Breast Masses Classification using a Sparse Representation

Fabián Narváez, Andrea Rueda, Eduardo Romero

2011

Abstract

Breast mass detection and classification in mammograms is considered a very difficult task in medical image analysis. In this paper, we present a novel approach for classification of masses in digital mammograms according with their severity (benign or malign). Unlike other approaches, we do not segment masses but instead, we attempt to describe entire regions of interest (RoIs) based on a sparse representation. A set of patches selected by a radiologist in a RoI are characterized by their projection onto learned dictionaries, constructed previously from classified regions. Finally, the region class was identified using a decision rule algorithm. The strategy was assessed in a set of 80 masses with different shapes extracted from the DDSM database. The classification was compared with a ground truth already provided in the data base, showing an average accuracy rate of 70%.

References

  1. American Cancer Society: American Cancer Statistics. (2007) Updated: September 2, 2008.
  2. American College of Radiology (ACR): Illustrated Breast Imaging Reporting and Data System (BI-RADS). ACR (1998)
  3. R. Bird, T. Wallace, and B. Yankaskas, Analysis of cancers missed at screening mammography, Radiology 178 (1992), 234-247.
  4. S. Buseman, J. Mouchawar, N. Calonge, and T. Byers., Mammography screening matters for young women with breast carcinoma., Cancer 97 (2003), 352-358.
  5. A. M. Bruckstein, D. L. Donoho, and M. Elad., From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images., SIAM Review 51 (2009), 34-81.
  6. H. D. Cheng, X. J. Shi, R. Min, L. M. Hu, X. P. Cai, H. N. Du. Approaches for automated detection and classification of masses in mammograms., Pattern Recognition 39 (2006), 646- 668
  7. D. Gur, J. S Stalder, L. A. Hardesty, B. Zheng, J. H. Sumkin, D. M Chough, B. E. Shindel, and H. E. Rockette, Computer-aided detection performance in mammographic examination of masses: assessment., Radiology 233 (2004), 418-423.
  8. M. Heath, K. Bowyer, D. Kopans, R. Moore, and W. P. Kegelmeyer, The digital database for screening mammography, in Proceedings of the Fifth International Workshop on Digital Mammography, Medical Physics Publishing M.J. Yaffe, ed (2001), 212-218.
  9. J. Herredsvela, K. Engan, T. O. Gulsrud, and K. Skretting, Detection of masses in mammograms by watershed segmentation and sparse representation using learned dictionaries. (paper in pdf-format), Proceedings NORSIG (2005), 35-40.
  10. H. Kim and J. Kim, Region-based shape descriptor invariant to rotation, scale and translation., Signal Proc.: Image Communication 16 (2000), 87-93.
  11. S. G. Mallat and Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on signal processing 41 (1993), no. 12, 3397-3415.
  12. R. M. Nishikawa, Current status and future directions of computer-aided diagnosis in mammography, Computerized Medical Imaging and Graphics 31 (2007), 224-235.
  13. F. Narvaez, G. Diaz, E. Romero, Automatic BI-RADS description for mammographic masses, IWDM2010 Digital Mammographhy, LNCS 6136 (2010), 673-681.
  14. B. A. Olshausen, Principles of image representation in visual cortex, pp. 1603-1615, MIT Press, 2003.
  15. N. A. Rosa, J. C. Felipe, A. J. Traina, R. M Rangayyan, and P. M. Azevedo-Marques, Using relevance feedback to reduce the semantic gap in content-based image retrieval of mammographic masses., Conf Proc IEEE Med Biol Soc (2008), 406-409.
  16. Y. Tao, S. B. Lo, M. T. Freedman, and J. Xuan, A preliminary study of content-based mammographic masses retrieval, Proc SPIE 6514 (2007), 65141Z.
  17. K. Verma and J. Zakos, A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques, IEEE Transactions on Information Technology in Biomedicine 16 (2002), 219-223.
  18. B. Zheng, C. Mello-Thoms, X. H. Wang, G. S. Abrams, J. H. Sumkin, D. M. Chough, M. A. Ganott, A. Lu, and D. Gur, Interactive computer aided diagnosis of breast masses: Computerized selection of visually similar image sets from a reference library, Academical Radiology 14 (2007), 917-927.
  19. K. Wongsritong, K. Kittayaruasiriwat, F. Cheevasuvit, K. Dejhan, A. Somboonkaew. Contrast enhancement using multipeak histogram equalization with brightness preserving., IEEE Asia-Pacific Conference on Circuits and Systems Proceedings, (1998), 455-458
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Paper Citation


in Harvard Style

Narváez F., Rueda A. and Romero E. (2011). Breast Masses Classification using a Sparse Representation . 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 26-33. DOI: 10.5220/0003304300260033


in Bibtex Style

@conference{miad11,
author={Fabián Narváez and Andrea Rueda and Eduardo Romero},
title={Breast Masses Classification using a Sparse Representation},
booktitle={Proceedings of the 2nd International Workshop on Medical Image Analysis and Description for Diagnosis Systems - Volume 1: MIAD, (BIOSTEC 2011)},
year={2011},
pages={26-33},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003304300260033},
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 - Breast Masses Classification using a Sparse Representation
SN - 978-989-8425-38-6
AU - Narváez F.
AU - Rueda A.
AU - Romero E.
PY - 2011
SP - 26
EP - 33
DO - 10.5220/0003304300260033