Medical Image Compression - AI Data Preprocessing Solution

A single CT/MRI examination produces hundreds of DICOM slices, with total sizes reaching hundreds of MB or even GB levels; whole-slide pathology TIFF scan files can easily reach several GB, placing enormous storage pressure from massive image data.

PACS image archiving systems need to retain image data for years or even decades, with storage capacity constantly reaching critical limits, short hardware expansion cycles, high costs, and retrieval speeds for older images declining as data volume grows.

Medical images are used for clinical diagnosis, requiring extremely high image fidelity. Any information loss may affect lesion identification and diagnostic accuracy. Traditional lossy compression solutions struggle to meet medical compliance requirements.

DICOM image metadata contains sensitive privacy data such as patient names, ID numbers, and examination information. There are privacy leakage risks during image archiving, transmission, and research sharing, requiring strict de-identification processing.

Performs lossless compression on DICOM images, ensuring compressed pixel data is completely identical to the original data without affecting any diagnostic information, meeting medical image lossless archiving compliance requirements.

Intelligently optimizes the multi-level pyramid structure of whole-slide pathology TIFF files, significantly reducing file size while maintaining image quality in diagnostically critical areas, balancing quality and storage efficiency.

The compression process strictly maintains diagnostic-grade image quality. Lossless mode ensures zero information loss, while near-lossless mode achieves higher compression ratios within controlled error ranges without affecting clinical diagnostic interpretation.

Optionally performs automatic de-identification of patient privacy fields in DICOM metadata during compression, clearing or replacing sensitive information to meet medical data research sharing and privacy protection compliance requirements.

Acquire raw DICOM/TIFF images from CT, MRI, ultrasound, pathology scanners, and other imaging equipment, connecting to the compression service via API or file queue.

Automatically identifies DICOM, TIFF, and other image formats and their internal structures, parses metadata tags, distinguishes pixel data from patient information, and provides the basis for subsequent compression strategies.

Selects lossless or near-lossless compression strategies based on image type and usage scenario. DICOM defaults to lossless compression to ensure diagnostic accuracy, while TIFF pathology slides support near-lossless mode for improved compression ratios.

Completely preserves the DICOM metadata structure during compression, with optional privacy field de-identification, ensuring compressed image metadata remains complete and usable while meeting privacy protection requirements.

Compressed images are written back to the PACS system or image archive storage, with standard DICOM format output, compatible with existing PACS retrieval and diagnostic workflows.

DICOM lossless compression ratio

TIFF compression ratio

Supported standard

Zero pixel data loss

Performs lossless compression archiving of CT, MRI, and other tomographic scan images, reducing PACS storage footprint while ensuring diagnostic-grade image quality, extending archive hardware lifecycle.

Intelligently compresses digital whole-slide pathology TIFF files, significantly reducing multi-level pyramid data volume while maintaining image quality in diagnostically critical areas, lowering pathology database storage costs.

Compressed image files are significantly smaller, accelerating data transmission in remote diagnosis and regional imaging collaboration, reducing bandwidth usage, and improving image retrieval efficiency for primary healthcare institutions.

Apply for an API Key and make your first image compression request using curl/Python/Java/Node.js

Complete OpenAPI 3.0 specification, online debugger, and error code reference

Rust SDK dynamic library, C FFI headers, Python bindings, and multi-language example code