Abstract
Higher-order texture features from 100 mammographic images with known cancer were compared to texture features from 100 images from women with no known cancer. Texture features from images of the same breasts from screening rounds two and four years previously were also compared. The A z score for classifying cancer images from non-cancer images was 0.749. The A z score for classification two years previous to detection of cancer was 0.674 and the score for four years previous was 0.601. There was no signicant difference between classifying images from the round in which cancer was actually detected and the screening rounds two and four years previous. Similar results were obtained if the breast with no known cancer (contralateral breast) was used instead the breast with cancer, leading to the conclusion that texture alone has moderate predictive power regarding breast cancer risk and that this predictive value is roughly constant in the four years prior to mammographically apparent cancer.
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
Gong, Y.C., Brady, M., Petroudi, S.: Texture based mammogram classification and segmentation. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds.) IWDM 2006. LNCS, vol. 4046, pp. 616–625. Springer, Heidelberg (2006)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd edn. Springer Series in Statistics. Springer (2008)
Karemore, G., Keller, B.M., Oh, H., Tchou, J., Nielsen, M., Conant, E.F., Kontos, D.: Mammographic parenchymal texture analysis for estrogen-receptor subtype specific breast cancer risk estimation. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds.) IWDM 2012. LNCS, vol. 7361, pp. 596–603. Springer, Heidelberg (2012)
Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision 43(1), 29–44 (2001)
Li, H., Giger, M.L., Olopade, O.I., Lan, L.: Validation of mammographic texture analysis for assessment of breast cancer risk. In: Martí, J., Oliver, A., Freixenet, J., Martí, R. (eds.) IWDM 2010. LNCS, vol. 6136, pp. 267–271. Springer, Heidelberg (2010)
Li, X.-Z., Williams, S., Bottema, M.J.: Intensity independent texture analysis in screening mammograms. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds.) IWDM 2012. LNCS, vol. 7361, pp. 474–481. Springer, Heidelberg (2012)
Li, X.-Z., Williams, S., Bottema, M.J.: Background intensity independent texture features for assessing breast cancer risk in screening mammograms. Pattern Recognition Letters 34(9), 1053–1062 (2013)
Li, X.-Z., Williams, S., Bottema, M.J.: Constructing and applying higher-order textons: Estimating breast cancer risk. Pattern Recognition 47(3), 1375–1382 (2014)
Li, X.-Z., Williams, S., Bottema, M.J.: Texture and region dependent breast cancer risk assessment from screening mammograms. Pattern Recoginition Letters 36(15), 117–124 (2014)
Magnin, I.E., Cluzeau, F., Odet, C.L., Bremond, A.: Mammographic texture analysis: An evaluation of risk for developing breast cancer. Optical Engineering 25(6), 780–784 (1986)
Ting, C., Astley, S.M., Morris, J., Stavrinos, P., Wilson, M., Barr, N., Boggis, C., Sergeant, J.C.: Longitudinal change in mammographic density and association with breast cancer risk: A case-control study. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds.) IWDM 2012. LNCS, vol. 7361, pp. 205–211. Springer, Heidelberg (2012)
Varma, M., Zisserman, A.: Texture classification: Are filter banks necessary? In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 691–698 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Li, XZ., Williams, S., Downey, P., Bottema, M.J. (2014). Temporal Breast Cancer Risk Assessment Based on Higher-Order Textons. In: Fujita, H., Hara, T., Muramatsu, C. (eds) Breast Imaging. IWDM 2014. Lecture Notes in Computer Science, vol 8539. Springer, Cham. https://doi.org/10.1007/978-3-319-07887-8_79
Download citation
DOI: https://doi.org/10.1007/978-3-319-07887-8_79
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07886-1
Online ISBN: 978-3-319-07887-8
eBook Packages: Computer ScienceComputer Science (R0)