Abstract
Automatic detection and identification of mammography masses is important for breast cancer diagnosis. However, it is challenging to differentiate masses from normal breast regions because they usually have low contrast and a poor boundary. In this study, we present a Computer-Aided Detection (CAD) system for automatic breast mass identification. A four-stage region-based procedure is adopted for processing the mammogram images, i.e. localization, segmentation, feature extraction, and feature selection and classification. The proposed CAD system is evaluated using selected mammogram images from the Mammographic Image Analysis Society (MIAS) database. The experimental results demonstrate that the proposed CAD system is able to identify mammography masses in an automated manner, and is useful as a decision support system for breast cancer diagnosis.
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References
American Cancer Society, http://www.cancer.org (access date: 2010-08-13)
Sampat, M.P., Markey, M.K., Bovik, A.C.: Computer-aided detection and diagnosis in mammography. In: Bovik, A.C. (ed.) Handbook of Image and Video Processing, 2nd edn., pp. 1195–1217. Academic, New York (2005)
Olivera, A., Freixeneta, J., MartÃa, J., Pérezb, E., Pontb, J., Dentonc, E.R.E., Zwiggelaard, R.: A review of automatic mass detection and segmentation in mammographic images. Med. Imag. Anal. 14(2), 87–110 (2010)
Liu, S., Babbs, C.F., Delp, E.J.: Multiresolution detection of speculated lesions in digital mammograms. IEEE Trans Image Process 10(6), 874–884 (2001)
The Mini-MIAS Database of Mammograms, http://peipa.essex.ac.uk (access date: 2010-08-13)
Mudigonda, N.R., Rangayyan, R.M., Desautels, J.E.L.: Gradient and texture analysis for the classification of mammographic masses. IEEE Trans. Med. Imag. 19(10), 1032–1043 (2000)
Hupse, R., Karssemeijer, N.: Use of normal tissue context in computer-aided detection of masses in mammograms. IEEE Trans. Med. Imag. 28(2), 2033–2041 (2009)
Gao, X., Wang, Y., Li, X., Tao, D.: On combining morphological component analysis and concentric morphology model for mammographic mass detection. IEEE Trans. Info Tech. in Biomed. 14(2), 266–273 (2010)
Shi, J., Sahiner, B., Chan, H.P., Ge, J., Hadjiiski, L.M., Helvie, M.A., Nees, A., Wu, Y.T., Wei, J., Zhou, C., Zhang, Y., Cui, J.: Characterization of mammographic masses based on level set segmentation with new image features and patient information. Med. Phys. 35(1), 280–290 (2008)
Pu, J., Zheng, B., Leader, J.K., Gur, D.: An ellipse-fitting based method for efficient registration of breast masses on two mammographic views. Med. Phys. 35(2), 487–494 (2008)
Velikova, M., Samulski, M., Lucas, P.J., Karssemeijer, N.: Improved mammographic CAD performance using multi-view information: a Bayesian network frameworks. Phys. Med. Biol. 54(5), 1131–1147 (2009)
Varela, C., Tahoces, P.G., Mendez, A.J., Souto, M., Vidal, J.J.: Computerized detection of breast masses in digitized mammograms. Comput. Biol. Med. 37(2), 214–226 (2007)
Rojas, A., Nandi, A.: Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection. Comput Med. Imag. Graph 32(4), 304–315 (2008)
Qian, W., Song, D., Lei, M., Sankar, R., Eikman, E.: Computer-aided mass detection based on ipsilateral multiview mammograms. Acad. Radiol. 14(5), 530–538 (2007)
Digital Database for Screening Mammography, http://marathon.csee.usf.edu (access date: 2010-08-13)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man, Cybern. 9(1), 62–66 (1979)
Vincent, L.: Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans. Image Process 2(2), 176–201 (1993)
Li, C., Kao, C., Gore, J.C., Ding, Z.: Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process 17(10), 1940–1949 (2008)
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Samma, H., Lim, C.P., Samma, A. (2010). A Computer-Aided Detection System for Automatic Mammography Mass Identification. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_28
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DOI: https://doi.org/10.1007/978-3-642-17534-3_28
Publisher Name: Springer, Berlin, Heidelberg
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