vPSNR: a visualization-aware image fidelity metric tailored for diagnostic imaging
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Purpose Often, the large amounts of data generated in diagnostic imaging cause overload problems for IT systems and radiologists. This entails a need of effective use of data reduction beyond lossless levels, which, in turn, underlines the need to measure and control the image fidelity. Existing image fidelity metrics, however, fail to fully support important requirements from a modern clinical context: support for high-dimensional data, visualization awareness, and independence from the original data.
Methods We propose an image fidelity metric, called the visual peak signal-to-noise ratio (vPSNR), fulfilling the three main requirements. A series of image fidelity tests on CT data sets is employed. The impact of visualization transform (grayscale window) on diagnostic quality of irreversibly compressed data sets is evaluated through an observer-based study. In addition, several tests were performed demonstrating the benefits, limitations, and characteristics of vPSNR in different data reduction scenarios.
Results The visualization transform has a significant impact on diagnostic quality, and the vPSNR is capable of representing this effect. Moreover, the tests establish that the vPSNR is broadly applicable.
Conclusions vPSNR fills a gap not served by existing image fidelity metrics, relevant for the clinical context. While vPSNR alone cannot fulfill all image fidelity needs, it can be a useful complement in a wide range of scenarios.
KeywordsImage fidelity Image quality Metric Diagnostic imaging vPSNR Data reduction Image compression Visualization Perception
This work was supported by the Swedish Foundation for Strategic Research, grant SM10-0022. The author would like to thank the participating radiologists and acknowledge the Center for Medical Image Science and Visualization, Linköping University, for access to leading edge infrastructure and image data. Image data used in this research were also obtained from The Cancer Imaging Archive (http://cancerimagingarchive.net/) sponsored by the Cancer Imaging Program, DCTD/NCI/NIH. The author acknowledges the National Cancer Institute and the Foundation for the National Institutes of Health and their critical role in the creation of the free publicly available LIDC-IDRI Database used in this study.
Conflict of interest
Claes Lundström is also employed by Sectra AB, Sweden.
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