AVITECH Seminar series: 26/02/2019, Nguyễn Thành Trung, CT Image denoising

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Title

CT Image Denoising Using Sparse Representation

Speaker

Mr. Nguyen Thanh Trung, UET, VNU

Time: 15:30, Tuesday, February 26, 2019
Venue: E3-707, 144 Xuan Thuy, Cau Giay, Hanoi

ABSTRACT

X-ray computed tomography (CT) is now a widely used imaging modality for numerous medical purposes. Due to the biological risk of x-ray radiation, developing CT methodologies minimizing x-ray exposure to patients while achieving clinical tasks has been a major concern. However, reducing the radiation dose as in low-dose CT techniques results in images that are often degraded by noise and artifacts. We present here two methods for denoising of low-dose CT images.  In the first method, a noisy image is decomposed into three frequency bands namely low-band, middle-band and high-band such that the noise component mainly is presented in the middle and high bands. Then, by exploiting the fact that a small patch of the noisy image can be approximated by a linear combination of several elements in a given dictionary of noise-free image patches, generated from noise-free CT images taken at nearly the same position with the noisy image, noise on these two bands is effectively eliminated. In the second method, the noisy image is denoised patch-wise in which each noisy patch is estimated by a sparse representation using a dictionary of patches built from noise-free example images, which are similar to the noisy image. Experimental results conducted on both synthetic and real noise data demonstrated the efficiency of the proposed methods.

BIOGRAPHY

Nguyen Thanh Trung is a Ph.D. student at the Faculty of Electronics and Telecommunications, VNU University of Engineering and Technology (VNU-UET), Vietnam National University, Hanoi (VNU). He received the B.Sc. and M.Sc. degrees from VNU-UET respectively in 2003 in Electronics and Telecommunications and 2011 in Electronics Engineering. His research interests are machine learning, biomedical signal and image processing.