Soft, Hard and Block Thresholding Techniques for Denoising of Mammogram Images

REFERENCES

• E. J. Candes, and D. L. Donoho, “Curvelets – a surprisingly effective nonadaptive representation for objects with edges,” in Curve and Surface Fitting: Saint-Malo 1999, A. Cohen, C. Rabut, and L. Schumaker, Eds. Nashville, TN: Vanderbilt University Press, 1999, pp. 1–10.
   Google Scholar
• E. Malar, A. Kandaswamy, S. S. Kirthana, and D. Nivedhitha, “A comparative study on mammographic image denoising technique using wavelet, curvelet and contourlet transforms,” in International Conference on Machine Vision and Image Processing (MVIP), Taipei, Dec. 14–15, 2012, pp. 65–8.
   Google Scholar
• V. Kidsumran, and W. Chiracharit, “Contrast enhancement mammograms using denoising in wavelet coefficients,” in 10th International Joint Conference on Computer Science and Software Engineering (JCSSE), Maha Sarakham, May 29–31, 2013, pp. 82–6.
   Google Scholar
• R. Strickland, and H. Hahn, “Wavelet transforms for detecting microcalcifications in mammogram,” IEEE Trans. Med. Imaging, Vol. 15, no. 2, pp. 218–29, Apr. 1996.
   PubMed Web of Science ®Google Scholar
• C. T. Wang, and B. N. Karayiannis, “Detection of microcalcification in digital mammograms using wavelets,” IEEE Trans. Med. Imaging, Vol. 17, no. 4, pp. 498–509, Aug. 1998.
   PubMed Web of Science ®Google Scholar
• S. M. H. Jamarani, G. Rezai-rad, and H. Behnam, “A novel method for breast cancer prognosis using wavelet packet based neural network,” in IEEE 27th Annual International Conference on Engineering in Medicine and Biology Society, Shanghai, Sep. 1–4, 2005, pp. 3414–7.
   Google Scholar
• P. Görgel, A. Sertbas, and O. N. Ucan, “Mammographical mass detection and classification using local seed region growing-spherical wavelet transform (LSRG-SWT) hybrid scheme,” Comput. Biol. Med., Vol. 43, no. 6, pp. 765–74, Mar. 2013.
   PubMed Web of Science ®Google Scholar
• V. Alarcon-Aquino, O. Starostenko, J. M. Ramirez-Cortes, R. Rosas-Romero, J. Rodriguez-Asomoza, O. J. Paz-Luna, and K. Vazquez-Munoz, “Detection of microcalcifications in digital mammograms using the dual-tree complex wavelet transform,” Eng. Intell. Syst., Vol. 17, no. 1, pp. 49–63, Mar. 2009.
   Web of Science ®Google Scholar
• M. M. Eltoukhy, I. Faye, and B. B. Samir, “A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram,” Comput. Biol. Med., Vol. 40, no. 4, pp. 384–91, Feb. 2010.
   PubMed Web of Science ®Google Scholar
• Hong-qiao, Li, S.-Q. Wang, and C.-Z. Deng, “ New image denoising method based wavelet and curvelet transform,” in WASE International Conference on Information Engineering, ICIE'09, Taiyuan, Jul. 10–11, 2009, Vol. 1, pp. 136–9.
   Google Scholar
• D.-Y. Tsai, E. Matsuyama, and H.-M. Chen, “Improving image quality in medical images using a combined method of undecimated wavelet transform and wavelet coefficient mapping,” Int. J. Biomed. Imaging, Vol. 2013, article id: 797924, 2013.
   PubMedGoogle Scholar
• E. Malar, A. Kandaswamy, S. S. Nivedhitha, and M. Gauthaam, “Curvelet image denoising of mammogram images,” Int. J. Med. Eng. Inform., Vol. 5, no. 1, pp. 60–7, Jan. 2013.
   Google Scholar
• F. E. Ali, I. M. El-Donaky, A. A. Saad, and F. E. Abd El-Samie, “Curvelet fusion of MR and CT images,” Prog. Electromagn. Res. C, Vol. 3, pp. 215–24, 2008.
   Google Scholar
• R. Cohen, (2012, February) “Signal denoising using wavelets,” Project Report, Department of Electrical Engineering, Technion, Israel Institute of Technology, Available: http://www.tx.technion.ac.il/∼rc/SignalDenoisingUsingWavelets_RamiCohen.pdf
   Google Scholar
• J. Ma, and G. Plonka, “Computing with curvelets: From image processing to turbulent flows,” Comput. Sci. Eng., Vol. 11, no. 2, pp. 72–80, Mar.–Apr. 2009.
   Web of Science ®Google Scholar
• P. Hall, G. Kerkyacharian, and D. Picard, “Block threshold rules for curve estimation using kernel and wavelet methods,” Ann. Stat., Vol. 26, no. 3, pp. 922–42, Jun. 1998.
   Web of Science ®Google Scholar
• P. Hall, G. Kerkyacharian, and D. Picard, “On the minimax optimality of block thresholded wavelet estimators,” Stat. Sin., Vol. 9, pp. 33–49, Jan. 1999.
   Web of Science ®Google Scholar
• T. Cai, “On block thresholding in wavelet regression: Adaptivity, block size, and threshold level,” Stat. Sin., Vol. 12, pp. 1241–73, Oct. 2002.
   Web of Science ®Google Scholar
• J. Suckling, J. Parker, D. R. Dance, S. M. 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 digital mammogram database,” in International Workshop on Digital Mammography, York, Jul. 10–12, 1994, pp. 211–21.
   Google Scholar
• N. Naveed, A. Hussain, M. J. Arfan, and TS Choi, “Quantum and impulse noise filtering from breast mammogram images,” Comput. Methods Programs Biomed., Vol. 108, no. 3, pp. 1062–9, Dec. 2012.
   PubMed Web of Science ®Google Scholar
• G. Peyre, “The numerical tours of signal processing–Advanced computational signal and image processing, ” IEEE Computing in Science and Engineering, Vol. 13, no. 4, pp. 94–7, Jul.–Aug. 2011.
   Web of Science ®Google Scholar