Edge-detecting operator-based selection of Huber regularization threshold for low-dose computed tomography imaging.
- Author:
Shanli ZHANG
1
;
Hua ZHANG
;
Debin HU
;
Dong ZENG
;
Zhaoying BIAN
;
Lijun LU
;
Jianhua MA
;
Jing HUANG
Author Information
- Publication Type:Journal Article
- MeSH: Artifacts; Humans; Image Processing, Computer-Assisted; Tomography, X-Ray Computed
- From: Journal of Southern Medical University 2015;35(3):375-379
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVETo compare two methods for threshold selection in Huber regularization for low-dose computed tomography imaging.
METHODSHuber regularization-based iterative reconstruction (IR) approach was adopted for low-dose CT image reconstruction and the threshold of Huber regularization was selected based on global versus local edge-detecting operators.
RESULTSThe experimental results on the simulation data demonstrated that both of the two threshold selection methods in Huber regularization could yield remarkable gains in terms of noise suppression and artifact removal.
CONCLUSIONBoth of the two methods for threshold selection in Huber regularization can yield high-quality images in low-dose CT image iterative reconstruction.
