Joint sparsity and fidelity regularization for segmentation-driven CT image preprocessing

Joint sparsity and fidelity regularization for segmentation-driven CT image preprocessing
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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
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发表时间:
2016-01
影响因子:
8.8
通讯作者:
Li Huibin
Li Huibin
中科院分区:
计算机科学2区
文献类型:
--
作者:
Liu Feng;Li Huibin

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在本文中,我们提出了一种新的分割驱动的计算机断层扫描(CT)图像预处理方法。该方法,即联合稀疏和保真度正则化(JSFR)模型,通过增加一个额外的梯度保真度正则化子和一个更强的梯度稀疏正则化子,可以被看作是一个广义的全变差(TV)去噪模型或一个广义的稀疏表示去噪模型.因此,JSFR模型由三项组成:强度保真度项、梯度保真度项和梯度稀疏项。这些项的相互作用和平衡使得JSFR模型具有降低图像强度不均匀性和改善边缘模糊性的能力。在真实的牙科锥束CT数据上进行的实验结果表明,JSFR模型在CT图像灰度均匀化、边缘增强以及组织分割等方面是有效的。
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.
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)
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