Abstract
Mammographic risk assessment is concerned with the probability of a woman developing breast cancer. Recently, it has been suggested that the density of linear structures is related to risk. Two independent sets of mammographic images were annotated according to BIRADS risk classes by expert radiologists. Linear structure information was extracted from each image using the line operator method, and density segmentation was performed using a method based on minimum error thresholding.
Linear discriminant analysis and a Support Vector Machine classifer were used to classify the images in to BIRADS classes. The classification was performed three times for each dataset – once using density information only, once using linear structure information only, and once using both density and linear structure information. The results of classification showed a marked improvement when both density and linear structure information were used, suggesting that linear structure information is valuable in mammographic risk classification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
van Gils, C.H., Otten, J.D., Hendriks, J.H., Holland, R., Straatman, H., Verbeek, A.L.: High mammographic breast density and its implications for the early detection of breast cancer. Journal of Medical Screening 6, 200–204 (1999)
Wolfe, J.N.: Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer 37(5), 2486–2492 (1976)
Kolb, T.M., et al.: Comparison of the performance of screening mammography, physical examination, and breast us and evaluation of factors that influence them: An analysis of 27,825 patient evaluations. Radiology 225(1), 165–175 (2002)
Gram, I.T., et al.: The Tabár classification of mammographic parenchymal patterns. European Journal of Radiology 24(2), 131–136 (1997)
Tabár, et al.: Breast Cancer - The Art and Science of Early Detection with Mammography. Georg Thieme Verlag, Stuttgart (2005)
Tabár, L., Dean, P.B.: Mammographic parenchymal patterns. Risk indicator for breast cancer? Journal of the American Medical Association 247(2), 185–189 (1982)
American College of Radiology: Illustrated Breast Imaging Reporting and Data System. 3rd edn. American College of Radiology (1998)
Suckling, J., et al.: The mammographic images analysis society digital mammogram database. Exerpta Medica. Int. Congress Series 1069, 375–378 (1994)
Heath, M., et al.: The digital database for screening mammography. In: Proc. I.W.D.M., pp. 212–218. Medical Physics Publishing (2001)
Sivaramakrishna, R., et al.: Automatic segmentation of mammographic density. Academic Radiology 8(3), 250–256 (1998)
Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recognition 19(1), 41–47 (1986)
Hadley, E.M., et al.: Mammographic risk assessment based on anatomical linear structures. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds.) IWDM 2006. LNCS, vol. 4046, pp. 626–633. Springer, Heidelberg (2006)
Hadley, E.M., et al.: Risk Classification of Mammograms Using Anatomical Linear Structure and Density Information. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4478, pp. 186–193. Springer, Heidelberg (2007)
Dixon, R.N., Taylor, C.J.: Automated asbestos fibre counting. Institute of Physics Conference Series 44, 178–185 (1979)
Zwiggelaar, R., et al.: Linear structures in mammographic images: Detection and classification. IEEE Transactions on Medical Imaging 23(9), 1077–1086 (2004)
Vapnik, V.N.: The Nature of Staistical Learning Theory. Springer, Heidelberg (1995)
Duin, R., Juszczak, P., Paclik, P., Pekalska, E., de Ridder, D., Tax, D.: Prtools4, a matlab toolbox for pattern recognition (2004)
Bovis, K., Singh, S.: Classification of mammographic breast density using a combined classifier paradigm. In: Proc. M.I.U.A., pp. 177–180 (2002)
Petroudi, S., et al.: Automatic classification of mammographic parenchymal patterns: a statistical approach. In: IEEE I.C.E.M.B.S., vol. 1, pp. 798–801 (2003)
Oliver, A., Freixenet, J., Martí, R., Pont, J., Pérez, E., Denton, E.R., Zwiggelaar, R.: A novel breast tissue density classification methodology. IEEE Trans Inf Technol Biomed. 12(1), 55–65 (2008)
Field, S.: Breast screening issues. Newsletter of the Royal College of Radiologists 54, 12–14 (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hadley, E.M., Denton, E.R.E., Pont, J., Pérez, E., Zwiggelaar, R. (2008). Analysis of Anatomical Linear Structure Information in Mammographic Risk Assessment. In: Krupinski, E.A. (eds) Digital Mammography. IWDM 2008. Lecture Notes in Computer Science, vol 5116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70538-3_67
Download citation
DOI: https://doi.org/10.1007/978-3-540-70538-3_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70537-6
Online ISBN: 978-3-540-70538-3
eBook Packages: Computer ScienceComputer Science (R0)