References
[1]. N. Riyahi Alam, F. Younesi, M. S. Riyahi Alam (2009).
'Computer-Aided Mass Detection on Digitized
Mammograms using a Novel Hybrid Segmentation
System', International Journal of Biology and Biomedical
Engineering, Volume 3, Issue 4, 51.
[2]. B.Sridhar, Dr. K.V.V.S.Reddy. Efficient computer aided
system based on mathematical morphology and higher
order partial differential equations for breast cancer
detection, CNC2013 234-239.
[3]. Jelena Bozek, Kresimir Delac, Mislav Grgic (2008).
Computer-Aided Detection and Diagnosis of Breast Abnormalities in Digital Mammography, 50th International Symposium ELMAR-2008, 10-12, Zadar, Croatia.
[4]. T. Matsubaraa,, T. Ichikawab, T. Harab, H. Fujitab, S.
Kasaic, T. Endod, T. Iwasee (2003). ' Automated detection
methods for architectural distortions around skinline and within mammary gland on mammograms, International
Congress Series, Vol. 1256, pp 950–955.
[5]. Liu, S.,(1999). The Analysis of Digital Mammograms:
Speculated Tumor Detection and Normal Mammogram
Characterization, in School of Electrical and Computer
Engineering, Purdue University.
[6]. Rashed, E.A. and M.G. Awad, (2006). Neural networks
approach for mammography diagnosis using wavelets
features, in First Canadian Student Conference on
Biomedical Computing.
[7]. Qian, W., L. Li, and L. Clarke, (1999). Image feature
extraction for mass detection in digital mammography:
influence of wavelet analysis. Med Phys,. 26(3): p. 402-
408.
[8]. Chen, C. and G. Lee, (1997). Image segmentation
using multi resolution wavelet analysis and expectationmaximization
(EM) algorithm for digital mammography.
Int J Imaging Syst Technol,. 8(5): p. 491-504.
[9]. Wang, T. and N. Karayiannis, (1998). Detection of
micro calcifications in digital mammograms using
wavelets. IEEE Trans Med Imaging,. 17(4): p. 498-509.
[10]. Kim, J.K. and H.W. Park, (1999). Statistical textural
features for detection of microcalci"cations in digitized
mammograms. IEEE Trans. Med. Imag.,18(3): p. 231-238.
[11]. Lefebvre, F., H. Benali, and E. Kahn, (1992). Fractal
analysis of clustered micro calcifications in
mammograms. Acta Stereol,.11: p. 611-616.
[12]. Haralick, R.M. (1979). Statistical and structural
approaches to texture. In Proceedings of the IEEE. Vol. 67,
No. 5.
[13]. Kim, J.K. and H.W. Park, (1999). Statistical textural
features for detection of micro calcinations in digitized
mammograms. IEEE Trans. Med. Image.,18(3): p. 231-
238.
[14]. Polakowski, W., et al.,(1997). Computer-aided
breast cancer detection and diagnosis of masses using
difference of Gaussians and derivative based feature
saliency. IEEE Trans Med Imaging,.16(6): p. 811-819.
[15]. Buyue Zhang, Jan P. Allebach (2008). Adaptive
Bilateral Filter for Sharpness Enhancement and Noise Removal, IEEE Transactions on Image Processing, Vol. 17,
No. 5.
[16]. Alexander Wong, (2008). Adaptive bilateral filtering
of image signals using local phase characteristics,
Elsevier Signal Processing, 88, 1615–1619
[17]. Jinshan Tang, Shengwen Guo, Qingling Sun,
Youping Deng, Dongfeng Zhou (2009). Speckle noise
reduction by bilateral filter for cattle follicle segmentation.
The 2009 International Conference on Bioinformatics &
Computational Biology (BioComp 2009) Las Vegas, NV,
USA. 13-16.
[18]. Hyung W., Kang Charles K., Chui Uday K.
Chakraborty (2006). A unified scheme for adaptive
stroke-based Rendering, Visual Computer, 22: 814–824
[19]. Xiaoping Lin (2009). Research and Application of
Mathematical Morphology Algorithms on OSSC, IEEEOSSC 173.
[20]. Joseph M. Reinhardt and William E., (1996). Higgins
Efficient Morphological Shape Representation, IEEE
Transactions on Image Processing, Vol. 5, No. 1, 89.
[21]. Tomasz Arod´za, (2006). Detection of clustered
micro calcifications in small field digital mammography,
Computer methods and programs in biomedicine, 81
56–65.
[22]. Rafael C. Gonzalez, Richard E. Woods and Steven L.
Eddins (2004). Digital Image Processing using MATLAB.
Pearson Education. ISBN 978-81-7758-898-9
[23]. http://www.mammoimage.org/databases/
[24]. Jinsan Tang (2009). A multi-direction GVF snake for
the segmentation of skin cancer images, ELSEVIER Pattern
recognition, Vol: 42, 1172-1179.
