Journal of Applied Science and Engineering

Published by Tamkang University Press


Impact Factor



Hassan M. Qassim This email address is being protected from spambots. You need JavaScript enabled to view it.1, Nasseer M. Basheer1 and Mazin N. Farhan1

1Technical Engineering College, Mosul, Northern Technical University, Iraq


Received: October 9, 2018
Accepted: November 15, 2018
Publication Date: March 1, 2019

Download Citation: ||  


Enhancing dental x-ray images could be of great importance in helping the dentist for proper diagnosis. Image enhancement by histogram equalization, log transform, gamma correction for multiple gamma values, and contrast limited adaptive histogram equalization (CLAHE) were considered. Matlab 2015 was used to perform all the enhancement tasks. Dental x-rays images (periapical images) were obtained from a dataset published on the internet. It is preferred to preserve the x-ray images brightness so that the dentist can still use it for diagnosis with gray levels as close as possible to the originals. The CLAHE image enhancement, with eight rows and eight columns with clip limit of 0.01, proves to be the best. Two metrics were used for deciding the best enhancement, the entropy, looking for the highest obtained entropy, and the flatness of the resulted histogram. Eye inspection agrees with the metrics used.

Keywords: CLAHE, Dental X-ray Enhancement, Gamma Correction, Histogram Equalization, Image Entropy, Log Transform, Periapical Image


  1. [1] Ahmad, S., M. N. Taib, N. E. Abdulkhalid, and H. Taib et al. (2012) An analysis of image enhancement techniques for dental X-ray image interpretation, International Journal of Machine Learning and Computing 2(3), 292297. doi: 10.7763/IJMLC.2012.V2.133
  2. [2] Langland, O. E., R. P. Langlais, and J. W. Preece (2002) Principles of Dental Imaging, Lippincott Williams & Wilkins, ISBN-0781729653.
  3. [3] Menon, P., B. Rajeshwari (2016) Enhancement of Dental Digital X-ray Images Based on the Image Quality, Advances in Intelligent Systems and Computing 530. Springer International Publishing AG.
  4. [4] Kim, Y. T. (1997) Contrast enhancement using brightness preserving bi-histogram equalization, IEEE Transactions on Consumer Electronics 43(1), 18. doi: 10.1109/ 30.580378
  5. [5] Hossain, Md. F., M. R. Alsharif, and K. Yamashita (2010) Medical image enhancement based on non-linear technique and logarithmic transform coefficient histogram matching, International Conference on Complex Medical Engineering, IEEE/ICME. doi: 10.1109/ICCME.2010.5558871
  6. [6] Sundaram, M., K. Ramar, N. Arumugam, and G. Prabin (2011) Histogram based contrast enhancement for mammogram images, International Conference on Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), IEEE, India, 2122 July. doi: 10.1109/ICSCCN.2011.6024667
  7. [7] Sarage, G. N., and S. Jambhorkar (2012) Enhancement of chest X-ray images using filtering techniques, International Journal of Advanced Research in Computer Science and Software Engineering 2(5), 308312.
  8. [8] Ritika,and S.Kaur (2013) Contrast enhancement techniques for images – a visual analysis, International Journal of Computer Applications 64(17), 2025. doi:10.5120/10727-5679
  9. [9] Ikhsan, I., A. Hissain, M. A. Zulkifley, N. M. Tahir, and A. Mustapha (2014) An analysis of X-ray image enhancement methods for vertebral bone segmentation, International Colloquium on Signal Processing & Its Applications (CSPA), IEEE, Malasia, 79 March. doi: 10.1109/CSPA.2014.6805749
  10. [10] Datta, N. S., P. Saha, H. S. Dutta, D. Sarkar, S. Biswas, and P. Sarkar (2015) Anew contrast enhancement method of retinal images in diabetic screening system, International Conference on Recent Trends in Information Systems (ReTIS), IEEE, India, 911 July. doi:10.1109/ReTIS.2015.7232887
  11. [11] Mehdizadeh, M., and S. Dolatyar (2009) Study of effect of adaptive histogram equalization on image quality in digital preapical image in pre apex area, Research Journal of Biological Science 4(8), 922924.
  12. [12] Wang, C., and Z. Ye (2005) Brightness preserving histogram equalization with maximum entropy: a variational perspective, IEEE Transactions on Consumer Electronics 51(4), 13261334. doi: 10.1109/TCE.2005.1561863
  13. [13] Pratt, W. K. (2001) Digital Image Processing, Third Edition. John Wiley & Sons, Inc. ISBN-0471374075.
  14. [14] Lim, S. H., and N. A. Mat Isa (2013) Anew histogram equalization method for digital image enhancement and brightness preservation, Springer-Verlag London, 9(3), 675689. doi: 10.1007/s11760-013-0500-z
  15. [15] Yoon, H., Y. Han, and H. Hahn (2009) Image contrast enhancement based sub-histogram equalization technique without over-equalization noise, International Journal of Electrical and Computer Engineering 3(2), 189195.
  16. [16] Manikpuri, U., and Y. Yadav (2014) Image enhancement through logarithmic transformation, International Journal of Innovative Research in Advanced Engineering (IJIRAE) 1(8), 357362.
  17. [17] Hossain, Md. F., and M. R. Alsharif (2007) Image enhancement based on logarithmic transform coefficient and adaptive histogram equalization, International Conference on Convergence Information Technology, South Korea, 2123 Novemeber.
  18. [18] Solomon, C., and T. Breckon (2011) Fundamentals of Digital Image Processing, John Wiley & Sons, Ltd, ISBN-9780470844724.
  19. [19] Singh, B. B., and S. Patel (2017) Efficient medical image enhancement using CLAHE enhancement and wavelet fusion, International Journal of Computer Applications 167(5), 15. doi: 10.5120/ijca2017913277
  20. [20] Reza, A. M. (2004) Realization of the contrast limited adaptive histogram equalization (CLAHE) for real time image enhancement, Journal of VLSI Signal Processing 38, 35–44. doi: 10.1023/B:VLSI.0000028532.53893.82
  21. [21] Gonzalez, R. C., R. E. Woods, and S. L. Eddins (2009) Digital Image Processing Using MATLAB, Second Edition. Gatesmark Publishing, ISBN-0982085400.
  22. [22] Rad, A. E., M. S. Mohd Rahim, A. Rehman, and T. Saba (2016) Digital Dental X-ray Database for Caries Screening,Research Center, Kwangwoon University and Springer-Verlag Berlin Heidelberg, published online.
  23. [23] Min, B. S. (2013) A novel method of determining parameters of CLAHE based on image entropy, International Journal of Software Engineering and Its Applications 7(5), 113120. doi: 10.14257/ijseia.2013.7.5.11



60th percentile
Powered by  Scopus

SCImago Journal & Country Rank

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.