Region of Interest based Medical Image Compression

TL;DR

This paper presents a novel ROI-based medical image compression method that uses UNET segmentation to identify critical regions and applies HEVC compression, balancing image quality and storage efficiency for telemedicine.

Contribution

It introduces a combined segmentation and compression approach specifically tailored for medical images, optimizing storage and transmission without losing vital diagnostic information.

Findings

Accurately identifies tumor regions using UNET segmentation.

Enhances compression efficiency while preserving critical image details.

Supports remote healthcare by reducing data size and maintaining image quality.

Abstract

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.