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Optimization of ultrasound information imaging algorithm in cardiovascular disease based on image enhancement

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posted on 2024-10-14, 12:55 authored by Yongfu Shao, Jue Wu, Hongping Ou, Min Pei, Li Liu, Ali Akbar Movassagh, Ashutosh Sharma, Gaurav Dhiman, Mehdi Gheisari, Alia AsheralievaAlia Asheralieva
To improve the interpretability or perception of information in images for human viewers is the main goal of image enhancement. Aiming at the problem that image edges are difficult to determine due to artefacts, plaques, and vascular branches in cardiovascular ultrasound, an edge ultrasound imaging detection algorithm based on spatial-frequency-domain image enhancement is proposed to improve the clarity of ultrasound images. Firstly, this paper uses the space-frequency-domain enhancement algorithm to enhance the image. This algorithm overcomes the problem of low contrast of conventional algorithms. The enhanced data matrix is used as the cost matrix, and then, the heuristic image search method is used to search the image of the cost matrix. The results show that the use of spatial-frequency-domain image ultrasound imaging algorithm can improve the contrast and sharpness of ultrasound images of cardiovascular disease, which can make the middle edge of the image clearer, the detection accuracy rate is increased to 92.76%, and the ultrasound of cardiovascular disease is improved. The edge of the image gets accuracy. The paper confirms that the ultrasound imaging algorithm based on spatial-frequency-domain image enhancement is worthy of application in clinical ultrasound image processing. The performance of the proposed technique is 32.54%, 75.30%, 21.19%, 21.26%, and 11.10% better than the existing technique in terms of edge energy, detail energy, sharpness, contrast, and information entropy, respectively.

Funding

National Natural Science Foundation of China: grant Number: 61950410603

Government of the Russian Federation

History

School

  • Science

Department

  • Computer Science

Published in

Mathematical Problems in Engineering

Volume

2021

Pages

1 - 13

Publisher

Hindawi Limited

Version

  • VoR (Version of Record)

Rights holder

© 2021 Yongfu Shao et al

Publisher statement

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Acceptance date

2021-02-08

Publication date

2021-03-25

Copyright date

2021

Notes

This article is part of Special Issue: Metaheuristics and Machine Learning: Theory and Applications.

ISSN

1024-123X

eISSN

1563-5147

Language

  • en

Editor(s)

Essam Houssein

Depositor

Dr Alia Asheralieva. Deposit date: 29 May 2024

Article number

580630

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