title: Adaptive template filtering method for MRI.
abstract: Denoising is an important step for image processing. One of the most important characteristics of MRI (MRI) is the complicated changes of gray level. For MRI, preservation of useful information is more important than simple improvement of Signal-Noise Ratio (SNR). Traditional filtering algorithms are not fit for MRI. Adaptive Template Filtering Method (ATFM) can dynamically match the best template from the predetermined multi templates based on local texture characteristics for each pixel. In this paper, detail algorithm and analysis are given. Compared with other filtering methods, the performance of ATFM is better than that of other filtering methods as our experiment demonstrates. It can both effectively suppresses noise and best preserve useful information at the same time for MRI. Thus, ATFM can meet the need of clinical diagnosis and image processing.
authors.affiliation: Key Laboratory of Electromagnetic Field & Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China. guoshengrui@163.com
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.forename: Xuena
authors.initials: X
authors.lastname: Liu
authors.forename: Youxi
authors.initials: Y
authors.lastname: Wu
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: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique