Skip to main content
SpringerLink
Log in

A fast and effective method for enhancement of contrast resolution properties in medical images

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In medical imaging, the poor quality image specifically the low contrast image may deliver inadequate data for the visual interpretation of affected portions. Hence, a combined approach called exposure based contrast limited bi-histogram equalization method is proposed to improve the visual quality of medical images. The proposed method has three stages: At first, input histogram is sub-divided into two histograms based on the exposure threshold to preserve mean brightness and strengthen the fine details. Then, the two sub histograms are clipped to limit the contrast amplification and a new dynamic range is assigned to each clipped sub-histogram by its exposure threshold value. At last, a contrast-enhanced image is gained by equalizing each clipped sub-histogram individually. Experiments were conducted on a wide variety of medical images to evaluate the performance of proposed method both qualitatively and quantitatively. Extensive quantitative measures show that the proposed technique achieves better performance in terms of peak signal to noise ratio, entropy, contrast ratio, enhancement measures and computational complexity when compared to state of art enhancement methods. The proposed algorithm improves contrast while preserving brightness and visual quality. The proposed method provides better quality for disease examination and diagnosis.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

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

References

  1. Abdullah-Al-Wadud M, Ali Akber Dewan M, Hasanul Kabir M, Chae O (2007) A dynamic histogram equalization for image contrast enhancement. IEEE Trans Consum Electron 53(2):593–600

    Article  Google Scholar 

  2. Al-Ameen Z, Sulong G, Rehman A, Al-Dhelaan A, Saba T, Al-Rodhaan M (2015) An innovative technique for contrast enhancement of computed tomography images using normalized gamma-corrected contrast-limited adaptive histogram equalization. EURASIP J Adv Signal Process 2015:32. https://doi.org/10.1186/s13634-015-0214-1

    Article  Google Scholar 

  3. Aquino-Morínigo PB et al (2017) Bi-histogram equalization using two plateau limits. Signal Image Vid Process 11(5):857–864

    Article  Google Scholar 

  4. Benson CC, Lajish VL, Morphology based enhancement and skull stripping of MRI brain images, Proceedings – 2014 International Conference on Intelligent Computing Applications. (2014) 254–257.

  5. Chaira T (2014) An improved medical image enhancement scheme using Type II fuzzy set. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2014.09.004

  6. Chen S, Ramli R (2003) Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans Consum Electron 49(4):1310–1319

    Article  Google Scholar 

  7. Chen SD, Ramli R (2003) Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Trans Consum Electron 49(4):1301–1309

    Article  Google Scholar 

  8. Gambino O, Daidone E, Sciortino M, Pirrone R, Ardizzone E, Automatic skull stripping in MRI based on morphological filters and fuzzy c-means segmentation, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. (2011) 5040–5043.

  9. He D, Deng W, Sun X, Liu M, Ye C, Zhou X (2017) Mammogram enhancement using intuitionistic fuzzy sets. IEEE Trans Biomed Eng 64(8):1803–1814

    Article  Google Scholar 

  10. Huang S-C, Cheng F-C, Chiu Y-S (2013) Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE Trans Image Process 22(3):1032–1041

    Article  MathSciNet  Google Scholar 

  11. Ibrahim H, Kong N (2007) Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Trans Consum Electron 53(4):1752–1758

    Article  Google Scholar 

  12. Kim Y-T (1997) Contrast enhancement using brightness preserving bi histogram equalization. IEEE Trans Consum Electron 43(1):1–8

    Article  MathSciNet  Google Scholar 

  13. Magudeeswaran V, Singh JF (2017) Contrast limited fuzzy adaptive histogram equalization for enhancement of brain images. Int J Imaging Syst Technol 27(1):98–103

    Article  Google Scholar 

  14. Oak PV, Kamathe PRS (2013) Contrast enhancement of brain MRI images using histogram based techniques. Int J Innov Res Electric Electron Instrum Control Eng 1(3):90–94

    Google Scholar 

  15. Ooi CH, Kong NSP, Ibrahim H (2009) Bi-histogram equalization with a plateau limit for digital image enhancement. IEEE Trans Consum Electron 55(4):2072–2080

    Article  Google Scholar 

  16. Pizer SM, Amburm EP, Austin JD, Cromartie R, Geselowitz A, Greer T, Romeny BTH, Zimmerman JB (1987) Adaptive histogram equalization and its variations. Comp Vis Graph Image Process 39(3):355–368

    Article  Google Scholar 

  17. RuiTang J, Isa NAM (2017) Bi-histogram equalization using modified histogram bins. Appl Soft Comput 55:31–43

    Article  Google Scholar 

  18. Satheesh S, Santosh Kumar RT, Prasad KVSVR, Jitender Reddy K (2011) Skull removal of noisy magnetic resonance brain images using contour let transform and morphological operations, Proceedings of 2011 International Conference on Computer Science and Network Technology 4: 2627–2631.

  19. Sengee N, Choi H (2008) Brightness preserving weight clustering histogram equalization. IEEE Trans Consum Electron 54(3):1329–1337

    Article  Google Scholar 

  20. Shakeri M, Dezfoulian MH, Khotanlou H, Barati AH, Masoumi Y (2017) Image contrast enhancement using fuzzy clustering with adaptive cluster parameter and sub-histogram equalization. Digital Signal Process 62:224–237

    Article  Google Scholar 

  21. Singh K, Kapoor R (2014) Image enhancement using exposure based sub image histogram equalization. Pattern Recogn Lett 36(15):10–14

    Article  Google Scholar 

  22. Singh K, Rajiv Kapoor B (2014) Image enhancement via Median-Mean Based Sub-Image Clipped Histogram Equalization. Optik 125:4646–4651

    Article  Google Scholar 

  23. Tang JR, Isa NAM (2014) Adaptive Image Enhancement based on Bi-Histogram Equalization with a clipping limit. Comput Electr Eng 40(8):86–103

    Article  Google Scholar 

  24. Wang Y, Pan Z (2017) Image contrast enhancement using adjacent-blocks-based modification for local histogram equalization. Infrared Phys Technol 86:59–65

    Article  Google Scholar 

  25. Wang Y, Pan Z (2017) An effective histogram modification scheme for image contrast enhancement. Signal Process Image Commun 58:187–198

    Article  Google Scholar 

  26. Wang Y, Zhang B (1999) Image enhancement based on equal area dualistic sub image histogram equalization method. IEEE Trans Consum Electron 45(1):68–75

    Article  Google Scholar 

  27. Yu H, He F, Pan Y (2018) A novel region-based active contour model via local patch similarity measure for image segmentation. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-5697-y

  28. Yu H, He F, Pan Y (2018) A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-6735-5

  29. Zhaoa C, Wanga Z, Lia H, Wua X, Qiaoa S, Sunb J (2019) A new approach for medical image enhancement based onluminance-level modulation and gradient modulation. Biomed Signal Process Control 48:189–196

    Article  Google Scholar 

  30. Zhu Y, Huang C (2012) An adaptive histogram equalization algorithm on the image gray level mapping. Phys Procedia 25:601–608

    Article  Google Scholar 

Download references

Acknowledgements

This work was encouraged by PSNA College of Engineering and Technology, Dindigul, India. The authors thank our experts P.B. Barani Kumar, Shriram Varadharajan and the reviewers for their comments on the manuscript.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, Tamilnadu, 624622, India

    Bharath Subramani & Magudeeswaran Veluchamy

Authors
  1. Bharath Subramani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Magudeeswaran Veluchamy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bharath Subramani.

Ethics declarations

Declarations of interest

The authors declare that there is no conflict of interest for publication of this manuscript.

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

Subramani, B., Veluchamy, M. A fast and effective method for enhancement of contrast resolution properties in medical images. Multimed Tools Appl 79, 7837–7855 (2020). https://doi.org/10.1007/s11042-019-08521-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08521-0

Keywords

Search

Navigation