Irreversible compression in digital radiology. A literature review

Abstract 

The purpose of this literature review was to explore the research conducted to date on the use of irreversible compression in digital diagnostic radiology.

The degree of research on the use of irreversible compression in digital radiology is still in its infancy, since the technologies for digital radiology are still evolving. However, 90 papers reviewed address research examining the use of various compression ratios on image quality and observer performance on several detection tasks such as identifying structures and lesion detection, on chest, CT, skeletal, angiography, mammography, MRI, nuclear medicine, ultrasound, and teleradiology images.

In general the results of these studies show that image types in digital radiology are different based on their mode of generation, as well as their spatial and contrast resolution, determined by their matrix size/pixel size, and bit depth, respectively. Furthermore, there are several forms of irreversible compression algorithms, and they are not all equal in terms of performance. Additionally, of the three evaluation methods used to measure observer performance on compressed images, the ROC methodology is most commonly used.

Some types of images such as digitized chest images, CT, MRI and ultrasound images have different “compression tolerance” and therefore a single compression ratio cannot be assigned to a modality, even for a given organ system. Chest images for example can be compressed at ratios as high as 10:1–20:1 using CR and DR without compromising image quality. Other image types such as CT images for example, can be compressed at ratios as high as 20:1 in the detection of coronary artery calcification. The results of these studies would appear to indicate that image compression in digital radiology would have to be optimized based on the types of images being generated, interpreted for primary diagnosis, stored, and transmitted to remote sites for clinical review by physicians other than radiologists.

Keywords: Digital image, Digital image compression, Irreversible compression, Research, Evaluation methods