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Graph Modeling for Identifying Breast Tumor Located in Dense Background of a Mammogram

  • Shelda SajeevEmail author
  • Mariusz BajgerEmail author
  • Gobert LeeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11849)

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.

Keywords

Graph modeling Superpixel tessellation Mass localization Dense background Mammography 

Notes

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.

References

  1. 1.
    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)Google Scholar
  2. 2.
    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)MathSciNetCrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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_12CrossRefGoogle Scholar
  7. 7.
    Hall, M.A.: Correlation-based feature selection for machine learning (1999)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Nock, R., Nielsen, F.: Statistical region merging. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1452–1458 (2004)CrossRefGoogle Scholar
  12. 12.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    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 2015Google Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    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)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Science and EngineeringFlinders UniversityAdelaideAustralia

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