A customized acutance metric for quality control applications in MRI | Medical & Biological Engineering & Computing Skip to main content
Springer Nature Link
Log in

A customized acutance metric for quality control applications in MRI

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Acutance is a subjective parameter which indicates the quality of edges in an image. Objective metrics for measuring image acutance are helpful for designing new imaging protocols and sequences in magnetic resonance imaging (MRI) studies. In addition to this, image acutance metrics have a significant role in the design and optimisation of post-processing algorithms used for restoration and sharpening of MR imagery. Most of the existing blur/sharpness metrics are specifically designed for natural-scene (panoramic) images. A blur/sharpness metric suitable for MR imaging applications is absent in the literature. To fill this gap, a computationally fast metric, ‘largest local gradient-based sharpness metric (LLGSM)’, for measuring sharpness and blur in MR imagery, is proposed in this paper. The LLGSM is the root mean square (RMS) of exponentially weighted elements in an array of lexicographically ordered largest local gradient (LLG) values in the image, sorted in descending order. In terms of overall agreement with subjective scores, and computational speed, the LLGSM is observed to be more efficient than its alternatives available in the literature.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Availability of data and material

Data may be provided on request.

Code availability

Code can be made available on request.

References

  1. Osadebey M, Pedersen M, Arnold D, Wendel-Mitoraj K (2018) Image quality evaluation in clinical research: a case study on brain and cardiac MRI images in multi-center clinical trials. IEEE Journal of Translational Engineering in Health and Medicine 6:1–15

    Article  Google Scholar 

  2. Esslinger D, Rapp P, Knödler L et al (2019) A novel finite element model–based navigation system–supported workflow for breast tumor excision. Med Biol Eng Compu 57:1537–1552. https://doi.org/10.1007/s11517-019-01977-0

    Article  Google Scholar 

  3. Zhao L et al (2017) Using anatomic magnetic resonance image information to enhance visualization and interpretation of functional images: a comparison of methods applied to clinical arterial spin labeling images. IEEE Trans Med Imaging 36(2):487–496

    Article  PubMed  Google Scholar 

  4. Singh M, Verma A, Sharma N (2018) Optimized multistable stochastic resonance for the enhancement of pituitary microadenoma in MRI. IEEE J Biomed Health Inform 22(3):862–873

    Article  PubMed  Google Scholar 

  5. Vivek U, Salim M (2021) Comparative study of compressive sensing approach in T1, T2, and proton density-based MRI images. IEIE Transact Smart Process Comput (3):219–226

  6. Vivek U, Salim M (2021) To analyse the effect of relaxation type on magnetic resonance image compression using compressive sensing. Int J Online Biomed Eng 17(4)

  7. Chen Z, Zhou Z, Adnan S (2021) Joint low-rank prior and difference of Gaussian filter for magnetic resonance image denoising. Med Biol Eng Compu 59:607–620. https://doi.org/10.1007/s11517-020-02312-8

    Article  CAS  Google Scholar 

  8. Chen L, Tang C, Xu M et al (2021) Enhancement and denoising method for low-quality MRI, CT images via the sequence decomposition Retinex model, and haze removal algorithm. Med Biol Eng Compu 59:2433–2448. https://doi.org/10.1007/s11517-021-02451-6

    Article  Google Scholar 

  9. Ferrari RJ (2013) Off-line determination of the optimal number of iterations of the robust anisotropic diffusion filter applied to denoising of brain MR images. Med Biol Eng Compu 51:71–88. https://doi.org/10.1007/s11517-012-0971-z

    Article  Google Scholar 

  10. Subudhi A, Jena S, Sabut S (2018) Delineation of the ischemic stroke lesion based on watershed and relative fuzzy connectedness in brain MRI. Med Biol Eng Compu 56:795–807. https://doi.org/10.1007/s11517-017-1726-7

    Article  Google Scholar 

  11. Simi VR, Edla DR, Joseph J (2021) A no-reference metric to assess quality of denoising for magnetic resonance images. Biomed Signal Process Control 70:102962. https://doi.org/10.1016/j.bspc.2021.102962

    Article  Google Scholar 

  12. Feichtenhofer C, Fassold H, Schallauer P (2013) A perceptual image sharpness metric based on local edge gradient analysis. IEEE Signal Process Lett 20(4):379–382

    Article  Google Scholar 

  13. Panetta K, Zhou Y, Agaian S, Jia H (2011) Nonlinear unsharp masking for mammogram enhancement. IEEE Trans Inf Technol Biomed 15(6):918–928

    Article  PubMed  Google Scholar 

  14. Li L, Wu D, Wu J, Li H, Lin W, Kot AC (2016) Image sharpness assessment by sparse representation. IEEE Trans Multimedia 18(6):1085–1097

    Article  Google Scholar 

  15. Hassen R, Wang Z, Salama MMA (2013) Image sharpness assessment based on local phase coherence. IEEE Trans Image Process 22(7):2798–2810

    Article  PubMed  Google Scholar 

  16. Ferzli R, Karam LJ (2009) A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB). IEEE Trans Image Process 18(4):717–728

    Article  PubMed  Google Scholar 

  17. Narvekar ND, Karam LJ (2011) A No-reference image blur metric based on the cumulative probability of blur detection (CPBD). IEEE Trans Image Process 20(9):2678–2683

    Article  PubMed  Google Scholar 

  18. Vu CT, Phan TD, Chandler DM (2012) S3: a spectral and spatial measure of local perceived sharpness in natural images. IEEE Trans Image Process 21(3):934–945

    Article  PubMed  Google Scholar 

  19. Simi VR, Edla DR, Joseph J (2021) An inverse mathematical technique for improving the sharpness of magnetic resonance images. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-03416-1

    Article  Google Scholar 

  20. Joseph J, Periyasamy R (2019) Nonlinear sharpening of MR images using a locally adaptive sharpness gain and a noise reduction parameter. Pattern Anal Appl 22:273–283. https://doi.org/10.1007/s10044-018-0763-7

    Article  Google Scholar 

  21. Anoop BN, Joseph J, Williams J et al (2018) A prospective case study of high boost, high frequency emphasis and two-way diffusion filters on MR images of glioblastoma multiforme. Australas Phys Eng Sci Med 41:415–427. https://doi.org/10.1007/s13246-018-0638-7

    Article  CAS  PubMed  Google Scholar 

  22. Renuka SV, Edla DR, Joseph J (2021) An objective measure for assessing the quality of contrast enhancement on magnetic resonance images. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2021.12.005

    Article  Google Scholar 

  23. Joseph J, Anoop BN, Williams J (2019) A modified unsharp masking with adaptive threshold and objectively defined amount based on saturation constraints. Multimedia Tools and Applications 78:11073–11089. https://doi.org/10.1007/s11042-018-6682-1

    Article  Google Scholar 

  24. Seshadrinathan K, Soundararajan R, Bovik AC, Cormack LK (2010) Study of Subjective and Objective Quality Assessment of Video. IEEE Trans Image Process 19(6):1427–1441

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, VIT Bhopal University, Bhopal, 466114, India

    Simi Venuji Renuka & Damodar Reddy Edla

  2. School of Bioengineering, VIT University, Bhopal, 466114, India

    Justin Joseph

Authors
  1. Simi Venuji Renuka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Damodar Reddy Edla
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Justin Joseph
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Simi Venuji Renuka.

Ethics declarations

Compliance with Ethical Standards

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Venuji Renuka, S., Edla, D.R. & Joseph, J. A customized acutance metric for quality control applications in MRI. Med Biol Eng Comput 60, 1511–1525 (2022). https://doi.org/10.1007/s11517-022-02547-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-022-02547-7

Keywords

Search

Navigation