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

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.

Figures (3)

• Figure 1: ROI-centric medical image compression. (a) 2D slice-wise compression pipeline: individual axial slices are extracted from the cropped volume and compressed/reconstructed independently. (b) 3D volumetric compression pipeline: ROI extraction, metadata generation, and volume-level codec compression/reconstruction. Both pipelines use metadata M (54 bytes) storing bounding box coordinates, original volume dimensions, and affine matrix for spatial restoration during decompression.
• Figure 2: Rate-distortion performance of evaluated compression methods on the ADNI dataset. Each point represents the average PSNR and compression ratio across 200 brain MRI volumes. Solid markers indicate full-volume compression, while hollow markers represent ROI-based compression.
• Figure 3: Qualitative comparison of Full vs ROI compression results. Visual comparison of reconstruction quality across different compression methods on subject 002_S_0413 (slice 128). Rows 1-2 show Full and ROI compression for 3D codecs (SirenMRI, JPEG2000-3D Lv3) and 2D conventional codec (JPEG2000-2D Lv3), while rows 3-4 display 2D neural compression codec (BCM-Net, TCM, TCM+AuxT) and HEIF Lv3. Ground truth is shown in the leftmost column, with the ROI bounding box (white rectangle) indicating the cropped region.