标题： 基于感兴趣区域的医学图像压缩 显示英文标题

标题： Region of Interest based Medical Image Compression

摘要： 大量医学图像数据需要高效的压缩技术以支持远程医疗服务。 本文探讨感兴趣区域（ROI）编码，以解决压缩率和图像质量之间的平衡问题。 通过在Brats 2020数据集上应用UNET分割，我们准确识别肿瘤区域，这对于诊断至关重要。 这些区域随后通过高效视频编码（HEVC）进行压缩，提高压缩率的同时保留必要的诊断信息。 这种方法确保关键图像区域保持其质量，而非关键区域则被更多压缩。 我们的方法优化了存储空间和传输带宽，满足远程医疗和大规模医学成像的需求。 通过这种技术，我们提供了一个稳健的解决方案，保持关键数据的完整性并提高医学图像处理的效率。 显示英文摘要

摘要： The vast volume of medical image data necessitates efficient compression techniques to support remote healthcare services. This paper explores Region of Interest (ROI) coding to address the balance between compression rate and image quality. By leveraging UNET segmentation on the Brats 2020 dataset, we accurately identify tumor regions, which are critical for diagnosis. These regions are then subjected to High Efficiency Video Coding (HEVC) for compression, enhancing compression rates while preserving essential diagnostic information. This approach ensures that critical image regions maintain their quality, while non-essential areas are compressed more. Our method optimizes storage space and transmission bandwidth, meeting the demands of telemedicine and large-scale medical imaging. Through this technique, we provide a robust solution that maintains the integrity of vital data and improves the efficiency of medical image handling.