Artifact Suppressed Dictionary Learning for Low-Dose CT Image Processing

Chen, Yang; Shi, Luyao; Feng, Qianjing; Jiang, Yang; Hu, Shu; Luo, Limin; Coatrieux, Jean-Louis; Chen, Wufan

doi:10.1109/tmi.2014.2336860

Cited by 290 publications

(131 citation statements)

References 49 publications

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“…One algorithm decomposes the artifact-full image into highfrequency bands in the horizontal, vertical, and diagonal directions and computes the sparse representation of patches of these bands in three "discriminative dictionaries," that include atoms learned to represent artifacts and genuine image features [15]. Artifacts are suppressed by setting to zero the large coefficients that correspond to the artifact atoms.…”

Section: Post-processing Methodsmentioning

confidence: 99%

Patch-based models and algorithms for image processing: a review of the basic principles and methods, and their application in computed tomography

Karimi¹,

Ward²

2016

Int J CARS

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Section: Post-processing Methodsmentioning

confidence: 99%

Patch-based models and algorithms for image processing: a review of the basic principles and methods, and their application in computed tomography

Karimi¹,

Ward²

2016

Int J CARS

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“…For instance, Chen et al [34] proposed a Low-Dose CT (LDCT) image processing method based on artifact suppressed dictionary learning, where an overcomplete global dictionary was included. Li et al [35] used the group-sparse representation with dictionary learning for medical image denoising and fusion, and the dictionary utilized was also overcomplete.…”

Section: Rd and Ed Generationmentioning

confidence: 99%

Blood vessel enhancement via multi-dictionary and sparse coding: Application to retinal vessel enhancing

Chen

Shao

et al. 2016

Neurocomputing

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Blood vessel images can provide considerable information of many diseases, which are widely used by ophthalmologists for disease diagnosis and surgical planning. In this paper, we propose a novel method for the blood Vessel Enhancement via Multi-dictionary and SparseCoding (VE-MSC). In the proposed method, two dictionaries are utilized to gain the vascular structures and details, including the Representation Dictionary (RD) generated from the original vascular images and the Enhancement Dictionary (ED) extracted from the corresponding label images. The sparse coding technology is utilized to represent the original target vessel image with RD. After that, the enhanced target vessel image can be reconstructed using the obtained sparse coefficients and ED. The proposed method has been evaluated for the retinal vessel enhancement on the DRIVE and STARE databases. Experimental results indicate that the proposed method can not only effectively improve the image contrast but also enhance the retinal vascular structures and details.

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“…In a word, it is helpful to improve the effect of image reconstruction by adding proper regularization term. Chen et al [22] proposed image domain artifacts used in low-dose CT image suppression dictionary learning method. The establishment of sparse representation of the discriminant dictionary filters a part of the artifacts and noise.…”

Section: Introductionmentioning

confidence: 99%

Double regularization medical CT image blind restoration reconstruction based on proximal alternating direction method of multipliers

Zhang

Sun

Zhang

et al. 2017

J Image Video Proc.

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To solve the problem of CT image degradation, a double regularization CT image blind restoration reconstruction method was proposed. The objective function including both a clear image and point spread function was established. To avoid the over-smoothing phenomenon and protect the detail, the objective function includes two constraint regularization terms. They are total variation (TV) and wavelet sparsity respectively. The objective function was solved by the alternating direction multiplier method (ADMM), and the optimal solution was obtained. Firstly, the CT image blind restoration reconstruction was decomposed into two sub-problems: reconstructed image estimation and point spread function estimation. Furthermore, each sub-problem can be solved by the proximal alternating direction method of multipliers. Finally, the CT image blind restoration reconstruction was realized. The experimental results show that the proposed algorithm takes into account the degradation effect of projection data, and the proposed algorithm is superior to other existing algorithms in the subjective visual effect. At the same time, in the aspect of objective evaluation, the proposed algorithm improves the objective image quality metrics such as peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), and universal image quality index (UIQI).

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Artifact Suppressed Dictionary Learning for Low-Dose CT Image Processing

Cited by 290 publications

References 49 publications

Patch-based models and algorithms for image processing: a review of the basic principles and methods, and their application in computed tomography

Patch-based models and algorithms for image processing: a review of the basic principles and methods, and their application in computed tomography

Blood vessel enhancement via multi-dictionary and sparse coding: Application to retinal vessel enhancing

Double regularization medical CT image blind restoration reconstruction based on proximal alternating direction method of multipliers

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