Research On The Analysis Method Of Multimodality Medical Image Registration & Integration

Medical image registration has been a heated research area in recent years, the main purpose of this paper is to study the maximization of mutual information based on multi-modal medical image registration, mainly brain CT-MRI image registration. The standard text used in the experimental data from the open source segmentation of medical image registration database ITK, using real patient data from the CT room of the Second Hospital of Jilin University, provided the diseased CT, MRI images, in addition to some experimental data from the RIRE project.This paper introduces background and significance of the research, the concept of image registration and the current research status. Besides, it gives a general framework for medical image registration. The framework mainly consists of four modules: geometric transformation model, image interpolation, similarity measure, optimized function. Also, highlight the basic theory of mutual information, as well as Parzen window and Historgram estimates which used to estimate the mutual information.Then introduced the mutual information calculation process, given the mutual information calculation process icon and pseudo-code, and use one of the simplest examples to illustrate the calculation of mutual information. Because in the registration process, the calculation of mutual information is the most time-consuming part, in order to avoid the image detail of tissues and organs, we use ITK provides standard test images. Besides, carry out experiments to two main parameters for the pixel number of samples and the number of histogram bins. First select a fixed image pixel sample 50% of the total pixels, and change the number of histogram bins, experimental results show that the performance of bins which obtained 32 or 50 are perform much better.Then fixed bins of 32 and 50, selected from a fixed image pixel sample 5% of the total pixels to 100%, the results show that when the sample of more than 20% of the total pixels are doing very well, that is, there is no need to use too many samples.Then we did experiments based on real patient's images, using the parameter value based on previous experimental results. The geometric transformation model is affine transformation; interpolation strategy is a simple 3-order B-spline interpolation. Given values of mutual information and X-axis changes with the number of iterations curve, experimental results also verify the previous parameters which obtained using standard image is correct.We use the redundant Contourlet transform the multi-scale, orientation, anisotropy and the advantages of translation invariance and the fusion rules which based on regional energy integration in the selection coefficient of superiority, a transformation based on redundancy and regional energy integration Contourlet Rules Image fusion algorithms and particle swarm optimization algorithm for the introduction of thresholds for the global optimal solution.The experimental results show that: this algorithm is a well-established image fusion algorithm.