Abstract
Identifying breast tumor in a mammogram is a challenging task even for experienced radiologists if the tumor is located in a dense tissue. In this study, a novel superpixel based graph modeling technique is proposed to extract texture features from the computer identified suspicious regions of mammograms. Graph models are constructed from specific structured superpixel patterns and used to generate feature vectors used for classifications of regions of mammograms. Two mammographic datasets were used to evaluate the effectiveness of the proposed approach: the publicly available Digital Database for Screening Mammography (DDSM), and a local database of mammograms (BSSA). Using Linear Discriminant Analysis (LDA) classifier, an AUC score of 0.910 was achieved for DDSM and 0.893 for BSSA. The results indicate that graph models can capture texture features capable of identifying masses located in dense tissues, and help improve computer-aided detection systems.
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 subscriptionsReferences
Bilgin, C., Demir, C., Nagi, C., et al.: Cell-graph mining for breast tissue modeling and classification. In: 29th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, EMBS 2007, pp. 5311–5314 (2007)
Bilgin, C.C., Bullough, P., Plopper, G.E., et al.: ECM-aware cell-graph mining for bone tissue modeling and classification. Data Min. Knowl. Disc. 20(3), 416–438 (2010)
Chawla, N.V., Bowyer, K.W., Hall, L.O., et al.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16(2002), 321–357 (2002)
Chen, Z., Strange, H., Oliver, A., et al.: Topological modeling and classification of mammographic microcalcification clusters. IEEE Trans. Biomed. Eng. 62(4), 1203–1214 (2015)
Choi, J., Ro, Y.M.: Multiresolution local binary pattern texture analysis combined with variable selection for application to false-positive reduction in computer-aided detection of breast masses on mammograms. Phys. Med. Biol. 57(21), 7029–7052 (2012)
Don, S., Choi, E., Min, D.: Breast mass segmentation in digital mammography using graph cuts. In: Lee, G., Howard, D., Ślęzak, D. (eds.) ICHIT 2011. CCIS, vol. 206, pp. 88–96. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24106-2_12
Hall, M.A.: Correlation-based feature selection for machine learning (1999)
Heath, M., Bowyer, K., Kopans, D., et al.: The digital database for screening mammography. In: Proceedings of the Fifth International Workshop on Digital Mammography, pp. 212–218. Medical Physics Publishing (2001)
Ma, F., Bajger, M., Bottema, M.J.: A graph matching based automatic regional registration method for sequential mammogram analysis. In: Proceedings of SPIE, vol. 6915, p. 6915-11 (2008)
Ma, F., Bajger, M., Slavotinek, J.P., et al.: Two graph theory based methods for identifying the pectoral muscle in mammograms. Pattern Recogn. 40(9), 2592–2602 (2007)
Nock, R., Nielsen, F.: Statistical region merging. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1452–1458 (2004)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Oztan, B., Shubert, K.R., Bjornsson, C.S., et al.: Biologically-driven cell-graphs for breast tissue grading. In: 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI), pp. 137–140. IEEE (2013)
Sajeev, S., Bajger, M., Lee, G.: Segmentation of breast masses in local dense background using adaptive clip limit-CLAHE. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8, November 2015
Sajeev, S., Bajger, M., Lee, G.: Superpixel pattern graphs for identifying breast mass ROIs in dense background: a preliminary study. In: 14th International Workshop on Breast Imaging (IWBI 2018). Proc. SPIE. vol. 10718 (2018)
Sajeev, S., Bajger, M., Lee, G.: Superpixel texture analysis for classification of breast masses in dense background. IET Comput. Vision 12(6), 779–786 (2018)
Stephen, K., James, D., McCluggage, G., et al.: An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN). J. Pathol. 192(3), 351–362 (2000)
Acknowledgments
The authors would like to thank Dr. Peter Downey, clinical radiologist of BreastScreen SA for validating the core mass contours and valuable comments and discussions.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Sajeev, S., Bajger, M., Lee, G. (2019). Graph Modeling for Identifying Breast Tumor Located in Dense Background of a Mammogram. In: Zhang, D., Zhou, L., Jie, B., Liu, M. (eds) Graph Learning in Medical Imaging. GLMI 2019. Lecture Notes in Computer Science(), vol 11849. Springer, Cham. https://doi.org/10.1007/978-3-030-35817-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-35817-4_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35816-7
Online ISBN: 978-3-030-35817-4
eBook Packages: Computer ScienceComputer Science (R0)
