Features Extraction and Fuzzy Logic based Classification for False Positives Reduction in Mammographic Images

Arianna Mencattini, Giulia Rabottino, Marcello Salmeri, Roberto Lojacono, Eleonora Tamilia

2011

Abstract

Breast cancer is one of the most common neoplasms in women and it is a leading cause of death worldwide. A proper screening procedure can help an early diagnosis of the tumor so reducing the death risk. A suitable computer aided detection system can help the radiologist to detect many subtle signs, normally missed during the screening phase, submitting to the radiologist’s attention several regions that could contain an abnormality. However, one of the most critical problem deals with a suitable tradeoff regarding the number of suspicious zones to present to the radiologist and the capability of identify the correct ones. In this work, the classification of suspicious signs into normal tissue or massive lesion has been faced in order to get a False Positive reduction without noticeably affecting the number of True Positives.

References

  1. M. Bazzocchi and F. Mazzarella, “CAD systems for mammography: a real opportunity? A review of the literature,” http://www.springerlink.com/content/x3157r8u72196h45/ fulltext.pdf/, 2006.
  2. A. Oliver, “A new approach to the classification of mammographic masses and normal breast tissue,” in Proceedings of the 18th International Conference on Pattern Recognition, 2006, vol. 4, pp. 707 - 710.
  3. J. Wei, H-P. Chan, B. Sahiner, C. Zhou, and L. M. Hadjiiski, “Computer-aided detection of breast masses on mammograms: Dual system approach with two-view analysis,” Med. Phys., vol. 36, no. 10, pp. 4451 - 4460, 2009.
  4. C. Balleyguier, S. Ayadi, K. Van Nguyen, D. Vanel, C. Dromain, and R. Sigal, “BIRADS classification in mammography,” European Journal of Radiology, vol. 61, pp. 192-194, 2007.
  5. University of South Florida, “DDSM: Digital database for screening mammography,” http://marathon.csee.usf.edu/Mammography/Database.html, 2000.
  6. A. Mencattini, G. Rabottino, M. Salmeri, and R. Lojacono, “Assessment of a breast masses identification procedure using an iris detector,” IEEE Transactions on Instrumentation and Measurement, in press.
  7. K. I. Laws, “Laws' texture measures,” http://www.ccs3.lanl.gov/ kelly/ZTRANSITION/ notebook/laws.shtml, 2001.
  8. R. M. Haralick, “Statistical and structural approaches to texture,” Proceedings of the IEEE, vol. 67, no. 5, pp. 786 - 804, 1979.
  9. M. Masotti, N. Lanconelli, and R. Campanini, “Computer aided mass detection in mammography: False positive reduction via gray scale invariant ranklet texture features,” Medical Physics, vol. 36, no. 2, 2009.
  10. F. Smeraldi, “Ranklets: Orientation selective non-parametric features applied to face detection,” in 16th International Conference on Pattern Recognition, 2002, vol. 3, pp. 351 - 359.
  11. E. Rakus-Andersson, Fuzzy and rough techniques in Medical Diagnosis and Medication, Springer-Verlag, 2007.
  12. L. Li, Y. Zheng, L. Zhang, and R. A. Clark, “False-positive reduction in cad mass detection using a competitive classification strategy,” Medical Physics, vol. 28, 2001.
  13. E. Angelini, R. Campanini, and A. Riccardi, “Support vector regression filtering for reduction of false positives in a mass detection cad scheme: Preliminary results,” http://amsacta.cib.unibo.it/archive/00000912/01/angelini05support.pdf, 2005.
  14. G. D. Tourassi, R. Vargas-Vorecek, D. M. Catarious, and C. E. Floyd, “Computer assisted detection of mammographic masses: a template matching scheme based on mutual information,” Medical Physics, vol. 30, no. 8, pp. 2123 - 2130, 2003.
  15. C. Varela, P. G. Tahoces, A. J. Mendez, M. Souto, and J. J. Vidal, “Computerized detection of breast masses in mammograms,” Computers in Biology and Medicine, vol. 37, pp. 214 - 226, 2007.
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Paper Citation


in Harvard Style

Mencattini A., Rabottino G., Salmeri M., Lojacono R. and Tamilia E. (2011). Features Extraction and Fuzzy Logic based Classification for False Positives Reduction in Mammographic Images . In Proceedings of the 2nd International Workshop on Medical Image Analysis and Description for Diagnosis Systems - Volume 1: MIAD, (BIOSTEC 2011) ISBN 978-989-8425-38-6, pages 13-25. DOI: 10.5220/0003303500130025


in Bibtex Style

@conference{miad11,
author={Arianna Mencattini and Giulia Rabottino and Marcello Salmeri and Roberto Lojacono and Eleonora Tamilia},
title={Features Extraction and Fuzzy Logic based Classification for False Positives Reduction in Mammographic Images},
booktitle={Proceedings of the 2nd International Workshop on Medical Image Analysis and Description for Diagnosis Systems - Volume 1: MIAD, (BIOSTEC 2011)},
year={2011},
pages={13-25},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003303500130025},
isbn={978-989-8425-38-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Workshop on Medical Image Analysis and Description for Diagnosis Systems - Volume 1: MIAD, (BIOSTEC 2011)
TI - Features Extraction and Fuzzy Logic based Classification for False Positives Reduction in Mammographic Images
SN - 978-989-8425-38-6
AU - Mencattini A.
AU - Rabottino G.
AU - Salmeri M.
AU - Lojacono R.
AU - Tamilia E.
PY - 2011
SP - 13
EP - 25
DO - 10.5220/0003303500130025