【作者】 曹新华；

【导师】 冯焕清； 李传富；

【作者基本信息】 中国科学技术大学 ， 生物医学工程， 2010， 硕士

【摘要】 医学图像配准是当前医学图像研究领域的研究热点和研究难点,在临床诊断和治疗方面具有重要的意义。医学图像配准是指对一幅医学图像寻找一种(或一系列)的空间的变换,使它与另一幅医学图像上的对应点在空间坐标中达到一致。很多学者已经提出了很多医学图像配准算法,它们大体上可分为基于灰度驱动的图像配准算法、基于模型驱动的图像配准算法和综合算法。在医学成像过程中,往往有着被检查部位远远大于图像探测器面积的矛盾,需要将该部位分为几次拍摄,再按照一定的规则拼接起来,这就是医学图像拼接。医学图像拼接在医学领域有着广泛的应用前景。可以解决医学图像获取过程中由于视野的限制而无法得到完整的图像的问题,为诊断提供更好的依据。典型的医学图像拼接算法主要有基于变换域的方法,基于图像灰度的方法和基于特征的方法等。随着医学影像技术、计算机科学技术的不断发展,医学图像的计算机智能化诊断是医学图像处理与分析研究的最重要的目标之一。要实现计算机智能化诊断或计算机辅助诊断,信息的可对比性和完整性是至关重要的,而其中涉及到的技术就是图像的配准技术和图像的拼接技术。医学图像配准和医学图像拼接是医学图像研究的热点和难点。本文针对构建基于高分辨率CT图像的脑部疾病辅助诊断系统的需求,研究了基于特征点驱动的图像配准技术,并对图像拼接技术进行了初步探讨。在研究和分析特征点自动提取方法的基础上,着重研究了尺度不变特征变换(SIFT)算法,找出两幅CT图像的匹配的特征点,实现了特征点的自动匹配,为基于特征点驱动图像配准和图像拼接做好铺垫。进一步对基于模型驱动的配准算法中的基于特征点驱动的图像配准方法进行研究,采用薄板样条插值方法,实现了两幅脑CT图像的正确配准,并提出了采用区域特征向量来去除不正确匹配点的方法。论文还着重研究了基于特征的医学图像拼接方法,给出了对两幅医学图像实现正确拼接的详细算法和实施步骤,并提出了采用统计斜率最多法和随机抽样一致性法去除不正确匹配点的方案,保证了所拼接出来的图像的质量。更多还原

【Abstract】 Medical Image Registration is one of hot research topics in the field of medical image. It has great significance in the clinical diagnosis and treatment. Medical image registration is for a medical image to find a (or a series) space transformation so that the corresponding points in this medical image will reach consistent with another medical image in the same place on both images. Many algorithms of medical image registration have been proposed. They are generally classified based on gray-driven method, modal-driven method and the hybrid algorithms.In the imaging process,the inspected area is often far greater than the image detector, the film must be divided into several parts, and then spliced together in accordance with certain rules, that is, medical image mosaic. Medical image mosaic has wide range of applications in the field of medical image. It can be solved the problem of unable to get the whole image because of the limited vision,and provide a better basis for the diagnosis. According to their methods, the medical image mosaic can be divided into the following categories: transform domain-based approach, the method based on image intensity and feature-based methods.As the medical imaging technology, computer science and technology continues to develop, the computer intelligent medical image diagnosis is one of the most important goals of medical image processing and analysis. To realize intelligent diagnosis or computer aided diagnosis, information comparability and information integrity are essential, of which the technology involved is image registration and image mosaic technology. Medical image registration and medical image mosaic are two hot and difficult research areas in the medical image field.Aim at the requirement of constructing a Computer Aided Diagnosis system for brain diseases based on High Resolution CT images, the research of this thesis is focused on the feature point-driven image registration and image mosaic technology.Based on the deep discussion and analysis for the methods of feature point automatic extraction, this paper put emphasis on the Scale Invariant Feature Transform (SIFT) algorithm. The matched points of two images were found, and paved the way for feature-driven image registration and image mosaic. These matched points were found by SIFT algorithm, achieving automatic matching feature points. For the image registration, we focused the research on the method of feature point-driven image registration. Based on the thin-plate spline interpolation algorithm, we realized the registration of two CT brain images, and also designed a globe feature vector based method to remove the error matched points of images that required for registration. Finally, primary research on medical images mosaic was conducted by using the method of feature point-driven image mosaic, and complete algorithm and programming procedures were proposed for two CT chest images mosaic. In accordance with the characteristics of medical images, the error matched points of images that required for mosaic were removed by random sample consensus (RANSAC) algorithm and statistics slope algorithm.更多还原