Commenced in January 2007
Paper Count: 30054
An Adaptive Mammographic Image Enhancement in Orthogonal Polynomials Domain
Abstract:X-ray mammography is the most effective method for the early detection of breast diseases. However, the typical diagnostic signs such as microcalcifications and masses are difficult to detect because mammograms are of low-contrast and noisy. In this paper, a new algorithm for image denoising and enhancement in Orthogonal Polynomials Transformation (OPT) is proposed for radiologists to screen mammograms. In this method, a set of OPT edge coefficients are scaled to a new set by a scale factor called OPT scale factor. The new set of coefficients is then inverse transformed resulting in contrast improved image. Applications of the proposed method to mammograms with subtle lesions are shown. To validate the effectiveness of the proposed method, we compare the results to those obtained by the Histogram Equalization (HE) and the Unsharp Masking (UM) methods. Our preliminary results strongly suggest that the proposed method offers considerably improved enhancement capability over the HE and UM methods.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1063316Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
 S. A. Feig, "Decreased cancer mortality through mammographic screening: Results of clinical trials", Radiology, vol. 167, pp. 659-665, 1988.
 R. M. Rangayyan, L. Shen, Y. Shen, J. E. L. Desautels, H. Byrant, T. J. Terry, N. Horeczko, and M. S. Rose, "Improvement of sensitivity of breast cancer diagnosis with adaptive neighborhood contrast enhancement of mammograms", IEEE Trans. Inf. Technol. Biomed., vol. 1, no. 3, pp. 161-169, 1997.
 P. Sakellaropulos, L. Costaridou and G. Panayiotakis, "A wavelet-based spatially adaptive method for mammographic contrast enhancement," Physics in Medicine and Biology, vol. 48, no. 6, pp.787-803, 2003.
 A. Mencattini, M. Salmeri, R. Lojacono, M. Frigerio, and F. Caselli, "Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing", IEEE Trans. Inst. and Meas., vol. 57, no. 7, pp. 1422-1430, 2008.
 S. Dippel, M. Stahl, R. Wiemker, and T. Blaffert, "Multiscale Contrast Enhancement for Radiographies: Laplacian Pyramid Versus Fast Wavelet Transform", IEEE Trans. Med. Imaging, vol. 21, pp. 343 - 353, 2002.
 J. Scharcanski and C. Jung, "Denoising and enhancing digital mammographic images for visual screening", Comput Med Imaging Graph., vol. 30, no. 4, pp. 243-54, 2006.
 M. Malfait and D. Roose, "Wavelet based image denoising using a Markov Random Field a priori model", IEEE Trans. Image Processing, vol. 6, no. 4, pp. 549-565, 1997.
 H.D. Cheng and H. Xu, "A novel fuzzy logic approach to mammogram contrast enhancement", Information Sciences, vol. 148, pp. 167-184, 2002.
 A. Papadopoulos, D.I. Fotiadis and L. Costaridou, "Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques", Computers in Biology and Medicine, vol. 38, pp. 1045 - 1055, 2008.
 P. Heinlein, J. Drexl and W. Schneider, "Integrated wavelets for enhancement of microcalcifications in digital mammography", IEEE Trans. Medical Imaging, vol. 22, pp. 402-413, 2003.
 M. G. Linguraru, K. Marias, R. English and M. Brady, "A biologically inspired algorithm for microcalcification cluster detection", Medical Image Analysis, vol. 10, pp. 850-862, 2006.
 H. Li, K.J.U. Liu and S.C.B. Lo, Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms, IEEE Trans. Med. Imaging, vol. 16, no.6, pp. 785-798, 1997.
 M. P. Sampat, G. J. Whitman, A. C. Bovik and M. K. Markey, "Comparison of Algorithms to Enhance Spicules of Spiculated Masses on Mammography", Journal of Digital Imaging, vol. 21, no. 1, pp. 9- 17, 2008.
 A. R. Dominguez and A. K. Nandi, "Detection of masses in mammograms via statistically based enhancement, multilevelthresholding segmentation, and region selection", Comp. Med. Imaging and Graph., vol. 32 pp. 304-315, 2008.
 J. Tang and E. Peli, "An Image Enhancement Algorithm in JPEG Domain for Low-vision Patient", IEEE Transaction On Biomedical Engineering, vol. 51, no.11, pp. 2013- 2023, 2004.
 L. Ganesan and P. Bhattacharyya, "Edge Detection in Untextured and Textured Images-A Common Computational Framework", IEEE Trans. Syst. Man Cybern, vol. 27, no. 5 pp. 823-834, 1997.
 R. Krishnamoorthi, "Transform coding of monochrome images with a statistical design of experiments approach to separate noise", Pattern Recognition Letters, vol. 28 , pp. 771-777, 2007.
 M. S. Bartlett, "Properties of sufficiency and statistical tests", Proceedings of the Royal Statistical Society Series A, vol. 160, pp. 268- 282, 1937.
 J. Suckling, J. Parker, D. R. Dance, S. Astley, I. Hutt, C. R. M. Boggis, I. Ricketts, E. Stamatakis, N. Cerneaz, S. L. Kok, P. Taylor, D. Betal, and J. Savage, "The mammographic image analysis society mammogram database," in Proc. 2nd Int. Workshop Digital Mammography, York, U.K., pp. 375-378, 1994.
 R. M. Haralick, K. Shanmugan and I. Dinstein, "Textural features for image classification", IEEE Trans. On Systems, Man and Cybernetics, vol. 3, pp. 610-621, 1973.