Motion blur invariant for estimating motion parameters of medical ultrasound images

0548672 - ÚTIA 2022 RIV GB eng J - Journal Article
Honarvar Shakibaei Asli, Barmak - Zhao, Y. - Erkoyuncu, J. A.
Motion blur invariant for estimating motion parameters of medical ultrasound images.
Scientific Reports. Roč. 11, č. 1 (2021), č. článku 14312. ISSN 2045-2322. E-ISSN 2045-2322
R&D Projects: GA ČR GJ18-26018Y
Institutional support: RVO:67985556
Keywords : motion blur * estimating motion parameters * medical ultrasound imagesBarm
OECD category: Automation and control systems
Impact factor: 4.997, year: 2021
Method of publishing: Open access
http://library.utia.cas.cz/separaty/2021/ZOI/honarvar-0548672.pdf https://www.nature.com/articles/s41598-021-93636-4

High-quality medical ultrasound imaging is definitely concerning motion blur, while medical image analysis requires motionless and accurate data acquired by sonographers. The main idea of this paper is to establish some motion blur invariant in both frequency and moment domain to estimate the motion parameters of ultrasound images. We propose a discrete model of point spread function of motion blur convolution based on the Dirac delta function to simplify the analysis of motion invariant in frequency and moment domain. This model paves the way for estimating the motion angle and length in terms of the proposed invariant features. In this research, the performance of the proposed schemes is compared with other state-of-the-art existing methods of image deblurring. The experimental study performs using fetal phantom images and clinical fetal ultrasound images as well as breast scans. Moreover, to validate the accuracy of the proposed experimental framework, we apply two image quality assessment methods as no-reference and full-reference to show the robustness of the proposed algorithms compared to the well-known approaches.
Permanent Link: http://hdl.handle.net/11104/0324805