An Unsupervised Method for Suspicious Regions Detection in Mammogram Images

Marco Insalaco, Alessandro Bruno, Alfonso Farruggia, Salvatore Vitabile, Edoardo Ardizzone

Abstract

Over the past years many researchers proposed biomedical imaging methods for computer-aided detection and classification of suspicious regions in mammograms. Mammogram interpretation is performed by radiologists by visual inspection. The large volume of mammograms to be analyzed makes such readings labour intensive and often inaccurate. For this purpose, in this paper we propose a new unsupervised method to automatically detect suspicious regions in mammogram images. The method consists mainly of two steps: preprocessing; feature extraction and selection. Preprocessing steps allow to separate background region from the breast profile region. In greater detail, gray levels mapping transform and histogram specifications are used to enhance the visual representation of mammogram details. Then, local keypoints and descriptors such as SURF have been extracted in breast profile region. The extracted keypoints are filtered by proper parameters tuning to detect suspicious regions. The results, in terms of sensitivity and confidence interval are very encouraging.

References

  1. Doi, K., 2007. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized medical imaging and graphics.
  2. Wolfe, N., 1976. Risk for breast cancer development determined by mammographic parenchimal pattern. Cancer.
  3. Cheng, H. D., Shi, X. J., Min, R., Hu, L. M., Cai, X. P., Du, H. N., 2005. Approaches for automated detection and classification of masses in mammograms. Pattern Recognition, Elsevier.
  4. Kom, G., Tiedeu, A., Kom, M., 2005. Automated detection of masses in mammograms by local adaptive thresholding. Computers in Biology and Medicine. Elsevier.
  5. te Brake, G. M., Karssemeijer, N., Hendriks, J. H., 1998. Automated detection of breast carcinomas not detected in a screening program. Radiology. Elsevier.
  6. Petrick, N., Chan, H. P., Sahiner, B., Wei, D., 1996. An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection. IEEE Transaction Medical Imaging. IEEE.
  7. Gupta, R., Undrill, P. E., 1995. The use of texture analysis to identify suspicious masses in mammography. Phys. Med. Bio.
  8. Viton, J. L., Rasigni, M. R. G., Llebaria, A., 1996. Method for characterizing masses in digital mammograms. Opt. Eng.
  9. Li, H., Wang, Y., Ray Liu, K. J., Shih-Chung, B. L., Freedman, M. T., 2001. Computerized radiographic mass detection. Part I-II: lesion site selection by morphological enhancement and contextual segmentation. IEEE Transaction Image Processing. IEEE.
  10. Highnam, R., Brady, M. 1999. Mammographic Image Analysis. Kluwer Academic Publishers.
  11. Tourassi, G. D., Vargas-Voracek, R.. 2003. Computerassisted detection of mammographic masses: a template matching scheme based on mutual information. Med. Phys.
  12. Rogova, G. L., Ke, C., Acharya, R., Stomper, P., 1999. Feature Choice for detection of cancerous masses by constrained optimization. In SPIE Conference on Image Processing.
  13. Sameti, M., Ward, R. K., 1996 A fuzzy segmentation algorithm for mammogram partitioning. Digital Mammography. Elsevier.
  14. Zheng, B., Chang, Y. H., Wang, X. H., Good, W. F., 1999. Comparison of artificial neural network and Bayesian belief network in a computer assisted diagnosis scheme for mammography. In IEEE International conference on Neural Network.
  15. Sahiner, B., Chan, H. P., Petrick, N., Helvie, M. A., Goodsitt, M. M., 1998. Desing of high-sensitivity classifier based on a genetic algorithm: application to computer aided diagnosis. Phys. Med. Bio.
  16. Constantinidis, A. S., Fairhust, M. C., Rahman, A. F. R., 2001. A new multi-expert decision combination algorithm and its application to the detection of circumscribed masses in digital mammograms. Pattern Recognition.
  17. Cascio, D., Fauci, F., Magro, R., Raso, G., Bellotti, R., De Carlo, F., Tangaro, S., De Nunzio, G,. Quarta, M., Forni, G., others. 2006. Mammogram Segmentation by Contour Searching and Mass Lesions Classification With Neural Network. IEEE Transaction on Nuclear Science. IEEE.
  18. Domìnguez, A. R., Nandi, A. K., 2008. Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection. Computerized Medical Imaging and Graphics. Elsevier.
  19. Choi, J. Y., Ro, Y. M.. 2012. Multiresolution local binary pattern texture analysis combined with variable selection for application to false-positive reduction in computer-aided detection of breast masses on mammograms. Physics in Medicine and Biology. Iop Publishing.
  20. Oliver, A., Freixenet, J., Perez, E., Pont, J., Denton, E. R. E., Zwiggelar, R.. 2010. A review of automatic mass detection and segmentation in mammographic masses. Med. Image Analysis.
  21. Muramatsu, C., Nishimura, K., Endo, T., Oiwa, M., Shiraiwa, M., Doi, K., Fujita, H., 2013. Representation of lesions similarity by use of Multidimensional Scaling for Breast Masses on Mammograms. Digit Imaging. Springer.
  22. Natarajan, P., Ghosh, D., Sandeep, K. N., Jilani, S., 2013. Detection of Tumor in Mammogram Images using Extended Local Minima Threshold. IJET International Journal of Engineering and Technology.
  23. Alias, A., Paulchamy, B.. 2014. Detection of Breast Cancer using artifical neural network. International Journal of Innovative Research in Science.
  24. Bay, H., Tuytelaars, T., Van Gool, L., 2008. Surf: Speeded up robust features. Computer vision and image understanding. Elsevier.
  25. Farruggia, A., Magro, R., Vitabile, S., 2014. A text based indexing system for mammographic image retrieval and classification. Future Generation Computer Systems. Elsevier.
  26. Kekre, H. B., Sarode, Tanuja, K., Gharge, Saylee M., 2009. Tumor Detection in mammography images using vector quantization technique. International Journal of Intellingent Information Technology Application.
  27. Lau, T. K., Bischof, W. F., 1991. Automated detection of breast tumors using the asymmetry approach. Computers and biomedical research. Elsevier.
Download


Paper Citation


in Harvard Style

Insalaco M., Bruno A., Farruggia A., Vitabile S. and Ardizzone E. (2015). An Unsupervised Method for Suspicious Regions Detection in Mammogram Images . In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-758-077-2, pages 302-308. DOI: 10.5220/0005277103020308


in Bibtex Style

@conference{icpram15,
author={Marco Insalaco and Alessandro Bruno and Alfonso Farruggia and Salvatore Vitabile and Edoardo Ardizzone},
title={An Unsupervised Method for Suspicious Regions Detection in Mammogram Images},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2015},
pages={302-308},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005277103020308},
isbn={978-989-758-077-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - An Unsupervised Method for Suspicious Regions Detection in Mammogram Images
SN - 978-989-758-077-2
AU - Insalaco M.
AU - Bruno A.
AU - Farruggia A.
AU - Vitabile S.
AU - Ardizzone E.
PY - 2015
SP - 302
EP - 308
DO - 10.5220/0005277103020308