<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Due to the amount of medical image data being produced and transferred over networks, employing lossy compression has been accepted by worldwide regulatory bodies. As expected, increasing the degree of compression leads to decreasing image fidelity. The extent of allowable irreversible compression is dependent on the imaging modality and the nature of the image pathology as well as anatomy. Interpolation, which often causes image distortion, has been extensively used to rescale images during radiological diagnosis. This work attempts to assess the quality of medical images after the application of lossy compression followed by rescaling. This research proposes a full-reference objective measure of quality for medical images that considers their deterministic and statistical properties. Statistical features are acquired from the frequency domain of the signal and are combined with elements of the structural similarity index (SSIM). The aim is to construct a model that is specialized for medical images and that could serve as a predictor of quality.</p></abstract>

Due to the amount of medical image data being produced and transferred over networks, employing lossy compression has been accepted by worldwide regulatory bodies. As expected, increasing the degree of compression leads to decreasing image fidelity. The extent of allowable irreversible compression is dependent on the imaging modality and the nature of the image pathology as well as anatomy. Interpolation, which often causes image distortion, has been extensively used to rescale images during radiological diagnosis. This work attempts to assess the quality of medical images after the application of lossy compression followed by rescaling. This research proposes a full-reference objective measure of quality for medical images that considers their deterministic and statistical properties. Statistical features are acquired from the frequency domain of the signal and are combined with elements of the structural similarity index (SSIM). The aim is to construct a model that is specialized for medical images and that could serve as a predictor of quality.

The fidelity of compressed and interpolated medical images

Institute of Teleinformatics, Faculty of Computer Science and Telecommunacations

Technical Transactions

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

{"article-title":"The fidelity of compressed and interpolated medical images"}

Due to the amount of medical image data being produced and transferred over networks, employing lossy compression has been accepted by worldwide regulatory...

The fidelity of compressed and interpolated medical images

Published Online: Oct 05, 2020

Page range: -

Received: Apr 28, 2020

Accepted: Sep 28, 2020

DOI: https://doi.org/10.37705/TechTrans/e2020031

Keywords
medical image quality, image quality assessment, structural similarity index, SSIM, compression, interpolation

© 2020 Ilona Anna Urbaniak et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

The fidelity of compressed and interpolated medical images

Published Online: Oct 05, 2020

Page range: -

Received: Apr 28, 2020

Accepted: Sep 28, 2020

DOI: https://doi.org/10.37705/TechTrans/e2020031

Keywordsmedical image quality, image quality assessment, structural similarity index, SSIM, compression, interpolation

© 2020 Ilona Anna Urbaniak et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Keywords
medical image quality, image quality assessment, structural similarity index, SSIM, compression, interpolation