Edge Weighted Local Texture Features for the Categorization of Mammographic Masses-Reference-Cited by-同舟云学术

Edge Weighted Local Texture Features for the Categorization of Mammographic Masses

Published:2017-10-04 Issue:3 Volume:38 Page:457-468
ISSN:1609-0985
Container-title:Journal of Medical and Biological Engineering
language:en
Short-container-title:J. Med. Biol. Eng.

Author:

Midya Abhishek,Rabidas Rinku,Sadhu Anup,Chakraborty Jayasree^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Biomedical Engineering,General Medicine

Link

http://link.springer.com/article/10.1007/s40846-017-0316-3/fulltext.html

Reference49 articles.

1. Oliver, A., Freixenet, J., Mart, J., Prez, E., Pont, J., Denton, E. R., et al. (2010). A review of automatic mass detection and segmentation in mammographic images. Medical Image Analysis, 14(2), 87–110.

2. American Cancer Society (ACS): Global Cancer Facts and Figures. http://www.cancer.org/acs/groups/content/@research/documents/document/acspc-044738.pdf (3rd Edition 2012) Accessed 15 June 2015.

3. Calas, M. J. G., Gutfilen, B., & de Albuquerque Pereira, W. C. (2012). CAD and mamography: why use this tool? Radiologia Brasileira, 45, 46–52.

4. Chakraborty, J., Mukhopadhyay, S., Singla, V., Khandelwal, N., & Rangayyan, R. M. (2012). Detection of masses in mammograms using region growing controlled by multilevel thresholding. In 25th IEEE international symposium on computer-based medical systems (CBMS) (pp 1–6).

5. Homer, M. J. (1991). Mammographic interpretation: a practical approach (2nd ed.). New York: McGraw-Hill.

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