title: Threshold optimization of adaptive template filtering for MRI based on intelligent optimization algorithm.
abstract: Intelligent Optimization Algorithm (IOA) mainly includes Immune Algorithm (IA) and Genetic Algorithm (GA). One of the most important characteristics of MRI is the complicated changes of gray level. Traditional filtering algorithms are not fit for MRI. Adaptive Template Filtering Method (ATFM) is an appropriate denoising method for MRI. However, selecting threshold for ATFM is a complicated problem which directly affects the denoising result. Threshold selection has been based on experience. Thus, it was lack of solid theoretical foundation. In this paper, 2 kinds of IOA are proposed for threshold optimization respectively. As our experiment demonstrates, they can effectively solve the problem of threshold selection and perfect ATFM. Through algorithm analysis, the performance of IA surpasses the performance of GA. As a new kind of IOA, IA exhibits its great potential in image processing.
authors.affiliation: Joint Key Lab. of Electromagn. Field & Electr. Apparatus Reliability, Hebei Univ. of Technol., Tianjin, China. guoshengrui@163.com
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.forename: Youxi
authors.initials: Y
authors.lastname: Wu
authors.forename: Xuena
authors.initials: X
authors.lastname: Liu
authors.forename: Ying
authors.initials: Y
authors.lastname: Li
authors.forename: Guizhi
authors.initials: G
authors.lastname: Xu
authors.forename: Weili
authors.initials: W
authors.lastname: Yan
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007107:Immune System / Q000502:physiology*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D008954:Models, Biological; D008957:Models, Genetic; D018448:Models, Immunological; D008959:Models, Neurological; D015233:Models, Statistical; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated