优化了基于哈夫曼编码的医疗图像压缩,并改进了HDBSCAN
Rajasekhar Butta1, Mastan Sharif Shaik2
1Department of Electronics Communication Engineering, Seshadri Rao Gudlavaller Engineering College, Gudlavalleru, India.
Network (Bristol, England)
|June 19, 2025
概括
本研究介绍了一种新型的图像压缩模型,使用带有噪声的应用程序的改进层次密度的空间集群 (HDBSCAN) 和蛇更新的BES优化 (SU-BESO) 进行高效的数据传输和存储.
科学领域:
- 医疗成像医学成像
- 图像处理 图像处理
- 数据压缩数据压缩
背景情况:
- 医学成像产生了大量的数据,增加了传输和存储负担.
- 有效的图像压缩对于优化存储容量和网络带宽至关重要.
研究的目的:
- 为医疗成像数据开发一种新的图像压缩模型.
- 提高医疗成像数据传输和存储的效率.
主要方法:
- 图像细分使用带有噪声的应用程序 (HDBSCAN) 的基于层次密度的空间聚类,以区分感兴趣区域 (ROI).
- 通过使用Snake Updated BES Optimization (SU-BESO) 进行最佳参数选择的改进的Huffman编码进行图像压缩.
- 解码涉及哈夫曼解码和区域融合.
主要成果:
- 开发的SU-BESO模型显示了提高图像压缩效率的潜力.
- 有效的ROI分离和优化的编码参数有助于减少数据大小.
结论:
- 拟议的SU-BESO模型为医疗图像数据传输和存储的挑战提供了一个有希望的解决方案.
- 集成HDBSCAN和SU-BESO优化了压缩过程,证明了它的潜力.
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