聚焦病灶区域压缩，提升医学影像传输效率
Region of Interest based Medical Image Compression
- 用UNet分割肿瘤区域，精准识别关键诊断区
- 病灶区用HEVC压缩，非病灶区大幅压缩，保真同时提率
- 适合远程医疗、大规模影像存储场景
海量医学图像数据亟需高效压缩以支撑远程医疗服务。本文探索基于感兴趣区域（ROI）的编码方法，在压缩率与图像质量间取得平衡。利用在BraTS 2020数据集上的UNet分割，准确识别出对诊断至关重要的肿瘤区域，并对这些区域采用高效视频编码（HEVC）进行压缩，从而在保持关键诊断信息的同时显著提升压缩率。该方法确保关键区域图像质量不受损，非关键区域则实现更大幅度压缩。实验表明，该方案有效优化了存储空间与传输带宽，满足远程医疗和大规模医学影像处理的需求。本方法为保障核心数据完整性的同时提升医学影像处理效率提供了可靠解决方案。
原文摘要 · Abstract (English)
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.
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