Premium
Content‐oriented sparse representation ( COSR ) for CT denoising with preservation of texture and edge
Author(s) -
Xie Huiqiao,
Niu Tianye,
Tang Shaojie,
Yang Xiaofeng,
Kadom Nadja,
Tang Xiangyang
Publication year - 2018
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13189
Subject(s) - noise reduction , imaging phantom , sparse approximation , artificial intelligence , noise (video) , computer science , pattern recognition (psychology) , image quality , similarity (geometry) , segmentation , computer vision , representation (politics) , signal to noise ratio (imaging) , enhanced data rates for gsm evolution , mathematics , image (mathematics) , nuclear medicine , medicine , telecommunications , politics , political science , law
Denoising has been a challenging research subject in medical imaging, since the suppression of noise conflicts with the preservation of texture and edges. To address this challenge, we develop a content-oriented sparse representation (COSR) method for denoising in computed tomography (CT).