Problems of the state of the art in medical images compression

The automation of radiology services has significantly improved access to radiological imaging for accurate diagnosis of diseases and injuries. However, the expansion of radiological equipment, the adoption of telemedicine, and the integration of AI-powered clinical decision support systems necessitate upgrades to existing medical image storage and processing solutions. This article reviews modern compression methods for radiological images, which offer higher compression ratios, im­proved image quality, and faster encoding/decoding times compared to the standards defined by the DICOM specification. It is established that radiological images possess unique characteristics—such as high noise levels, locally symmetric regions (similar patches), and the presence of multiple sequential frames in a single study—which, when accounted for in compression algorithms, can enhance compression efficiency. Implementing advanced data compression approaches can increase the fault tolerance of high-load medical systems and reduce costs associated with the storage, transmission, and processing of diagnostic studies.