CFP last date
20 August 2024
Reseach Article

Chest Radiograph Image Enhancement: A Total Variation Approach

by Matilda Wilson, Anthony Y. Aidoo, Charles H. Acquah, Peter A. Yirenkyi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 163 - Number 7
Year of Publication: 2017
Authors: Matilda Wilson, Anthony Y. Aidoo, Charles H. Acquah, Peter A. Yirenkyi

Matilda Wilson, Anthony Y. Aidoo, Charles H. Acquah, Peter A. Yirenkyi . Chest Radiograph Image Enhancement: A Total Variation Approach. International Journal of Computer Applications. 163, 7 ( Apr 2017), 1-7. DOI=10.5120/ijca2017913466

@article{ 10.5120/ijca2017913466,
author = { Matilda Wilson, Anthony Y. Aidoo, Charles H. Acquah, Peter A. Yirenkyi },
title = { Chest Radiograph Image Enhancement: A Total Variation Approach },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 163 },
number = { 7 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2017913466 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:09:30.087862+05:30
%A Matilda Wilson
%A Anthony Y. Aidoo
%A Charles H. Acquah
%A Peter A. Yirenkyi
%T Chest Radiograph Image Enhancement: A Total Variation Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 163
%N 7
%P 1-7
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

Wavelet denoising of medical images relies on the technique of thresholding. A disadvantage of this method is that even though it adequately removes noise in an image, it introduces unwanted artifacts into the image near discontinuities due to Gibbs phenomenon. A total variation method for enhancing chest radiographs is implemented. The approach focuses on lung nodules detection using chest radiographs (CRs) and the method achieves high image sensitivity and could reduce the average number of false positives radiologists encounter.

  1. International agency for research on cancer, 2012.
  2. Murphy, G. P., 1995. American Cancer Society Textbook of Clinical Oncology 2nd ed., Atlanta.
  3. Sarage, G. N., Jambjorkor, S., 2012. Enhancement of chest xray images using filtering techniques. International J of Adv. Res. in Comp. Sci. and Soft. Eng. 2(5), 308-312.
  4. Kwan, B. Y. and Kwan, H. K., 2011. Improved lung nodule visualization on chest radiographs using digital filtering and contrast enhancement. World Acad. Sci. Tech. 110:590-593.
  5. Saleh, S. H., Nordin, A. J., 2011. Improving diagnostic viewing of medical images using enhancement algorithms. J. Comp. Sci., 7, 1831-1838.
  6. Yoon, W. and Song, J. W., 2007. Image contrast enhancement based on the generalized histogram. Elect. Imag, 16(3), 033005 (August, 2007).
  7. Tsai, D.-Y., Lee, Y., and Chiba, R., 2005. An improved adaptive neighborhood contrast enhancement method for medical images, Proc. IASTED Int. Conf. Biom. Eng., 59-63.
  8. Ito, K. and Xiong, K., 2000. Gaussian filters for nonlinear filtering problems. Automatic Control, IEEE Transactions on, 45(5), 910-927.
  9. Tomasi, C. and Manduchi, R., Bilateral filtering for gray and color images. In Computer Vision, Sixth International Conference On, 1998. p. 839-846.
  10. Kurt, B., Nabiyew V. Turhan K., 2012. Medical images enhancement by using anisotropic filter and clahe. In Innovations in Intelligent Systems and Applications (INISTA), International Symposium On, (2012), 1-4.
  11. Tsai, C.-Y., An adaptive rank-ordered median image filter for removing salt-and-pepper noise. Master’s thesis, National Cheng-Kung University, 2006.
  12. H. Hwang and R. A. Haddad, Adaptive median filters: new algorithms and results. Image Processing, IEEE Transactions on, 1995. 4(4): p. 499-502.
  13. Yang, Y., Su, Z., and Sun, L., 2010. Medical image enhancement algorithm based on wavelet transform, ieeeexplore, 46(2), 120-121.
  14. Wang, Y. and Zhou, H., 2006. Total Variation Wavelet Based Medical Image Denoising, Int. J. Biom. Img., 2006, 1-6.
  15. Wang, Y., Yang, J., Yin, W., and Zhang, Y., 2008. A new alternating minimization algorithm for total variation image reconstruction, SIAM J. Imag. Sci, 1(3), 248-272.
  16. Mejia-Lavalle, M., Ortiz, E., Mujica, D., Ruiz, J., and Gerardo Reyes, G., 2016, An effective image de-noising alternative approach based on third generation neural networks, In Pattern Recognition, MCPR 2016, Lecture Notes in Computer Science 9703, 64-73.
  17. Vese, L. A. and Osher, S. J., Oscillating Patterns in Image Processing, Journal of Scientific Computing, 19, 553-572.
  18. Selesnick, I. W. and Bayram, I_, 2010. Total Variation Filtering, htpp://
  19. Chambolle, A., and Lions, P. L., 1997. Image Recovery Via Total Variation Minimization and Related Problems, Numer. Math., 76, 167-188.
  20. Meyer, Y., 2001. Oscillating Patterns in Image Processing and Nonlinear Evolution Equations, University Lecture Series, Vol 22, Amer. Math. Society. De-noising Alternative Approach Based on Third Generation Neural Networks, Pattern Recognition, V. 1907 of the series Lecture Notes in Computer Science, 2016, 64-73.
  21. Selesnick, I. 2012. Total Variation Denoising (An MM Approach), http:/
  22. Casellas, V., 2006. Total Variation Based Image Denoising and Testoration, Proceedings of the International Congress of Mathematics, Madrid, Spain.
  23. Rudin, L., Osher, S. J., and Fatemi, E., 1992. Nonlinear total variation based noise removal algorithms. Physica D., 60:259,
  24. Vese, L., 2001. A Study in the Bounded Variation Space of Denoising-deblurring Variational Problem, Applied Mathematics and optimization, 2001, 44(2): 131-161.
  25. Chan, T. F., Shen, J. J., 2005. Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods., volume 473. Society for Industrial and Applied Mathematics (SIAM).
  26. Chambolle, A., Caselles, V., Cremers D., and Pock, T., 2010. An introduction to total variation for image analysis, Theoretical Foundations and Numerical Methods for Sparse Recovery, 9, 263-340.
  27. Ikezoe, S., Matsumoto, J., Kobayashi, T., Komatsu, T., Matsui, K. I., Fujita, M., Kodera, H., Doi, Y., Shiraishi, K., and Katsuragawa, J., 2000. Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristics analysis of radiologists detection of pulmonary nodules. American Journal of Roentgenology, 174,71-74.
  28. Wei, J., Hagihara, Y., Shimizu, A., and Kobatake, A., 2002. Optimal image feature set for detecting lung nodules on chest x-ray images. Computer Assisted Radiology and Surgery Springer, Paris, 2002, pp. 706711.
  29. Coppini, G., Diciotti, S., and Falchini, M., 2003. Neural networks for computer-aided diagnosis: Detection of lung nodules in chest radiograms, IEEE Trans. Inf. Technol. Biomed., 7(4), 344-357
  30. Schilham, A. M., van Ginneken, B., and Loog, M., 2006. A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database, Med. Image Anal, 10(2), 247258.
  31. Hardie, R. C., Rojers, S. K., Wilson, T., Rogers, A., 2008. Performance analysis of a new computer aided detection system for identifying lung nodules on chest radiographs, Med. Image Anal. 12(3), 240258
  32. Chen, S., Suzuki, K., and MacMahon, H., 2011. Development and evaluation of a computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two-stage nodule enhancement with support vector classification, Med. Phys., 38(4):1844-58, A.
Index Terms

Computer Science
Information Sciences


Total Variation Chest Radiograph Algorithm Convolution Denoising