Mammographic Feature Enhancement by Curvelet Transform

Abstract:

This paper presents an approach for digital mammograms enhancement using curvelet transform. The curvelet transform breaks the limitation of the wavelet transform and provides efficient representation of smooth objects with discontinuities along curves. It has special micro-local features which make them especially adapted to the contrast enhancement of mammographic features. A kind of nonlinear enhancing function is applied to enhance the details of mammogram according to the importance of the curvelet coefficients from different scales and angles. Results show the technique is potential to improve the accuracy of cancer breast detection.

• Periodical: Applied Mechanics and Materials (Volumes 48-49)
• Pages: 664-667
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.48-49.664
• Online since: February 2011
• Authors: Qin Xue, Zhuang Zhi Yan, Sheng Qian Wang
• Keywords: Curvelet Transform, Mammographic Contrast Enhancement, Non-Linear Function

References

[1] R. M. Rangayyan, F. J. Ayres, and J. E. L. Desautels, A review of computer-aided diagnosis of breast cancer: Toward the detection of early signs, J. Franklin Inst., vol. 344, no. 3–4, p.312–348, May (2007).

DOI: 10.1016/j.jfranklin.2006.09.003

[2] E. D. Pisano, Shuquan Zong, B. M. Hemminger, M. Deluca, R. E. Johnston, K. Muller, M. P. Braeuning, and S. M. Pizer, Contrast Limited Adaptive Histogram Equalization Image Processing to Improve the Detection of Simulated Spiculations in Dense Mammograms. Journal of Digital Imaging, Vol. 11, No. 4, 1998, pp.193-200.

DOI: 10.1007/bf03178082

[3] A. Laine, J. Fan and W. Yang, Wavelets for contrast enhancement of digital mammography, IEEE Eng. Med. Biol. Vol. 14, 1995, p.536–550.

DOI: 10.1109/51.464770

[4] A. Laine, W. Huda, B. G. Steinbach and J. C. Honeyman, Mammographic image processing using wavelet processing techniques, Eur. Radiol. Vol. 5, No. 5, 1995, p.18–23.

DOI: 10.1007/bf00208345

[5] Yuancheng Li, Qiu Yang and Runhai Jiao, Image compression scheme based on curvelet transform and support vector machine. Expert Systems with Applications, 37(4): 3063-3069, (2010).

DOI: 10.1016/j.eswa.2009.09.024

[6] E. J. Candes and D. L. Donoho, Curvelets-a surprisingly effective nonadaptive representation for objects with edges, in Curve and Surface Fitting, A. Cohen, C. Rabut, and L. L. Schumaker, Eds. Nashville, TN: Vanderbilt Uni. Press, (1999).

[7] E. J. Candes, L. Demanet, D. L. Donoho, and L, Ying, Fast discrete curvelet transforms, Applied and computational mathematics, California Institute of Technology, p.1–43, (2005).

[8] Jinshan Tang, Xiaoming Liu and Qingling Sun, A Direct Image Contrast Enhancement Algorithm in the Wavelet Domain for Screening Mammograms. IEEE Journal of Selected Topics in Signal Processing, Vol. 3, No. 1, 2009, pp.74-80.

DOI: 10.1109/jstsp.2008.2011108

[9] P. Sakellaropoulos, L. Costaridou and G. Panayiotakis, A wavelet-based spatially adaptive method for mammographic contrast enhancement, Physics in Medicine and Biology, 48(6), pp.787-803, Mar (2003).

DOI: 10.1088/0031-9155/48/6/307