We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Robust Approach for Medical Image Enhancement using DTCWT

by Gagandeep Kaur, Shruti Vashist
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 167 - Number 6
Year of Publication: 2017
Authors: Gagandeep Kaur, Shruti Vashist
10.5120/ijca2017914316

Gagandeep Kaur, Shruti Vashist . A Robust Approach for Medical Image Enhancement using DTCWT. International Journal of Computer Applications. 167, 6 ( Jun 2017), 26-29. DOI=10.5120/ijca2017914316

@article{ 10.5120/ijca2017914316,
author = { Gagandeep Kaur, Shruti Vashist },
title = { A Robust Approach for Medical Image Enhancement using DTCWT },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 167 },
number = { 6 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 26-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume167/number6/27777-2017914316/ },
doi = { 10.5120/ijca2017914316 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:14:08.059708+05:30
%A Gagandeep Kaur
%A Shruti Vashist
%T A Robust Approach for Medical Image Enhancement using DTCWT
%J International Journal of Computer Applications
%@ 0975-8887
%V 167
%N 6
%P 26-29
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image Enhancement is one of the most important features in Image processing domain whose main target is to improve with the help of which contrast and the visual appearance of an image can be improved that makes the input image more suitable than the original one for specific application. Medical Image enhancement is the active research field. A novel approach to enhance resolution of medical images using Dual tree complex transform, a filter and singular value decomposition (SVD) has been discussed. Here, SVD is proposed to enhance the contrast of an image while high frequency sub bands are produced using dual-tree complex wavelet transform. Filter is used to remove the artefacts produced by dual-tree complex wavelet transform. Interpolated filtered high frequency sub-bands and contrast enhanced low resolution image are combined using inverse dual-tree complex wavelet transform to obtain contrast enhanced image of super resolution. Qualitative and quantitative measures are used to justify the importance of proposed technique.

References
  1. A K Jain, ‘Fundamentals of Digital Image Processing’, Prentice Hall, NJ, 1990.
  2. R. C. Gonzalez, R. E. Woods, “Digital Image Processing using Matlab”, 2nd ed., Prentice Hall.
  3. Mallat, S.G.: ‘Multifrequency channel decompositions of image and wavelet models’, IEEE Trans. Acoust. Speech Signal Process., 1989, 37, (12), pp. 2091–2110.
  4. Lu, J., Healy, D.M., and Weaver, J.B.: „Contrast enhancement of medical images using multi-scale edge repressentation‟, Opt. Eng., 1994, 33, (7), pp. 2151– 2161.
  5. Yang, G. , and Hansell, D. M. : 'CT image enhancement with wavelet analysis for the detection of small airways disease', IEEE Trans. Med. Imaging, 1997, 16, (6), pp. 953–961.
  6. Fang, Y., and Qi, F.: „A method of wavelet image enhancement based on soft threshold‟, Comput. Eng. Appl., 2002, 23, pp. 16–19.
  7. Zhou, X., Zhou, S., Huang, F., and Zhou, X.T.: „New algorithm of image enhancement based on wavelet transform‟, Comput. Appl., 2005, 25, (3),pp. 606–608.
  8. W.L. Lee, C.C. Yang, H.T. Wu, M.J. Chen, Wavelet-based interpolation scheme for resolution enhancement of medical images, J. Signal Process. Syst. 55 (2009) pp.251–265.
  9. M.Z. Iqbal, A. Ghafoor, A.M. Siddiqui, Wavelet transform and non local means based super resolution, in: International Symposium.
  10. H. Demirel, C. Ozcinar, G. Anbarjafari, Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition, IEEE Geosci. Remote Sens. Lett. 7 (April (2)) (2010).
  11. Y. Tian, T. Tan, Y. Wang, Y. Fang, Do singular values contain adequate information for face recognition, Pattern Recognit. 36 (3) (2003) pp. 649–655.
  12. H. Demirel, G. Anbarjafari, M.N.S. Jahromi, Image equalization based on singular value decomposition, in: IEEE International Symposium on Computer and Information Sciences, October 27–29, 2008, pp. 1–5.
  13. H. Demirel, G. Anbarjafari, M.N.S. Jahromi, Image equalization based on singular value decomposition, in: IEEE International Symposium on Computer and Information Sciences, October 27–29, 2008, pp. 1–5.
  14. Y. Yang, Z. Su, L. Sun, Medical image enhancement algorithm based on wavelet transform, Electron. Lett. 46 (January (2)) (2010).
  15. J.L. Starck, F. Murtagh, J.M. Fadili, Sparse Image and Signal ProcesQ5 sing: Wavelets, Curvelets, Morphological Diversity, Cambridge University Press, 2010.
  16. L. Shao, R. Yan, X. Li, Y. Lin, From heuristic optimization to dictionary learning: a review and comprehensive comparison of image denoising algorithm, IEEE Trans. Cybern. (2013).
  17. Khare, A., Khare, M., Jeong, Y., Kim, H., and Jeon, M.: ‘Despeckling ofmedical ultrasound images using daubechies complex wavelet transform’, Signal Process., 2010, pp. 428–439.
  18. Nick Kingsbury,”Shift invariant properties of the dual tree Complex Wavelet Transform” IEEE Trans. On signal processing letters, 1999.
Index Terms

Computer Science
Information Sciences

Keywords

Image Enhancement DTCWT SVD MSE MAE PSNR

Powered by PhDFocusTM