title: Adaptive Tensor-Based Principal Component Analysis for Low-Dose CT Image Denoising.
abstract: Computed tomography (CT) has a revolutionized diagnostic radiology but involves large radiation doses that directly impact image quality. In this paper, we propose adaptive tensor-based principal component analysis (AT-PCA) algorithm for low-dose CT image denoising. Pixels in the image are presented by their nearby neighbors, and are modeled as a patch. Adaptive searching windows are calculated to find similar patches as training groups for further processing. Tensor-based PCA is used to obtain transformation matrices, and coefficients are sequentially shrunk by the linear minimum mean square error. Reconstructed patches are obtained, and a denoised image is finally achieved by aggregating all of these patches. The experimental results of the standard test image show that the best results are obtained with two denoising rounds according to six quantitative measures. For the experiment on the clinical images, the proposed AT-PCA method can suppress the noise, enhance the edge, and improve the image quality more effectively than NLM and KSVD denoising methods.
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
authors.forename: Danni
authors.initials: D
authors.lastname: Ai
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Yang
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
authors.forename: Jingfan
authors.initials: J
authors.lastname: Fan
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
authors.forename: Weijian
authors.initials: W
authors.lastname: Cong
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
authors.forename: Yongtian
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D006801:Humans; D025341:Principal Component Analysis; D011829:Radiation Dosage; D059629:Signal-To-Noise Ratio; D014057:Tomography, X-Ray Computed / Q000379:methods*