Optimization of Ultrasound Information Imaging Algorithm in Cardiovascular Disease Based on Image Enhancement. (25th March 2021)

• Record Type: Journal Article
• Authors: Shao, Yongfu; Wu, Jue; Ou, Hongping; Pei, Min; Liu, Li; Movassagh, Ali Akbar; Sharma, Ashutosh; Dhiman, Gaurav; Gheisari, Mehdi; Asheralieva, Alia
• Other Names: Houssein Essam Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2021(2021)
• Publication Date: 2021-03-25
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2021/5580630
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : 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.