Separation-based model for low-dose CT image denoising

• Main Authors: Wenbin Chen, Junjie Bai, Xiaohua Gu, Yuyan Li, Yanling Shao, Quan Zhang, Yi Liu, Yanli Liu, Zhiguo Gui
• Format: Article
• Language: English
• Published: Wiley 2020-12-01
• Series: The Journal of Engineering
• Subjects: singular value decomposition; image denoising; phantoms; smoothing methods; computerised tomography; medical image processing; filtered structural image; streak artefacts image; structural atoms; low-dose ct image; low-dose computed tomography image; separation-based method; residual mottle noise; image smoothing method; image decomposition structural-preserving image smoothing method; local intuitional fuzzy entropy; modified shepp-logan phantom; k-singular value decomposition algorithm
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0996

Low-dose computed tomography (LDCT) image often contains mottle noise and streak artefacts, which seriously interfere with clinical diagnosis. In this study, the separation-based (SEPB) method is proposed for mottle noise and streak artefacts suppression and structure preservation. In it, the LDCT image is decomposed into the structural image with residual mottle noise and the streak artefacts image with residual structural details by the image decomposition structural-preserving image smoothing method. The structural image is filtered by the K-singular value decomposition algorithm to remove the residual mottle noise, and the structural details in the streak artefacts image are extracted by the morphological component analysis theory. The extracted structural details are added to the filtered structural image to get the LDCT result image. Meanwhile, in the process of extracting the structural details, the streak artefacts dictionary learned from the streak artefacts image is corrected by the local intuitional fuzzy entropy to remove its structural atoms. The experiments are conducted on the modified Shepp–Logan phantom, the pelvis phantom and the clinical abdominal data to evaluate the proposed SEPB method. Compared to several comparative denoising methods, the experimental results show that the SEPB method has better performance in subjective visual effect and objective indicators.