Region of Interest based Medical Image Compression
This provides an incremental improvement for telemedicine by optimizing storage and bandwidth for medical imaging.
The paper tackled medical image compression by using UNET segmentation to identify tumor regions on the Brats 2020 dataset and applying HEVC for compression, enhancing compression rates while preserving diagnostic quality in critical areas.
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.