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Joint sparsity and fidelity regularization for segmentation-driven CT image preprocessing
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DOI：10.1007/s11432-015-5375-x.png)
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摘要
En 中文
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.
Keyword:
CT image
homogenization
enhancement
tissue segmentation
gradient fidelity
gradient sparsity
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期刊
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7.6
论文数:
4.9K
被引数:
8.9K
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