-Neighborhood-Selection based Particle Swarm Optimization (VNS-PSO) algorithm. The algorithm updates the premature particles by re-assigning a better reference particle in its neighborhood. This will lead the algorithm out of the premature state and continue its global optimization searching. Experiments show that, the registration method combining the revised VNS-PSO algorithm and Wendland compactly support radial basis function can effectively register the images with global or local nonrigid transformations.To meet the speed and quality requirements for series and nonrigid image registration, the dissertation proposes an Integer Lifting Wavelet Transform (ILWT) based multi-resolution analysis registration model. This model decomposes the images firstly, registers the approximate images to obtain the transformation model secondly, and finally, reconstructs the registered image by applying the transformation parameters to the original resolution image. Comparing to the first generation wavelet transformation, ILWT can implement transformation from integer to integer, and lossless reconstruction of image. Therefore it is more suitable for multi-resolution analysis than the first generation wavelet transformation. In the experiment, orthogonal wavelet and biorthogonal wavelet are used to decompose ultrasonic image multi-dimensionally. Results show that ILWT based multi-resolution analysis can keep more original information in the processed images. Moreover, the registration results of the ultrasonic images decomposed at different resolution level by biorthogonal wavelet verify that ILWT can effectively reduce the iteration and the calculation time of the algorithm.Using the proposed rigid and non-rigid image registration methods and multi-resolution analysis strategy, the kidney ultrasonic series image is registered. By analyzing the time-intensity curves of the registered series image, we can achieve the correct conclusion about whether the focus is benign or malign. This application further validates that the researches on medical image registration methods in this dissertation are applicable in clinical diagnosis.