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Curvelet based Rayleigh CLAHE Medical Image Enhancement

by Shefali Gupta, Sandeep Singh Kang
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 6
Year of Publication: 2018
Authors: Shefali Gupta, Sandeep Singh Kang
10.5120/ijca2018917554

Shefali Gupta, Sandeep Singh Kang . Curvelet based Rayleigh CLAHE Medical Image Enhancement. International Journal of Computer Applications. 182, 6 ( Jul 2018), 19-23. DOI=10.5120/ijca2018917554

@article{ 10.5120/ijca2018917554,
author = { Shefali Gupta, Sandeep Singh Kang },
title = { Curvelet based Rayleigh CLAHE Medical Image Enhancement },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2018 },
volume = { 182 },
number = { 6 },
month = { Jul },
year = { 2018 },
issn = { 0975-8887 },
pages = { 19-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number6/29766-2018917554/ },
doi = { 10.5120/ijca2018917554 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:10:34.218296+05:30
%A Shefali Gupta
%A Sandeep Singh Kang
%T Curvelet based Rayleigh CLAHE Medical Image Enhancement
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 6
%P 19-23
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image enhancement is one of the main issues in digital image processing. Image enhancement is done to obtain a high quality image. This makes output image better than original image. Images that are obtained from medical imaging systems are of low quality. This may happen because available range of possible gray levels may not be utilized properly. Therefore images may suffer from underexposure and overexposure problems. A new algorithm has been proposed in this paper to enhance such medical images. A comparison of existing image enhancement techniques with the proposed technique based on different performance parameters is presented. Experimental results show that proposed technique is better than various existing techniques.

References
  1. Gonzalez, R. C., & Woods, R. E. 2013. Digital Image Processing. NJ: Prentice Hall of India.
  2. Singh, S., Bansal, R. K., & Bansal, S. 2012. Comparative Study and Implementation of Image Processing Techniques Using MATLAB. International Journal of Advanced Research in Computer Science and Software Engineering, 2(3).
  3. Stark, J. A. 2000. Adaptive Image Contrast Enhancement Using Generalizations of Histogram Equalization. IEEE Transactions on Image Processing, 9(5), 889-896.
  4. Gupta, S., Kang, S. S. 2018. Image Enhancement of Medical Images using Curvelet and Rayleigh CLAHE. IOSR Journal of Computer Engineering, IOSR-JCE, 20(3), 69-80.
  5. Pizer, S. M., Amburn, E. P., Austin, J. D., Cromartie, R., Geselowitz, A., Greer, T., Romeny, B. H., Zimmerman, J. B., & Zuiderveld, K. 1987. Adaptive Histogram Equalization and Its Variations. Computer Vision Graphics and Image Processing, 39, 355–368.
  6. Pujiono, Pulung, N.A., Purnama, I. K. E., & Hariadi, M. 2013. Color Enhancement of Underwater Coral Reef Images using Contrast Limited Adaptive Histogram Equalization with Rayleigh Distribution. The Proceedings of The 7th ICTS.
  7. Kim, J. Y., Kim, L. S., & Hwang, S. H. 2001. An Advanced Contrast Enhancement using Partially Overlapped Sub-Block Histogram Equalization. IEEE Transactions on Circuits and Systems for Video Technology, 11(4), 475-484.
  8. Bhadauria, H. S., Dewal, M. L., & Anand, R. S. 2011. Comparative Analysis of Curvelet based techniques for denoising of Computed Tomography Images. IEEE.
  9. Suprijanto, G., Juliastuti, E., Azhari, & Epsilawati, L. 2012. Image Contrast Enhancement for Film-Based Dental Panoramic Radiography. International Conference on System Engineering and Technology, IEEE International Conference on.
  10. Nayak, R., Bhavsar, J., Chaudhari, J., & Mitra, S. K. 2012. Object tracking in Curvelet Domain with dominant Curvelet Subbands. International Journal of Engineering Research and Applications (IJERA), 2(3), 1219-1225.
  11. Bedi, S. S., & Khandelwal, R. 2013. Various Image Enhancement Techniques- A Critical Review. IJARCCE, 2(3), 1605-1609.
  12. Min, B. S., Lim, D. K., Kim, S. J., & Lee, J. H. 2013. A Novel Method of Determining Parameters of CLAHE Based on Image Entropy. International Journal of Software Engineering and Its Applications, 7(5), 113-120.
  13. Jintasuttisak, T., & Intajag, S. 2014. Color Retinal Image Enhancement by Rayleigh Contrast-Limited Adaptive Histogram Equalization. 14th International Conference on Control, Automation and Systems, ICROS, 692-697.
  14. Yadav, G., Maheshwari, S., & Agarwal, A. 2014. Contrast Limited Adaptive Histogram Equalization Based Enhancement For Real Time Video System. International Conference on Advances in Computing, Communications and Informatics, ICACCI, IEEE, 2392-2397.
  15. Sargun, & Rana, S. B. 2015. A Review of Medical Image Enhancement Techniques for Image Processing. International Journal of Current Engineering and Technology, 5(2), 1282-1286.
  16. Anjum, K., & Bhyri, C. 2015. Image Denoising Using Curvelet Transform and Edge Detection in Image Processing, Proceeding of NCRIET.
  17. Guo, Q., & Su, X. 2015. The study of medical image enhancement based on curvelet. Technology and Health Care 23, S319–S323.
  18. Lidong, H., Wei, Z., Jun, W., & Zebin, S. 2015. Combination of contrast limited adaptive histogram equalization and discrete wavelet transform for image enhancement. IET Image Processing, 9(10), 908–915.
  19. Namdeo, A., & Bhadoriya, S. S. 2016. A Review on Image Enhancement Techniques with its Advantages and Disadvantages. IJSART, 2(5), 171-182.
  20. Singh, S., & Pal, P. 2016. Contrast Enhancement of Medical Images: A Review. IJRDO-Journal of Health Sciences and Nursing, 1(4), 32-35.
  21. Joseph, J., Sivaraman, J., Periyasamy, R., & Simi,V. R. 2017. An objective method to identify optimum clip-limit and histogram specification of contrast limited adaptive histogram equalization for MR images. Biocybernetics and Biomedical Engineering, Science Direct, Elsevier, 489 – 497.
  22. Farzam, S., & Rastgarpour, M. 2017. An Image Enhancement Method Based on Curvelet Transform for CBCT Images. International Journal of Computer and Information Engineering, 11(6), 215-221.
  23. Fu, Q., Celenk, M., & Wu, A. 2018. An improved algorithm based on CLAHE for ultrasonic well logging image enhancement. Cluster Computing, Springer.
Index Terms

Computer Science
Information Sciences

Keywords

Image enhancement histogram equalization adaptive histogram equalization clahe curvelets.

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