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.

翻译：医疗图像数据量巨大，需要高效的压缩技术以支持远程医疗服务。本文探讨了基于感兴趣区域（ROI）的编码方法，以解决压缩率与图像质量之间的平衡问题。通过在Brats 2020数据集上利用UNET分割技术，我们精确识别了对诊断至关重要的肿瘤区域。随后对这些区域应用高效视频编码（HEVC）进行压缩，在保持关键诊断信息的同时提高了压缩率。该方法确保关键图像区域的质量得以保留，而非必要区域则被更大幅度地压缩。我们的方案优化了存储空间和传输带宽，满足了远程医疗和大规模医学影像处理的需求。通过该技术，我们提供了一种在保持重要数据完整性的同时提升医学图像处理效率的稳健解决方案。