Artifact Suppressed Dictionary Learning for Low-Dose CT Image Processing

By Yang Chen, Luyao Shi, Qianjin Feng, Jian Yang, H. Shu, L. Luo, J. Coatrieux, Wufan Chen. IEEE Transactions on Medical Imaging, 2014

301 citations recorded by the source corpus.

Low-dose computed tomography (LDCT) images are often severely degraded by amplified mottle noise and streak artifacts. These artifacts are often hard to suppress without introducing tissue blurring effects. In this paper, we propose to process LDCT images using a novel image-domain algorithm called “artifact suppressed dictionary learning (ASDL).” In this ASDL method, orientation and scale information on artifacts is exploited to train artifact atoms, which are then combined with tissue feature atoms to build three discriminative dictionaries. The streak artifacts are cancelled via a discriminative sparse representation operation based on these dictionaries. Then, a general dictionary learning processing is applied to further reduce the noise and residual artifacts. Qualitative and quantitative evaluations on a large set of abdominal and mediastinum CT images are carried out and the results show that the proposed method can be efficiently applied in most current CT systems.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d39fe3684b05acd34d210291e8a86ab966161d20

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network (2017, methodology): “Algorithms like K-SVD [2] have made dictionary learning very efficient and has inspired many applications in the medical domain [17, 18, 39, 40, 67].”