【作者】 鲍军肖；

【导师】 景军；

【作者基本信息】 燕山大学 ， 生物医学工程， 2010， 硕士

【摘要】 医学图像配准是医学图像处理领域的研究热点问题之一,是图像信息融合的前提和基础,医学图像的自动配准技术和理论研究对基础医学研究、临床诊断、治疗、和疗效评价等具有重要意义。本文针对一组医学图像数据对实现自动配准的关键技术及算法进行了理论和实验研究。主要工作有:针对目前医学图像的成像特点,对医学图像预处理方法的研究。对目前流行的灰度变换、直方图处理、滤波和色彩增强方法进行了归纳整理和实验研究,为后续的图像配准研究奠定基础。对基于harris特征点的医学图像配准和基于互信息的医学图像配准进行重点研究。基于特征点的医学图像配准方法主要从特征点提取算子入手,从准确性和稳定性等方面进行比较研究,通过理论和实验研究对算法进行改进。文中特征点匹配以特征点邻域灰度相关性作为相似性测度,搜索空间用仿射变换空间,并用马氏距离的仿射变换不变性校正特征点的匹配,最后再对参数优化。基于互信息的配准方法具有不需要做特征提取等预处理,需要的人机交互少,易于实现自动配准的优点。本文在对相关概念和基本原理研究的基础上,重点对插值和优化方法进行理论和实验研究,包括对双线性插值和PV(partial volume)插值以及Powell优化算法和PSO(Particle Swarm Optimization)算法进行理论和实验对比研究。最后,是对配准结果的评估。在对评估方法和评价指标分析整理基础上,从精度,速率,抗噪性等方面对前面的两类配准方法进行了性能比较分析。结果表明:文中两种方法都具有配准精度高、速度快、鲁棒性强等特点,但是配准效果因应用对象的不同而不同。更多还原

【Abstract】 Medical image registration is a hot topic in image processing, and is the basic issue of medieal image fusion.The research of automatic regisrraction technical and theoretical of medical images is important in the basic medical research, clinical diagnosis, treatment, and efficacy evaluation.In this paper, theoretical and experimental research of key technology and algorithm theory of automatic registration has been made which based on a set of medical image data.Main tasks are as follows:For the characteristics of current medical images, this thesis study the medical image preprocessing in detail.We summarize the methods of image preprocessing which were most commonly used, and verified the effect with medical images.It lay a solid foundation for the image registration.This study focused on medical image registration based on harris feature points, and medical image registration which based on mutual information.The method based on feature points had been study begin with the operator which commonly used in feature point extraction, and then comparative them from accuracy and stability.Then we made some improvements on algorithms through theoretical and experimental research.We selection the gray of neighborhood between feature point as the measure of correlation similarity, and using the affine transformation as the search space.The affine transformation invariance of Mahalanobis distance is used to calibrate the matching of feature point. Finally, it is the parameter refinement.The method which based on mutual information advantages in no need to feature extraction, requires less man–machine interaction, and is easy to realize automatic registration. In the beginning, we mainly study the relative concepts and principles. On this basis, we discuss the interpolation and optimization in detail. Bilinear interpolation and Parital volume interpolation, and Powell optimization and PSO algorithm has been madeed a detailed comparison. In the last, it is the evaluation of the results. Through the analysis of assessment menthods and indicators of evaluation, we analyse the previous two methods from the precision, speed, and noise immunity. The results show that: both methods have characteristics of high registration accuracy, speed and robustness. However, the results will be varying in the different application object.更多还原