The week's pick
Reseach Article
Robust Contrast Enhancement for Digital Mammography
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 56 - Number 9 |
| Year of Publication: 2012 |
| Authors: Norh`ene Gargouri Ben Ayed, Alima Damak Masmoudi, Dorra Sellami Masmoudi |
10.5120/8918-2976
|
Norh`ene Gargouri Ben Ayed, Alima Damak Masmoudi, Dorra Sellami Masmoudi . Robust Contrast Enhancement for Digital Mammography. International Journal of Computer Applications. 56, 9 ( October 2012), 15-19. DOI=10.5120/8918-2976
Abstract
Nowadays, mammography is currently considered as the most efficient way for the detection and diagnosis of breast cancer at early stages. In many case, due to the subtleness of the difference between normal features and cancerous ones and the bad imaging conditions, cancer is not easily detected with visual interpretation. Thus, image-enhancement technology is often used in screaming mammograms. In this paper, we develop a method based on Shock Filter to enhance the contrast of image and help radiologists. In the proposed method, the Shock Filter is applied for preprocessing, in order to improve the contrast, remove the noisy fluctuations and to enhance the edges containing useful information. Experiments show the efficiency of the proposed method.
References
- J. Tang, R. Rangayyan, Y. Yang, I. El Naqa, and J. Xu, "Computer Aided Breast Cancer Detection and Diagnosis Using Mammography: Recent Advance", IEEE Transactions on Information Technology in Biomedicine, 2009.
- H. Yoshida, K. Doi. , R. Nishikawa, M. Giger and R. Schmidt, "An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms", Acad. Radiol, 1996.
- Jain, A. K. 1989 Fundamentals of Digital Image Processing. Englewood Cliffs.
- E. D. Pisano, E. B. Cole, and B. M. H. e. al. , "Image Processing Algorithms for Digital Mammography: A Pictorial Essay. ", Radiographics, 2000.
- M. N. Do and M. Vetterli, "The contourlet transform: an efficient directional multiresolution image representation", IEEE Trans Image Process, 2005.
- A. L. Da Cunha, J. Zhou, and M. N. Do, "The Nonsubsampled Contourlet Transform: Theory, Design, and Applications", IEEE Trans. Image Process, 2005.
- S. M. Pizer, J. B. Zimmerman, and E. Staab, "Adaptive grey level assignment in CT scan display", Journal of Computer Assistant Tomography, 1984.
- Pizer, S. M. , Amburn, E. P. , Austin, J. D. , Cromartie, R. , Geselowitz, A. , Greer, T. , Romeny, B. T. H. , and Zimmerman, J. B. , 1987. Adaptive Histogram Equalization and Its Variations, Computer Vision, Graphics, and Image Processing.
- Rehm, k. , and Dallas, W. J. , 1989. Artifact Suppression in Digital Chest Radiographs Enhanced with Adaptive Histogram Equalization. presented at SPIE: Medical Imaging III.
- S. Osher and L. Rudin, "Feature-oriented image enhancement using shock filters", SIAM Journal on numerical Analysis, 1990.
- S. Bettahar, and A. B. Stambouli, "Shock filter coupled to curvature diffusion for image denoising and Sharpening", Journal of image vision and computing, 2008.
- Suganthi, M. , and Madheswaran. 2009. Mammogram image enhancement and denoising using shock filters. Proceedings of International Conference on Advanced Communication and Informatics.
- Suckling, J. , Parker, J. , Dance, D. R. , Astley, s. , Hutt, I. , Boggis, C. , I. Ricketts, Stamatakis,E. , Cerneaz, N. and Kok, S. L. 1994. The Mammographic Image Analysis Society digital mammogram database. Digital Mammography.
- R. M. Haralick. "Statistical and structural approachs to texture", Proceedings of the IEEE in Proceedings of the IEEE, 1979.
- A. Baraldi and F. Parmiggiani. "An investigation of the textural characteristics associated with gray level occuence matrix statistical parameters", IEEE Transactions on Geoscience and Remote Sensing, 1995.
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