Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
80 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A study on the adequacy of common IQA measures for medical images (2405.19224v4)

Published 29 May 2024 in eess.IV and cs.CV

Abstract: Image quality assessment (IQA) is standard practice in the development stage of novel machine learning algorithms that operate on images. The most commonly used IQA measures have been developed and tested for natural images, but not in the medical setting. Reported inconsistencies arising in medical images are not surprising, as they have different properties than natural images. In this study, we test the applicability of common IQA measures for medical image data by comparing their assessment to manually rated chest X-ray (5 experts) and photoacoustic image data (2 experts). Moreover, we include supplementary studies on grayscale natural images and accelerated brain MRI data. The results of all experiments show a similar outcome in line with previous findings for medical images: PSNR and SSIM in the default setting are in the lower range of the result list and HaarPSI outperforms the other tested measures in the overall performance. Also among the top performers in our experiments are the full reference measures FSIM, LPIPS and MS-SSIM. Generally, the results on natural images yield considerably higher correlations, suggesting that additional employment of tailored IQA measures for medical imaging algorithms is needed.

PDF HTML Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (35)
  1. Z. Wang et al. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600–612, 2004.
  2. S. Athar and Z. Wang. A comprehensive performance evaluation of image quality assessment algorithms. IEEE Access, 7:140030–140070, 09 2019.
  3. S. Kastryulina et al. Image quality assessment for magnetic resonance imaging. Elsevier Medical Image Analysis, 2022.
  4. L. S. Chow et al. Correlation between subjective and objective assessment of magnetic resonance (mr) images. Magn Reson Imaging, 34(6):820–831, Jul 2016.
  5. K. Ohashi et al. Applicability evaluation of full-reference image quality assessment methods for computed tomography images. Journal of Digital Imaging, 36(6):2623–2634, 2023.
  6. G. P. Renieblas et al. Structural similarity index family for image quality assessment in radiological images. J Med Imaging (Bellingham), 4(3):035501, Jul 2017.
  7. B. Girod. Psychovisual aspects of image processing: What’s wrong with mean squared error? In Proceedings of the Seventh Workshop on Multidimensional Signal Processing, pp. P.2–P.2, 1991.
  8. Multi-scale structural similarity for image quality assessment. In Proceedings of the 37th IEEE Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, 2003.
  9. Z. Wang and Q. Li. Information content weighting for perceptual image quality assessment. IEEE Transactions on Image Processing, 20(5):1185–1198, 2011.
  10. K. Ding et al. Image quality assessment: Unifying structure and texture similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5):2567–2581, 2022.
  11. A. Balanov et al. Image quality assessment based on dct subband similarity. In 2015 IEEE International Conference on Image Processing (ICIP), pp. 2105–2109, 2015.
  12. L. Zhang et al. Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing, 20(8):2378–2386, 2011.
  13. W. Xue et al. Gradient magnitude similarity deviation: A highly efficient perceptual image quality index. IEEE Transactions on Image Processing, 23(2):684–695, 2014.
  14. R. Reisenhofer et al. A haar wavelet-based perceptual similarity index for image quality assessment. Signal Process. Image Commun., 61:33–43, 2018.
  15. R. Zhang et al. The unreasonable effectiveness of deep features as a perceptual metric. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 586–595, 2018.
  16. H. Ziaei Nafchi et al. Mean deviation similarity index: Efficient and reliable full-reference image quality evaluator. IEEE Access, 4:5579–5590, 2016.
  17. H. Sheikh and A. Bovik. A visual information fidelity approach to video quality assessment. The First International Workshop on Video Processing and Quality Metrics for Consumer Electronics, 2005.
  18. L. Zhang et al. Vsi: A visual saliency-induced index for perceptual image quality assessment. IEEE Transactions on Image Processing, 23(10):4270–4281, 2014.
  19. A. Mittal et al. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21(12):4695–4708, 2012.
  20. Z. Ying et al. From patches to pictures (paq-2-piq): Mapping the perceptual space of picture quality. pp. 3572–3582, 06 2020.
  21. A. Mittal et al. Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters, 20(3):209–212, 2013.
  22. A. K. Venkataramanan et al. A hitchhiker’s guide to structural similarity. IEEE Access, 9:28872–28896, 2021.
  23. I. Selby. Github repository speedyiqa, March 2024.
  24. H. Sheikh et al. A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing, 15(11):3440–3451, 2006.
  25. H. R. Sheikh et al. Live image quality assessment database release 2, http://live.ece.utexas.edu/research/quality.
  26. D. Jayaraman et al. Objective quality assessment of multiply distorted images. In 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), pp. 1693–1697, 2012.
  27. J. Zbontar et al. fastmri: An open dataset and benchmarks for accelerated mri, 2019.
  28. A. Sriram et al. End-to-end variational networks for accelerated mri reconstruction. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II 23, pp. 64–73. Springer, 2020.
  29. Z. Ramzi et al. XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge. In ISMRM, pp. 1–4, 2021.
  30. H. Assi et al. A review of a strategic roadmapping exercise to advance clinical translation of photoacoustic imaging: From current barriers to future adoption. Photoacoustics, 32:100539, 2023.
  31. B. Cox et al. Quantitative spectroscopic photoacoustic imaging: a review. J Biomed Opt, 17(6):061202, Jun 2012.
  32. J. Grohl et al. Moving beyond simulation: data-driven quantitative photoacoustic imaging using tissue-mimicking phantoms. IEEE Trans Med Imaging, PP, Nov 2023.
  33. J. W. Pickering et al. Double-integrating-sphere system for measuring the optical properties of tissue. Appl. Opt., 32(4):399–410, Feb 1993.
  34. K. Lei et al. Artifact- and content-specific quality assessment for mri with image rulers. Medical Image Analysis, 77:102344, 2022.
  35. M. Chun et al. Fully automated image quality evaluation on patient ct: Multi-vendor and multi-reconstruction study. PLoS One, 17(7):e0271724, 2022.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (14)
  1. Anna Breger (14 papers)
  2. Clemens Karner (6 papers)
  3. Ian Selby (5 papers)
  4. Janek Gröhl (20 papers)
  5. Sören Dittmer (20 papers)
  6. Edward Lilley (2 papers)
  7. Judith Babar (2 papers)
  8. Jake Beckford (2 papers)
  9. Timothy J Sadler (2 papers)
  10. Shahab Shahipasand (3 papers)
  11. Arthikkaa Thavakumar (2 papers)
  12. Michael Roberts (25 papers)
  13. Carola-Bibiane Schönlieb (276 papers)
  14. Thomas R Else (2 papers)
Citations (3)
View on Semantic Scholar
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets