Motion blur invariant for estimating motion parameters of medical ultrasound images

Abstract 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 t...

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Autores principales:	Barmak Honarvar Shakibaei Asli, Yifan Zhao, John Ahmet Erkoyuncu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c7f2c687b6b947d28798204c97af9327
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spelling	oai:doaj.org-article:c7f2c687b6b947d28798204c97af93272021-12-02T15:33:13ZMotion blur invariant for estimating motion parameters of medical ultrasound images10.1038/s41598-021-93636-42045-2322https://doaj.org/article/c7f2c687b6b947d28798204c97af93272021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93636-4https://doaj.org/toc/2045-2322Abstract 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.Barmak Honarvar Shakibaei AsliYifan ZhaoJohn Ahmet ErkoyuncuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Barmak Honarvar Shakibaei Asli Yifan Zhao John Ahmet Erkoyuncu Motion blur invariant for estimating motion parameters of medical ultrasound images
description	Abstract 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.
format	article
author	Barmak Honarvar Shakibaei Asli Yifan Zhao John Ahmet Erkoyuncu
author_facet	Barmak Honarvar Shakibaei Asli Yifan Zhao John Ahmet Erkoyuncu
author_sort	Barmak Honarvar Shakibaei Asli
title	Motion blur invariant for estimating motion parameters of medical ultrasound images
title_short	Motion blur invariant for estimating motion parameters of medical ultrasound images
title_full	Motion blur invariant for estimating motion parameters of medical ultrasound images
title_fullStr	Motion blur invariant for estimating motion parameters of medical ultrasound images
title_full_unstemmed	Motion blur invariant for estimating motion parameters of medical ultrasound images
title_sort	motion blur invariant for estimating motion parameters of medical ultrasound images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c7f2c687b6b947d28798204c97af9327
work_keys_str_mv	AT barmakhonarvarshakibaeiasli motionblurinvariantforestimatingmotionparametersofmedicalultrasoundimages AT yifanzhao motionblurinvariantforestimatingmotionparametersofmedicalultrasoundimages AT johnahmeterkoyuncu motionblurinvariantforestimatingmotionparametersofmedicalultrasoundimages
_version_	1718387107351756800

Motion blur invariant for estimating motion parameters of medical ultrasound images

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