Purpose: This study aimed to develop a fully automatic, no-reference image-quality assessment (IQA) method for MR images. Methods: New quality-aware features were obtained by applying multidirectional filters to MR images and examining the feature statistics. A histogram of these features was then fitted to a generalized Gaussian distribution function for which the shape parameters yielded different values depending on the type of distortion in the MR image. Standard feature statistics were established through a training process based on high-quality MR images without distortion. Subsequently, the feature statistics of a test MR image were calculated and compared with the standards. The quality score was calculated as the difference between the shape parameters of the test image and the undistorted standard images. Results: The proposed IQA method showed a >0.99 correlation with the conventional full-reference assessment methods; accordingly, this proposed method yielded the best performance among no-reference IQA methods for images containing six types of synthetic, MR-specific distortions. In addition, for authentically distorted images, the proposed method yielded the highest correlation with subjective assessments by human observers, thus demonstrating its superior performance over other no-reference IQAs. Conclusion: Our proposed IQA was designed to consider MR-specific features and outperformed other no-reference IQAs designed mainly for photographic images. Magn Reson Med 80:914–924, 2018.
Bibliographical noteFunding Information:
School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea. Grant sponsor: National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP); Grant number: 2016R1A2R4015016. *Correspondence to: Dosik Hwang, Ph.D., College of Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea. E-mail: email@example.com Received 7 June 2017; revised 16 November 2017; accepted 20 December 2017 DOI 10.1002/mrm.27084 Published online 30 January 2018 in Wiley Online Library (wileyonlinelibrary. com).
Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging