title: Supervised restoration of degraded medical images using multiple-point geostatistics.
abstract: Reducing noise in medical images has been an important issue of research and development for medical diagnosis, patient treatment, and validation of biomedical hypotheses. Noise inherently exists in medical and biological images due to the acquisition and transmission in any imaging devices. Being different from image enhancement, the purpose of image restoration is the process of removing noise from a degraded image in order to recover as much as possible its original version. This paper presents a statistically supervised approach for medical image restoration using the concept of multiple-point geostatistics. Experimental results have shown the effectiveness of the proposed technique which has potential as a new methodology for medical and biological image processing.
authors.affiliation: School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia. t.pham@adfa.edu.au
authors.forename: Tuan D
authors.initials: TD
authors.lastname: Pham
mesh_terms: D000465:Algorithms; D003952:Diagnostic Imaging; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging