Abstract
In this work we study how the BI-RADS assessment could help to improve the performance of a CAD (Computer Aided Diagnosis) image-based system in the task of masses diagnosis. Our system is based on the use of Independent Component Analysis (ICA) as feature extractor from mammographic images, and Neural Networks as a final classifier. For our tests, the “Digital Database for Screening Mammography” (DDSM) has been used, particularly the subset BCRP_MASS1. The best results were obtained when we used the image data (with feature extraction by means of ICA) together with the BI-RADS assessment provided by DDSM database.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
CRUK. Breast cancer factsheet. Technical report, Cancer Research UK, London (December 2009), http://www.cancerresearchuk.org
Haffty, B.G., Kornguth, P., Fisher, D., Beinfield, M., McKhann, C.: Mammographically detected breast cancer: Results with conservative surgery and radiation therapy. Cancer 67, 2801–2804 (1991)
Chung, B., Cady, M.: Mammographic screening: No longer controversial. American Journal of Clinical Oncology: Cancer Clinical Trials 28(1), 1–4 (2005)
Wallis, R.G., Moss, M.G., Blanks, S.M.: A comparison of cancer detection rates achieved by breast cancer screening programmes by number of readers, for one and two view mammography. Journal of Medical Screening 5(4), 195–201 (1998)
Sampat, M.P., Markey, M.K., Bovik, A.C.: Computer-Aided Detection and Diagnosis in Mammography. In: Handbook of Image and Video Processing, 2nd edn., pp. 1195–1217. Academic Press, London (2005)
Rangayyan, R.M., Ayres, F.J., Leo Desautels, J.E.: A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs. Journal of the Franklin Institute 344(3-4), 312–348 (2007)
Elter, M., Horsch, A.: Cadx of mammographic masses and clustered microcalcifications: A review. Medical Physics 36(6), 2052–2068 (2009)
Nishikawa, R.M.: Current status and future directions of computer-aided diagnosis in mammography. Computerized Medical Imaging and Graphics 31(4-5), 224–235 (2005)
Domínguez, A.R., Nandi, A.K.: Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection. Computerized Medical Imaging and Graphics 32(4), 304–315 (2008)
Freixenet, J., Oliver, A., Martí, R., Lladó, X., Pont, J., Pérez, E., Denton, E.R.E., Zwiggelaar, R.: Eigendetection of masses considering false positive reduction and breast density information. Medical Physics 35(5), 1840–1853 (2008)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley & Sons, Chichester (2001)
Suckling, J., et al.: The mammographic image analysis society digital mammogram database. In: Exerpta Medica. International Congress Series, vol. 1069, pp. 375–378 (1994)
Heath, M., Bowyer, K.W., Kopans, D.B.: Current status of the digital database for screening mammography. In: Digital Mammography. Kluwer Academic, Dordrecht (1998)
American Collage of Radiology. BI-RADS – Breast Imaging Atlas – Mammography, 4th edn. (2003)
Hyvärinen, A., Oja, E.: Independent components analysis: algorithms and applications. Neural Networks 13, 411–430 (2000)
Sing, T., Sander, O., Beerenwinkel, N., Lengauer, T.: Rocr: visualizing classifier performance in r. Bioinformatics 21(20), 3940–3941 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
García-Manso, A., García-Orellana, C.J., Gallardo-Caballero, R., González-Velasco, H., Macías-Macías, M. (2010). Effect of BI-RADS Assessment in Improving CAD of Masses. In: Martí, J., Oliver, A., Freixenet, J., Martí, R. (eds) Digital Mammography. IWDM 2010. Lecture Notes in Computer Science, vol 6136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13666-5_83
Download citation
DOI: https://doi.org/10.1007/978-3-642-13666-5_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13665-8
Online ISBN: 978-3-642-13666-5
eBook Packages: Computer ScienceComputer Science (R0)