| 英文摘要 | Medical Image registration is an important component of Medical Image Processing, and it is playing a key role in current medical treatment and diagnose. The technique of medical image registration has various applications. It can be used to fuse multi-modal image data to offer doctors more comprehensive information about their patients’ diseases. It can also be applied to implement 3D reconstruction by registering a successive series of sections of biological tissue. And furthermore it can facilitate to make plans for clinical treatment and instruct operation, which has now become a routine of some hospitals. On the one hand, with the development of research on medical image registration over these years, this field has been enriched by a plenty of new algorithms and ideas. Some of them are originated from medical image processing, while others are introduced from correlative realms, such as computer vision and pattern recognition. On the other hand, in the wake of the extension of humans’ life span, our demand for medical service is higher than ever before, especially the demand for high-quality medical image processing, including medical image registration. However, traditional medical image registration algorithms are quite time-consuming and hence it becomes hard to meet the demand. To improve the efficiency of image registration, we combine traditional registration methods with the state-of-the-art parallel computing platform, CUDA(Compute Unified Device Architecture), which can accelerate the speed of these algorithms effectively. Our attention is focused on the speed-up of two algorithms, block-matching and a new registration algorithm based on Scale Invariant Feature Transform(SIFT) and point sets registration using Gaussian mixture model algorithm(GMMREG), which are robust registration methods, according to relevant references, but are respectively from two different registration sub-domains, namely intensity-based registration and feature-based registration. Our experiment’s results demonstrate the effectiveness of our methods. In this thesis, we firstly make a brief introduction to medical image registration, including the definition and features of image registration, current main methods addressing on it and its classification, and the challenges of this field. Then, we elaborate our work in this thesis. In this part, we will make clear the process of the two algorithms and our improvement to them. Lastly, we make a review to our w... |
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