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相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Brain Imaging01:14

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
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Imaging Studies for Cardiovascular System IV: CMRI01:21

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Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
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Imaging Studies IV: Magnetic Resonance Imaging01:27

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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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相关实验视频

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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使用多核FCM EHO方法对脑MRI图像进行细分.

Sreedhar Kollem1, Ch Rajendra Prasad1, J Ajayan1

  • 1Department of ECE, School of Engineering, SR University, Warangal-506371, Telangana, India.

Current medical imaging
|February 23, 2024
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概括

这项研究引入了一种有效的新方法,用于在MRI扫描中对脑瘤进行细分. 与传统方法相比,拟议的技术提高了识别瘤区域的准确性.

关键词:
增强对比度 增强对比度多核模糊c意味着集群.优化优化 优化优化部分微分方程部分微分方程.分段化 分段化 分段化 分段化持有门的人.

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科学领域:

  • 医疗成像医学成像
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 由于形状,位置和强度的变化,图像细分具有挑战性.
  • 脑瘤检测和细分在医学诊断中至关重要.
  • 准确的细分有助于治疗规划和患者的治疗结果.

研究的目的:

  • 将脑磁共振成像 (MRI) 图像细分为瘤和非瘤区域.
  • 开发一种用于精确细分脑瘤的自动化方法.
  • 在医学图像中提高脑瘤的可见性和划分.

主要方法:

  • 利用了来自BraTS2020数据库的MRI图像.
  • 应用使用值的对比增强.
  • 实现了用第四阶偏微分方程进行图像表示.
  • 采用大象牧养算法来优化心脏状元.
  • 使用多个内核模糊c-means集群执行图像分割.

主要成果:

  • 使用峰值信号与噪声比率,平均平方误差,灵敏度,特异性和精度来评估性能.
  • 与传统技术相比,拟议的方法显示出更高的性能.
  • 定量指标表明瘤细分的准确性提高.

结论:

  • 开发的方法是用于脑瘤细分的更有效的技术.
  • 拟议的方法比现有方法提供了更好的准确性和可靠性.
  • 这种技术在脑瘤分析中显示出临床应用的前景.