Skip to main content
Log in

Enhancement of MRI images of hamstring avulsion injury using histogram based techniques

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

Abstract

A novel histogram based image enhancement technique is introduced to visualize the image more effectively. The proposed method uses hamstring avulsion injury Magnetic Resonance Imaging (MRI) images from the database. First, the image is clipped using the histogram. Second, the image is subdivided into eight sub-images and enhanced individually until a better enhancement rate is maintained to obtain the final output of the proposed method. The proposed method shows effective enhancement for clear visualization of the injury. The strength of the proposed method is compared with different histogram based enhancement techniques based on the parameters such as F-measure, Contrast improvement index (CII), Absolute Mean Brightness Error (AMBE) and Peak Signal to Noise Ratio (PSNR) to determine the efficient enhancement technique. The parameters are defined to be significant for different enhancement techniques based on the statistical analysis. Further classification of the enhancement techniques are performed with the help of decision tree classifier. Based on the results of the classifier, the proposed algorithm is stated to be more significant and efficient in enhancing the region of interest in the Hamstring Avulsion Injury MRI images. Thus the proposed method shows effective enhancement for improved visualization of the hamstring injury for the diagnosis of the state of injury. With these results, the region of injury can be analysed effectively for further processing.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Abbas AH, Hussain L (2017) Identification the Best Histogram Techniques for Brain MRI Image Enhancement Depend on Different Quality Matrices. Iraqi Journal of Information Technology. 7(3):67–85

    Google Scholar 

  2. Benjamin-Laing H (2012) Hamstring Avulsion Injuries. Ann R Coll Surg Engl. 94(6):192–197

    Article  Google Scholar 

  3. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and Regression Trees. Chapman & Hall, Boca Raton

    MATH  Google Scholar 

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

    Article  Google Scholar 

  5. Chen SD, Ramli AR (2003) Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans. Consumer Electronics. 49(4):1310–1319

    Article  Google Scholar 

  6. Chen SD, Ramli AR (2004) Preserving brightness in histogram equalization based contrast enhancement techniques. Digital Signal Processing. 14(5):413–428

    Article  Google Scholar 

  7. Chen G, Zhang P, Wu Y, Shen D, Yap PT (2016) Denoising Magnetic Resonance Images Using Collaborative Non-Local Means. Neurocomputing. 177:215–227. https://doi.org/10.1016/j.neucom.2015.11.031

    Article  Google Scholar 

  8. Chen G, Dong B, Zhang Y, Lin W, Shen D, Yap PT (2019) Denoising of Diffusion MRI Data via Graph Framelet Matching in x-q Space. IEEE Trans Med Imaging. 38(12):2838–2848. doi: https://doi.org/10.1109/TMI.2019.2915629. Epub 2019 May 8.

  9. Guanche CA (2015) Hamstring injuries. Journal of Hip Preservation Surgery. 2(2):116–122

    Article  Google Scholar 

  10. Hemalatha RJ, Vijayabaskar V, Thamizhvani TR (2018) Performance Evaluation of Contour based segmentation methods for Ultrasound images. Advances in Multimedia. Article ID 4976372, 8 pages. https://doi.org/10.1155/2018/4976372

  11. Ibrahim H, Kong NSP (2007) Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement. IEEE Transactions on Consumer Electronics. 53(4):1752–1758

    Article  Google Scholar 

  12. Ibrahim H, Kong NSP (2009) Image sharpening using sub-regions histogram equalization. IEEE Transactions on Consumer Electronics. 55(2):891–895

    Article  Google Scholar 

  13. Kim YT (1997) Contrast enhancement using brightness preserving Bi-histogram equalization. IEEE Trans. consumer Electron. 43:1–8

    Article  Google Scholar 

  14. Lee CH, Chen LH, Wang WK (2012) Image contrast enhancement using classified virtual exposure image fusion. IEEE Trans. Consumer Electron. 58:1253–1261

    Article  Google Scholar 

  15. Liao X, Yin J j, Guo S, Li X, Sangaiah AK (2018) Medical JPEG image steganography based on preserving inter-block dependencies. Computers & Electrical Engineering. 67:320–329

    Article  Google Scholar 

  16. Meier J, Bock R, Michelson G, Nyúl LG, Hornegger J (2007) Effects of Preprocessing Eye Fundus Images on Appearance Based Glaucoma Classification. In: Kropatsch W.G., Kampel M., Hanbury A. (eds) Computer Analysis of Images and Patterns. CAIP 2007. Lecture Notes in Computer Science, 4673. Pp 165–172.

  17. Ooi C, Sia Pik Kong N, Ibrahim H (2009) Bi-Histogram Equalization with a Plateau Limit for Digital Image Enhancement. Consumer Electronics. IEEE Transactions on 55(4):2072–2080. https://doi.org/10.1109/TCE.2009.5373771

    Article  Google Scholar 

  18. Pizer SM (1987) Adaptive histogram equalization and its variations. Comput. Vis. Graph and Image Process. 39:355–368

    Article  Google Scholar 

  19. Pizer SM, Amburn EP, Austin JD (1987) Adaptive histogram equalization and its variations. Computer Vision Graphics Image and Processing. 39:355–368

    Article  Google Scholar 

  20. Raju G, Nair MS (2014) A fast and effective color image enhancement method based on fuzzy-logic and histogram. International Journal of Electronics and Communications 68(3):237–243

    Article  Google Scholar 

  21. Reza AM (2004) Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement. Journal of VLSI signal processing systems for signal, image and video technology. 38(1):35–44

    Article  Google Scholar 

  22. Shijin Kumar PS, Dharun VS (2016) An efficient skull stripping algorithm using connected regions and morphological operation. ARPN Journal of Engineering and Applied Sciences. 11(7):4305–4309

    Google Scholar 

  23. Sim KS, Tso CP, Tan YY (2007) Recursive sub-image histogram equalization applied to grayscale images. Pattern Recognition Letters. 28(10):1209–1221

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Singh K, Kapoor R (2014) Image Enhancement via Median-Mean Based Sub-Image-Clipped Histogram Equalization. Optik-International Journal for Light Electron Optics 125(17):4646–4651

    Article  Google Scholar 

  26. Slavotinek JP (2002) Hamstring Injury in Athletes: Using MR Imaging Measurements to Compare Extent of Muscle Injury with Amount of Time Lost from Competition. American Journal of Roentgenology. 179(6):1621–1628

    Article  Google Scholar 

  27. Tang JR, Isa NAM (2014) Adaptive Image Enhancement based on Bi-Histogram Equalization with a clipping limit. Computers & Electrical Engineering. 40(8):86–103

    Article  Google Scholar 

  28. Thamizhvani TR, Tanveer Ahmed KF (2018) Analysis of MRI slices of Hamstring Avulsion Injury using Histogram. Journal of Clinical and Diagnostic Research. 12(12): KC01-KC04.

  29. Wan Y, Chen Q, Zhang BM (1999) Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Trans. Consumer Electronics. 45:68–75

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to T. R. Thamizhvani.

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

Thamizhvani, T.R., Ahmed, K.F.T., Hemalatha, R.J. et al. Enhancement of MRI images of hamstring avulsion injury using histogram based techniques. Multimed Tools Appl 80, 12117–12134 (2021). https://doi.org/10.1007/s11042-020-10459-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-10459-7

Keywords

Navigation