Abstract
The angiography image enhancement technology has the potential to enhance the vascular structure in the image while suppressing the background and nonvascular structures simultaneously. This technology has the ability to enhance the result as close to the real structure of blood vessels as possible. Angiographic image processing is one of the essential contents in the field of medical image processing and analysis. However, the existing cardiovascular angiography schemes suffer from various issues. In this paper, the detection process of cardiovascular angiography is studied by combining the Internet of Things and rough set technology. Firstly, this paper designs the architecture design of the cardiovascular angiography process combined with the Internet of Things technology. Secondly, this paper uses a rough set algorithm to optimize the background noise and boundary shrinkage because of the sensitivity of the contrast background noise and boundary shrinkage. Simulation results verified the applicability and efficiency of the proposed model in the cardiovascular angiography scheme. The model has been optimized during implementation. Compared with the traditional algorithm, the same image data processing speed is significantly improved to ensure the enhancement effect.
Highlights
In recent years, cardiovascular disease has become a significant disease affecting human health and even lifethreatening. e “World Health Report” pointed out that there are as many as 17.5 million deaths due to cardiovascular diseases in the world each year [1, 2]
Due to the complex structure of the heart and blood vessels, motion imaging, uneven distribution of contrast agents, and other reasons, it is challenging to segment cardiovascular angiography images, which has attracted many experts and scholars [6, 7]. e coronary arteries have a tree-shaped structure as a whole, while the vascular wall is linear. ese characteristics determine the particularity of its segmentation method
Medical image processing technology is used to assist in analyzing cardiovascular angiography images that can quickly and accurately segment and measure coronary arteries
Summary
Cardiovascular disease has become a significant disease affecting human health and even lifethreatening. e “World Health Report” pointed out that there are as many as 17.5 million deaths due to cardiovascular diseases in the world each year [1, 2]. Due to the complex structure of the heart and blood vessels, motion imaging, uneven distribution of contrast agents, and other reasons, it is challenging to segment cardiovascular angiography images, which has attracted many experts and scholars [6, 7]. Medical image processing technology is used to assist in analyzing cardiovascular angiography images that can quickly and accurately segment and measure coronary arteries. It helps in evaluating the severity of coronary artery disease and assists doctors in making an objective and accurate diagnosis [14, 15]. To resolve the aforementioned issues, we have proposed a hybrid detection model which is based on the Internet of things (IoT) and rough set technology.
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