MedROI: Codec-Agnostic Region of Interest-Centric Compression for Medical Images

TL;DR

MedROI is a flexible ROI-centric compression framework for medical images that improves compression efficiency and speed by discarding background voxels before applying existing codecs.

Contribution

It introduces a codec-agnostic, plug-and-play method that extracts and compresses only the diagnostic ROI, enhancing compression ratios and processing times without retraining.

Findings

MedROI significantly improves compression ratios across multiple codecs.

MedROI reduces encoding and decoding times for most configurations.

Reconstruction quality within the ROI remains comparable to full-image compression.

Abstract

Medical imaging archives are growing rapidly in both size and resolution, making efficient compression increasingly important for storage and data transfer. Most existing codecs compress full images/volumes(including non-diagnostic background) or apply differential ROI coding that still preserves background bits. We propose MedROI, a codec-agnostic, plug-and-play ROI-centric framework that discards background voxels prior to compression. MedROI extracts a tight tissue bounding box via lightweight intensity-based thresholding and stores a fixed 54byte meta data record to enable spatial restoration during decompression. The cropped ROI is then compressed using any existing 2D or 3D codec without architectural modifications or retraining. We evaluate MedROI on 200 T1-weighted brain MRI volumes from ADNI using 6 codec configurations spanning conventional codecs (JPEG2000 2D/3D, HEIF) and neural compressors (LIC_TCM, TCM+AuxT, BCM-Net, SirenMRI). MedROI yields statistically significant improvements in compression ratio and encoding/decoding time for most configurations (two-sided t-test with multiple-comparison correction), while maintaining comparable reconstruction quality when measured within the ROI; HEIF is the primary exception in compression-ratio gains. For example, on JPEG20002D (lv3), MedROI improves CR from 20.35 to 27.37 while reducing average compression time from 1.701s to 1.380s. Code is available at https://github.com/labhai/MedROI.