基于计算机视觉的光学磁共振成像,用于提取和分析肾肿瘤的形态特征。

IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS SLAS Technology Pub Date : 2024-09-16 DOI:10.1016/j.slast.2024.100192
Wu Deng , Xiaohai He , Jia Xu , Boyuan Ding , Songcen Dai , Chao Wei , Hui Pu , Yi Wei , Xinpeng Ren
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引用次数: 0

摘要

计算机视觉技术在市场上的应用越来越广泛。目标检测和特征提取是该技术的两种重要辅助手段,有助于分析目标运动数据。然而,在生物学领域,对细菌和肿瘤等目标的分析还存在一些数据限制,需要进一步探索。基于计算机视觉的光学核磁共振成像技术为提取和分析肾脏肿瘤的形态特征提供了一种新方法。本文设计并开发了一种基于计算机视觉的光学核磁共振成像方法,用于提取和分析肾脏肿瘤的形态特征。利用基于计算机视觉的光学核磁共振成像技术,通过分析肾脏肿瘤的光学特征和核磁共振图像,提取肾脏肿瘤的形态学特征,并建立仿真模型模拟不同类型肾脏肿瘤的形态学特征,利用计算机算法进行特征提取和分析。通过对模拟模型的分析,提取并分析了肾脏肿瘤的形态学特征,为肾脏肿瘤的临床诊断和治疗提供了一种无创的新方法。
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Optical MRI imaging based on computer vision for extracting and analyzing morphological features of renal tumors

Computer vision technology is more and more widely used in the market. Target detection and feature extraction are two important auxiliary means of this technique, which are helpful to analyze target motion data. However, in the field of biology, there are some data limitations in the analysis of targets such as bacteria and tumors, which need to be further explored. Optical MRI imaging technology based on computer vision provides a new way to extract and analyze morphological features of renal tumors. In this paper, an optical MRI imaging method based on computer vision is designed and developed for the extraction and analysis of morphological features of kidney tumors. By using optical MRI imaging technology based on computer vision, the morphological characteristics of kidney tumors were extracted by analyzing the optical characteristics and MRI images of kidney tumors, and a simulation model was established to simulate the morphological characteristics of different types of kidney tumors, and feature extraction and analysis were carried out by computer algorithm. Through the analysis of the simulation model, the morphological characteristics of renal tumors were extracted and analyzed, which provided a new and non-invasive method for clinical diagnosis and treatment of renal tumors.

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来源期刊
SLAS Technology
SLAS Technology Computer Science-Computer Science Applications
CiteScore
6.30
自引率
7.40%
发文量
47
审稿时长
106 days
期刊介绍: SLAS Technology emphasizes scientific and technical advances that enable and improve life sciences research and development; drug-delivery; diagnostics; biomedical and molecular imaging; and personalized and precision medicine. This includes high-throughput and other laboratory automation technologies; micro/nanotechnologies; analytical, separation and quantitative techniques; synthetic chemistry and biology; informatics (data analysis, statistics, bio, genomic and chemoinformatics); and more.
期刊最新文献
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