由RG-FCM进行的细分MR图像,经过非均压缩,包括不同编码器的级联压缩
Lovepreet Singh Brar1, Sunil Agrawal1, Jaget Singh1
1Department of Electronics and Communication Engineering, University Institute of Engineering and Technology, Panjab University, Chandigarh160014, India.
Current medical imaging
|March 19, 2025
概括
本研究介绍了一种改进的方法,用于使用无监督机器学习细分来压缩医疗图像. 该技术提高了压缩效率,并保持了诊断信息,优于现有的方法.
科学领域:
- 医疗成像医学成像
- 图像压缩 图像压缩
- 机器学习 机器学习
背景情况:
- 由于文件大小和数据冗余,医疗图像传输和存储面临着挑战.
- 减少医疗图像尺寸而不会丢失诊断信息对于数字成像数据的增长至关重要.
- 现有的压缩方法通常依赖于手动或传统的细分,从而限制了效率.
研究的目的:
- 通过精确的细分和非均的压缩来提高医疗图像压缩效率.
- 开发一种无监督的机器学习方法,精确提取信息图像区域.
- 为了提高医疗图像压缩技术的性能.
主要方法:
- 提出了一个无监督修改的模糊c-means (FCM) 集群用于细分.
- 与FCM (RG-FCM) 集成了一个自动化的区域增长算法,以处理噪音并改善细分.
- 适用于信息和背景图像区域的分化比特率的编码器级联.
主要成果:
- 拟议的RG-FCM技术在磁共振成像 (MRI) 数据集上展示了优异的细分性能.
- 与现有方法相比,综合细分和非均压缩方法实现了更高的压缩指标.
- 经验分析证实了该技术在改善细分和压缩方面的有效性.
结论:
- 分段技术的结合改善了Jaccard和Dice的索引,验证了拟议的压缩方法.
- 编码器的级联进一步支持了新型压缩技术的卓越性能.
- 这种方法使医疗图像的压缩更高,同时保留了临床上重要的信息.
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