title: Medical image registration using fuzzy theory.
abstract: Mutual information (MI)-based registration, which uses MI as the similarity measure, is a representative method in medical image registration. It has an excellent robustness and accuracy, but with the disadvantages of a large amount of calculation and a long processing time. In this paper, by computing the medical image moments, the centroid is acquired. By applying fuzzy c-means clustering, the coordinates of the medical image are divided into two clusters to fit a straight line, and the rotation angles of the reference and floating images are computed, respectively. Thereby, the initial values for registering the images are determined. When searching the optimal geometric transformation parameters, we put forward the two new concepts of fuzzy distance and fuzzy signal-to-noise ratio (FSNR), and we select FSNR as the similarity measure between the reference and floating images. In the experiments, the Simplex method is chosen as multi-parameter optimisation. The experimental results show that this proposed method has a simple implementation, a low computational cost, a fast registration and good registration accuracy. Moreover, it can effectively avoid trapping into the local optima. It is adapted to both mono-modality and multi-modality image registrations.
authors: College of Computer Science and Technology, Hunan University of Arts and Science, Changde, Hunan Province, 415000, PR China. pmsjjj@126.com
authors: Meisen
authors: M
authors: Pan
authors: Jingtian
authors: J
authors: Tang
authors: Qi
authors: Q
authors: Xiong
mesh_terms: D001921:Brain / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D016208:Databases, Factual; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007257:Information Theory; D008279:Magnetic Resonance Imaging; D010363:Pattern Recognition, Automated; D049268:Positron-Emission Tomography; D059629:Signal-To-Noise Ratio; D014057:Tomography, X-Ray Computed