Abstract
An improved image similarity method is introduced to recognize breast cancer, and it is incorporated into a computer-aided breast cancer detection system through Bayes Theorem. Radiologists can use the differences between the left and right breasts, or asymmetry, in mammograms to help detect certain malignant breast cancers. Image similarity is used to determine asymmetry using a contextual and then a spatial comparison. The mammograms are filtered to find the most contextually significant points, and then the resulting point set is analyzed for spatial similarity. We develop the analysis through a combination of modeling and supervised learning of model parameters. This process correctly classifies mammograms 84% of the time, and significantly improves the accuracy of a computer-aided breast cancer detection system by 71%.
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.
References
American Cancer Society. Breast Cancer Facts and Figures 1999-2000. American Cancer Society, Inc., Atlanta, GA (1999)
Linda, J., Burhenne, W., Wood, S.A., D’Orsi, C.J., Feig, S.A., Kopans, D.B., O’Shaughnessy, K.F., Sickles, E.A., Tabar, L., Vyborny, C.J., Castellino, R.A.: Potential contribution of computer-aided detection to the sensitivity of screening mammography. Radiology 215, 554–562 (2000)
Freer, T.W., Ulissey, M.J.: Screening mammography with computer- aided detection. Radiology 220, 781–786 (2001)
Ferrari, R.J., Rangayyan, R.M., Desautels, J.E.L., Frere, A.F.: Analysis of asymmetry in mammograms via directional filtering with Gabor wavelets. IEEE Trans. on Medical Imaging 20(9) (2001)
Astley, S., Mistry, T., Boggis, C.R.M., Hillier, V.F.: Should we use humans or a machine to pre-screen mammograms? In: Proc. of the Sixth Int. Workshop on Digital Mammography, pp. 476–480 (2002)
Astley, S., Gilbert, F.J.: Computer-aided detection in mammography. Clinical Radiology 59, 390–399 (2004)
Tahmoush, D.A., Samet, H.: Using image similarity and asymmetry to detect breast cancer. In: Proc. SPIE Int. Soc. Opt. Eng., vol. 6144 (2006)
Soffer, A., Samet, H.: Pictorial queries by image similarity. In: Proc. of the 13th Int. Conf. on Pattern Recognition, vol. 3, pp. 114–119 (1996)
Gudivada, V., Raghavan, V.: Design and evaluation of algorithms for image retrieval by spatial similarity. ACM Trans. on Inf. Sys. 13(2), 115–144 (1995)
El-Naqa, I., Yang, Y., Galatsanos, N.P., Nishikawa, R.M., Wernick, M.N.: A similarity learning approach to content based image retrieval: application to digital mammography. IEEE Trans. on Medical Imaging 23(10), 1233–1244 (2004)
Goldberger, J., Gordon, S., Greenspan, H.: An efficient image similarity measure based on approximations of kl-divergence between two gaussian mixtures. In: Proc. of the Ninth IEEE Int. Conf. on Computer Vision, pp. 487–493 (2003)
Swett, H.A., Miller, P.L.: Icon: a computer-based approach to differential diagnosis in radiology. Radiology 163, 555–558 (1987)
Guimond, A., Subsol, G.: Automatic mri database exploration and applications. Pattern Recognition and Artificial Intelligence 11(8), 1345–1365 (1997)
Heath, M.D., Bowyer, K.W.: Mass detection by relative image intensity. In: The Proc. of the 5th Int. Conf. on Digital Mammography, Medical Physics Publishing, Madison (2000)
Sajda, P., Spense, C., Parra, L.: Capturing contextual dependencies in medical imagery using hierarchical multi-scale models. In: Proc. of the IEEE Int. Symp. on Biomedical Imaging, pp. 165–168 (2002)
Kalman, B.L., Kwasny, S.C., Reinus, W.R.: Diagnostic screening of digital mammograms using wavelets and neural networks to extract structure. Technical Report 98-20, Washington University (1998)
Lui, S., Babbs, C.F., Delp, E.J.: Multiresolution detection of spiculated lesions in digital mammograms. IEEE Trans. on Image Processing 6, 874–884 (2001)
Campanini, R., Bazzani, A., Bevilacqua, A., Bollini, D., Dongiovanni, D., Iampieri, E., Lanconelli, N., Riccardi, A., Roffilli, M., Tazzoli, R.: A novel approach to mass detection in digital mammography based on support vector machines. In: Proc. of the 6th Int. Workshop on Digital Mammography (2002)
Miller, P., Astley, S.: Detection of breast asymmetry using anatomical features. In: Proc. of the Int. Society for Optical Engineering Conf. on Biomedical Image Processing and Biomedical Visualization, vol. 1905, pp. 433–442 (1993)
Wirth, M.A., Jennings, A.: A nonrigid-body approach to matching mammograms. In: Proc. of the IEEE Image Processing and its Applications, pp. 484–487 (1999)
Yin, F.F., Giger, M.L., Doi, K., Metz, C.E., Vyborny, C.J., Schmidt, R.A.: Computerized detection of masses in digital mammograms: analysis of bilateral subtraction images. Medical Physics 18, 955–963
Gondra, I., Heisterkamp, D.R.: Learning in region-based image retrieval with generalized support vector machines. In: Proc. of the Computer Vision and Pattern Recognition, p. 149 (2004)
Heath, M.D., Bowyer, K.W., Kopans, D., et al.: Current status of the digital database for screening mammography. In: Digital Mammography, pp. 457–460. Kluwer Academic Publishers, Dordrecht (1998)
Dettling, M., Buhlmann, P.: Boosting for tumor classification with gene expression data. Bioinformatics 19(9), 1061–1069 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tahmoush, D., Samet, H. (2006). Image Similarity and Asymmetry to Improve Computer-Aided Detection of Breast Cancer. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds) Digital Mammography. IWDM 2006. Lecture Notes in Computer Science, vol 4046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11783237_31
Download citation
DOI: https://doi.org/10.1007/11783237_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35625-7
Online ISBN: 978-3-540-35627-1
eBook Packages: Computer ScienceComputer Science (R0)