Chinese Journal of Computers   Full Text
Title	Medical Image Registration Using Equivalent Meridian Plane and Mutual Information
Authors	LU Zhen-Tai FENG Yan-Qiu FENG Qian-Jin CHEN Wu-Fan
Address	(School of Biomedical Engineering, Southern Medical University, Guangzhou 510515)
Year	2009
Issue	No.8(1611—1617)
Abstract &   Background	Abstract This paper presents a new robust and fast 3-D image registration method based on the equivalent meridian plane (EMP) and mutual information (MI). Comparing with traditional MI based registration methods that estimate the MI using the whole volume intensity information, our approach uses the 2D plane—Equivalent meridian plane’s information. A novel aspect of our approach is using principal component analysis to find the equivalent meridian plane, and then compute its MI. We evaluate the effectiveness of the EMP-MI approach by applying it to the simulated and real brain image data. The experimental results indicate that the algorithm is effective in reducing computation time as well as in helping to avoid local minima. Keywords equivalent meridian plane; mutual information; image registration; principal component analysis; local minima Background This work was supported in part by the National Basic Research Program of China (973 Program) of China under grant No.2003CB716103, the National Natural Science Foundation of China under grant No.30730036 and the Guangdong Provincial Natural Science Foundation of China under grant No.06301304. In these projects, medical images and electrophysiological signals are the main research focuses, which are also key problems in the current field of medical information processing. The research outcomes of the project not only enriches the content of life science and information science, and promote the development of these fields,but also become a kind of new knowledge economy in post-processing software of medical clinical information. Especially, it will provide new intellectual properties for independently development of medical imaging equipments in China. The research group’s interests include image understanding and analysis, image segmentation, registration and medical image computing. The group has proposed some novel algorithms about image registration, such as elastic registration algorithm of medical image based on the prior knowledge and Markov Random Field model, a new registration method based on co-occurrence mutual information, etc. And a fast 3-D medical image registration algorithm based on equivalent meridian plane and mutual information is presented in the paper.