COMPLIANCE OF PUBLICLY AVAILABLE MAMMOGRAPHIC DATABASES WITH ESTABLISHED CASE SELECTION AND ANNOTATION REQUIREMENTS

Inês C. Moreira, Gustavo Bacelar-Silva, Pedro Pereira Rodrigues

2012

Abstract

Mammographic databases play an important role in the development of algorithms aiming to improve Computer-Aided Detection and Diagnosis systems (CAD). However, these often do not take into consideration all the requirements needed for a proper study, previously discussed at the Biomedical Image Processing Meeting in 1993. Case selection and annotation requirements are the most commonly referenced in literature, when describing a database used for the development of such algorithms. This work aims to assess the compliance and suitability of case selection and annotation requirements in the publicly available mammographic databases for development and optimization of CADs. A literature review has been made, applying proper selection criteria related to the research question. In the literature, we found citations to 3 publicly available mammographic databases and ten having restricted access. Through the analysis of the results attained, we noticed that none of the two requirements previously described is on its way to be fully complied in mammographic databases. We can conclude that researchers need a database that fulfils all the mentioned requirements in order to develop efficacious and effective CAD systems. We also believe that the requirements, discussed in 1993, need to be reviewed and updated. New paradigms and ideas to increase algorithms' performance are needed in order to improve CAD schemes.

References

  1. American College of Radiology, 2003. American College of Radiology Breast Imaging and Data System (BIRADS) 4th ed.
  2. Eurostat, 2009. Health Statistics Atlas on Mortality in the European Union.
  3. Heath, M. et al., 1998. Current status of the Digital Database for Screening Mammography. In Digital Mammography. p. 457-460.
  4. Jiang, L. et al., 2008. Automated Detection of Breast Mass Spiculation Levels and Evaluation of Scheme Performance. Academic Radiology, 15(12), p.1534- 1544.
  5. Llobet, R., Paredes, R. and Pérez-Cortés, J.C., 2005. Comparison of Feature Extraction Methods for Breast Cancer Detection. In J. S. Marques, N. Pérez de la Blanca, & P. Pina, orgs. Pattern Recognition and Image Analysis. Lecture Notes in Computer Science. Springer Berlin / Heidelberg, p. 495-502.
  6. Matheus, B. R. N. and Schiabel, H., 2010. Online Mammographic Images Database for Development and Comparison of CAD Schemes. Journal of Digital Imaging.
  7. Nishikawa, R. M., 1997. Development of a Common Database for Digital Mammography Research.
  8. Nishikawa, R. M., 1998. Mammographic databases. Breast Disease, 10(3-4), p.137-150.
  9. Oliver, A., Freixenet, J., et al., 2010. A review of automatic mass detection and segmentation in mammographic images. Medical Image Analysis, 14, p.87-110.
  10. Oliver, A., Lladó, X., et al., 2010. A Statistical Approach for Breast Density Segmentation. Journal of Digital Imaging, 23, p.527-537.
  11. Rangayyan, R. M., Mudigonda, N. and Desautels, J., 2000. Boundary modelling and shape analysis methods for classification of mammographic masses. Medical and Biological Engineering and Computing, 38(5), p.487- 496.
  12. Rojas Dominguez, A. and Nandi, A., 2007. Detection of masses in mammograms using enhanced multilevelthresholding segmentation and region selection based on rank. In Proceedings of the 5th IASTED International Conference on Biomedical Engineering, BioMED 2007. p. 370-375.
  13. Song, Enmin et al., 2010. Hybrid Segmentation of Mass in Mammograms Using Template Matching and Dynamic Programming. Academic Radiology, 17(11), p.1414-1424.
  14. Suckling, J., 1994. The Mammographic Image Analysis Society Digital Mammogram Database. In Exerpta Medica. International Congress Series 1069. York, England, p. 375-378.
  15. Wang, D., Shi, L. and Ann Heng, P., 2009. Automatic detection of breast cancers in mammograms using structured support vector machines. Neurocomputing, 72(13-15), p.3296-3302.
  16. World Health Organization, 2009. Fact sheet Nº 297: Cancer.
  17. Zheng, Bin et al., 2003. Mammography with ComputerAided Detection: Reproducibility Assessment - Initial Experience. Radiology, 228, p.58-62.
  18. Zheng, Bin et al., 2010. Computer-Aided Detection: The Effect of Training Databases on Detection of Subtle Breast Masses. Academic radiology, 17(11), p.1401- 1408.
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Paper Citation


in Harvard Style

C. Moreira I., Bacelar-Silva G. and Pereira Rodrigues P. (2012). COMPLIANCE OF PUBLICLY AVAILABLE MAMMOGRAPHIC DATABASES WITH ESTABLISHED CASE SELECTION AND ANNOTATION REQUIREMENTS . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2012) ISBN 978-989-8425-88-1, pages 337-340. DOI: 10.5220/0003704303370340


in Bibtex Style

@conference{healthinf12,
author={Inês C. Moreira and Gustavo Bacelar-Silva and Pedro Pereira Rodrigues},
title={COMPLIANCE OF PUBLICLY AVAILABLE MAMMOGRAPHIC DATABASES WITH ESTABLISHED CASE SELECTION AND ANNOTATION REQUIREMENTS},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2012)},
year={2012},
pages={337-340},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003704303370340},
isbn={978-989-8425-88-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2012)
TI - COMPLIANCE OF PUBLICLY AVAILABLE MAMMOGRAPHIC DATABASES WITH ESTABLISHED CASE SELECTION AND ANNOTATION REQUIREMENTS
SN - 978-989-8425-88-1
AU - C. Moreira I.
AU - Bacelar-Silva G.
AU - Pereira Rodrigues P.
PY - 2012
SP - 337
EP - 340
DO - 10.5220/0003704303370340