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
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.
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.
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