Segmentation of Region of Interest and Mass Auto Detection in Mammograms Based on Wavelet Transform Modulus Maximum
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N. B. C. S. P. W. site. (2005). [Online]. Available: www.cancerscreening. nhs.uk/breastscreen
T. N. C. I. W. site. (2005). [Online]. Available: www.cancer.gov
C. H. Yip, N. A. M. Taib and I. Mohamed, “Epidemiology of breast cancer in Malaysia,” Asian Pacific Journal of Cancer Prevention, vol. 7(3), 2006, pp. 369-374.
National Breast Cancer Foundation, U.S.A. [Online].Availble:http://www.nationalbreastcancer.org/early_detection/index.html
N. F. Boyd, J. W. Byng, R. A. Long, E. K. Fishell, L. E. Little, A. B. Miller, G. A. Lockwood, D. L. Tritchler and M. J. Yaffe, “Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening study,” J. Nat. Cancer Inst., vol. 87(9), 1995, pp. 670–675, doi:10.1093/jnci/87.9.670.
D. Cascio, F. Fauci , Fauci, R. Magro, G. Raso, R. Bellotti, F. De Carlo, Mammogram Segmentation by Contour Searching and Massive Lesion Classification with Neural Network, Nuclear Science Symposium Conference Record, 2004:2695-2699
R. Zwiggelaar, T. C. Parr, J. E. Schumm, I. W. Hutt, C. J. Taylor, S. M. Asley, and C. R. M. Boggis, “Model-based detection of speculated lesions in mammograms,” Med. Image Anal., vol. 3, no. 1, pp. 39–62, 1999.
X. Zhang, Desaimd. Segmentation of bright targets using wavelets and adaptive shareholding. IEEE Trans. on Image Processing, 2001, 10 (7): 1020-1030.
A. Mencattini, G. Rabottino, M. Salmeri, R.Lojacono, E. Colini, Breast mass segmentation in Mammographic Images by an effective region growing algorithm, Lecture Notes in Computer Science, 2008, 24 (7): 948-957
H.D.Li, M.Kallergi, L.P.Clarke, et al.Markov random field for tumor detection in digital mammography. IEEE Trans.Med.Imag.1995.14 (3): 565-576.
D. Guliato, R. M.Rangayyan, W. A.Carnielli, et al. Segmentation of breast tumors in mammograms by fuzzy region growing. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.1998.20 (2):1002-1005.
X.P. Zhang, M. D. Desai, Division of Engineering: Wavelet Based automatic thresholding for image segmentation, In Proc. of ICIP'97, 1997. 22: 17-19.
B. Sahiner, N. Petrick, H.P. Chan, et al., Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization, IEEE Transaction on Medical Imaging, 2001, 20(12): 1275-1284
S. Timp, N. Karssemeijer, A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography, Medical Physics, 2004, 31: 958-971.
A.H.Baydush, D.M.Catarious, C.K.Abbey, C.E. Floyd, Computer aided detection of masses in mammography using subregion Hotelling observers, Medical Physics, 2003, 30: 1781-1787.
G.D. Tourassi, R. Vargas-Voracek, D.M. Catarious Jr, C.E. Floyd Jr, Computer-assisted detection of mammographic masses: A template matching scheme based on mutual information, Medical Physics, 2003, 30 (8): 2123-2130.
D. L. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inf. Theory, vol. 41, no. 3, pp. 613–627, May 1995.
J. Fan and A. Laine, “Contrast enhancement by multiscale and nonlinear operators,” Wavelets in Medicine and Biology. Boca Raton, FL: CRC, Mar. 1996, pp. 163–192.
S. Mallat, S. Zhong. Characterization of signal from multiscale edge. IEEE Trans PAM I, 1992, (7): 710-732.
X.P. Luo, J. Tian, Y. Lin. An algorithm for segmentation of medical image series based on active contour model. Journal of Software, 2002, 13(6): 1050-1059.
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