Accelerated Multimodal Medical Image Registration Based on a Closed Incompressible Viscous Fluid Model

• Main Authors: Ching-Yu Chang, 張境畬
• Other Authors: 張恆華
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/3wh72u

碩士 === 國立臺灣大學 === 工程科學及海洋工程學研究所 === 105 === Image registration is an important technique for medical research and medical diagnosis. It is a process of looking for a spatial transformation between two images and mapping one to the other one based on the transformation function. There are many image registration methods and one algorithm is based on a non-rigid fluid flow model. However, the computation of the governing equation and Gaussian smoothing of this method is quite time-consuming and it is unable to perform multimodal registration. To address these problems, we adopt the Jacobi method iteratively to solve the implicit viscosity terms and parallelize the program with GPU. Compute Unified Device Architecture (CUDA), an application programming interface for GPU by NVIDIA, is used to accelerate the algorithm. Besides, we modify the body force term via the mutual information to achieve multimodal image registration. A variety of different types of magnetic resonance images were used to evaluate this new method. Experimental results indicated that the proposed method efficiently registered many kinds of images, including skull-stripping images, noisy images, large scale deformation images and multimodal images. Comparing to the previous fluid-flow model, our method approximately reduced the processing time by half and successfully achieved multimodal image registration.