Signal separation from X-ray image sequence using singular value decomposition. (April 2018)

• Record Type: Journal Article
• Authors: Yu, Chunyu; Sun, Jingyang
• Is Part Of: Biomedical signal processing and control. Volume 42(2018)
• Page Start: 210
• Page End: 215
• Publication Date: 2018-04
• Subjects: X-ray image denoising -- Singular value decomposition (SVD) -- Frame averaging (FA) -- Contrast-to-noise ratio (CNR) -- Weighting factor w -- Glandular ratio; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.01.012
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: This work proposes singular value decomposition (SVD) to separate the signal from a noisy X-ray image sequence without any prior knowledge of the noise. SVD is based on the theory that the noise is always uncorrelated to the signal in a noisy image, and SVD, which belongs to Blind Source Separation (BSS), can decorrelate the signal from the noise components. To apply this proposed denoising method, two groups of X-ray images produced at 25 kV & 20 mAs and 34 kV & 20 mAs are sampled. To measure the proposed denoising method, ROIs with differing glandularity are selected. This work supports the use of SVD in X-ray image denoising. Normally, the separated signal will be less noisy when more noisy images are included for separating signals. Compared with other classical denoising methods, SVD is superior in reducing noise and improving CNR or SNR.