Automatic Segmentation and Classification of Masses from Digital Mammograms

Authors

  • Basma A. Mohamed Faculty of Engineering, Biomedical Engineering Department, Helwan University, Cairo, Egypt
  • Nancy M Salem Faculty of Engineering, Biomedical Engineering Department, Helwan University, Cairo, Egypt
  • Marwa M Hadhoud Faculty of Engineering, Biomedical Engineering Department, Helwan University, Cairo, Egypt
  • Ahmed F Seddik Faculty of Computer Science, Nahda University (NUB)

DOI:

https://doi.org/10.14738/aivp.44.2151

Keywords:

Breast Cancer, Digital Mammograms

References

(1) Breast Cancer Foundation of Egypt (BCFE), http://www.bcfe.org/en/index.php, 2014.

(2) Verma, B., P. McLeod, and A. Klevansky, A novel soft cluster neural network for the classification of suspicious areas in digital mammograms, Pattern Recognition, 2009. 42 (9): p. 1845-1852.

(3) Heath, M., et al., The digital database for screening mammography, Proceedings of the International Workshop on Digital Mammography 2000. p. 212-218.

(4) ACR, Breast imaging reporting and data system (BI-RADS), Breast Imaging Atlas, 4th ed., American College of Radiology, Reston, VA, 2010.

(6) Tzikopoulosa, S., et al., A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry, Computers Methods and Programs in Biomedicine, 2011. (102): p. 47-63.

(8) Nunes, A., A. Silva, and A. Paiva, Detection of masses in mammographic images using geometry, Simpson's Diversity Index and SVM, Journal of Signal and Imaging Systems Engineering, 2010. 3(1): p. 43-51.

(9) Retico, A., et al., An automatic system to discriminate malignant from benign massive lesions on mammograms, Nuclear Instrumentation and Methods in Physics Research, 2006. 569(2): p. 596-600.

(10) B. Surendiran and A. Vadivel, Classifying mammographic masses into BI-RADSTM shape categories using various geometric and shape features, International Journal of Biomedical Signal Processing, 2011. 2(1): p. 43-47.

(11) Vadivel, A., and B. Surendiran, A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories, Computers in Biology and Medicine, 2013. 43: p. 259-267.

(12) Costa, D., et al., Independent component analysis in breast tissues mammograms images classification using LDA and SVM, Information technology Application in Biomedicine, 2007. p. 231-234.

(13) De Nazar Silva, J., et al., Automatic detection of masses in mammograms using quality threshold clustering, correlogram function, and SVM, Journal of Digital Imaging, 2015. 28 (3): p. 323-373.

(14) Women Health Care Program, http://www.whop.gov.eg, 2007.

(15) Heath, M., K. Bowyer, and D. Kopans, Current status of the digital database for screening mammography, Digital Mammography, Kluwer Academic Publishers: p. 457-460.

(16) Otsu, N., A threshold selection method from gray-level histograms, Systems, Man, and Cybernetics, IEEE Transactions on, 1979. 9(1): p. 62-66.

(17) Gonzalez, R., R. Woods, and S. Eddins, Digital image processing using MATLAB, Gatesmark Publishing; 2nd edition, 2009.

(18) Schalkoff, R., Artificial neural networks. McGraw Hill, Publishers,

p. 146-118.

Altman, N. S., An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 1992. 46(3): p. 175-185.

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Published

2016-09-14

How to Cite

Mohamed, B. A., Salem, N. M., Hadhoud, M. M., & Seddik, A. F. (2016). Automatic Segmentation and Classification of Masses from Digital Mammograms. European Journal of Applied Sciences, 4(4), 17. https://doi.org/10.14738/aivp.44.2151

Issue

Vol. 4 No. 4 (2016): European Journal of Applied Sciences

Section

Articles