Problem
Research questions and friction points this paper is trying to address.
medical image compression
region of interest
codec-agnostic
storage efficiency
data transfer
MedROI: Codec-Agnostic Region of Interest-Centric Compression for Medical Images
🤖 AI Summary
Medical image compression often suffers from inefficiency due to the retention of diagnostically irrelevant background regions. This work proposes MedROI, a lightweight framework that first crops compact tissue regions using an intensity threshold and records 54 bytes of metadata to enable spatial restoration during decompression, after which the cropped data is passed to any off-the-shelf 2D or 3D codec. MedROI achieves, for the first time, a codec-agnostic, plug-and-play region-of-interest (ROI)-centric compression scheme without requiring modifications or retraining of existing encoders. Evaluated on ADNI brain MRI data, MedROI substantially improves both compression ratio and speed—for instance, increasing JPEG2000’s 2D compression ratio from 20.35 to 27.37 while reducing encoding time from 1.701 s to 1.380 s—without compromising reconstruction quality within the ROI.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
codec-agnostic
region of interest (ROI)
medical image compression
background voxel removal
metadata-guided restoration
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