Joint sparsity and fidelity regularization for segmentation-driven CT image preprocessing
Joint sparsity and fidelity regularization for segmentation-driven CT image preprocessing
复制标题
分割驱动的 CT 图像预处理的联合稀疏性和保真度正则化
DOI:
10.1007/s11432-015-5375-x
复制
发表时间:
2016-01
影响因子:
8.8
通讯作者:
Li Huibin
中科院分区:
文献类型:
--
作者:
Liu Feng;Li Huibin
In this paper, we propose a novel segmentation-driven computed tomography (CT) image preprocessing approach. The proposed approach, namely, joint sparsity and fidelity regularization (JSFR) model can be regarded as a generalized total variation (TV) denoising model or a generalized sparse representation denoising model by adding an additional gradient fidelity regularizer and a stronger gradient sparsity regularizer. Thus, JSFR model consists of three terms: intensity fidelity term, gradient fidelity term, and gradient sparsity term. The interactions and counterbalance of these terms make JSFR model has the ability to reduce intensity inhomogeneities and improve edge ambiguities of a given image. Experimental results carried out on the real dental cone-beam CT data demonstrate the effectiveness and usefulness of JSFR model for CT image intensity homogenization, edge enhancement, as well as tissue segmentation.
登录
查看更多内容
DOI:
10.1109/cit.2007.143
发表时间:
2007-10
期刊:
7th IEEE International Conference on Computer and Information Technology (CIT 2007)
影响因子:
--
作者:
HyeSuk Kim;H. Yoon;K. Trung;Gueesang Lee
通讯作者:
HyeSuk Kim;H. Yoon;K. Trung;Gueesang Lee
影响因子:
4
作者:
RUDIN, LI;OSHER, S;FATEMI, E
通讯作者:
FATEMI, E
DOI:
10.14738/jbemi.14.310
发表时间:
2014-08
期刊:
Journal of Biomedical Engineering and Medical Imaging
影响因子:
--
作者:
N. Hai
通讯作者:
N. Hai
DOI:
10.1016/b978-0-12-373904-9.x0001-4
发表时间:
2009
期刊:
--
影响因子:
--
作者:
I. Bankman
通讯作者:
I. Bankman
影响因子:
2.5
作者:
DONOHO, DL
通讯作者:
DONOHO, DL