Presenting Two Tumor’ Edge Analyzing Strategies to Assert Lobularity Quantification on Digital Breast Tomosythesis Images

  • Fernando Yepes-CalderonEmail author
  • Camila Ospina
  • Flor Marina
  • Jose Abella
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 735)


Tumor grading is as important as difficult to assert, and there is a growing interest in making this possible from the medical images. Very often radiologist deal with the lack of consensus regarding tumor classification and grading. Despite there is an apparently comprehensive guide to qualify the masses, the qualitative nature of the classification directives guides the specialists to different appreciations. Then, the histology dissolves any hesitation at the expense of time and patient discomfort. Having a reliable quantifying strategy would not only assist the radiologist in their verdicts, but it will also speed up the diagnosing processes and may avoid the need for invasive and uncomfortable procedures such as the biopsy. In this manuscript, we present two algorithms that extract numbers in a slice by slice fashion using the tumor edging irregularities. As a proof of concetp, clinical breast tomosythesis images are treated under the mentioned methods and their outcomes are presented next to the extracted numerical insights.


Tumor grading Cancer Tumor characterization Medical image analysis 


  1. 1.
    Arakeri, M.P., Reddy, R.M.: A novel CBIR approach to differential diagnosis of liver tumor on computed tomography images. Procedia Eng. 38, 528–536 (2012)CrossRefGoogle Scholar
  2. 2.
    Chaudhry, H.S., Davenport, M.S., Nieman, C.M., Ho, L.M., Neville, A.M.: Histogram analysis of small solid renal masses: differentiating minimal fat angiomyolipoma from renal cell carcinoma. Genitourin. Imaging 1, 1–3 (2011)Google Scholar
  3. 3.
    Halpin, S.F.S.: Medico-legal claims against english radiologists: 1995–2006. Br. J. Radiol. 82(984), 982–988 (2009). PMID: 19470570CrossRefGoogle Scholar
  4. 4.
    Fütterer, J., Heijmink, S., Scheenen, T., Veltman, J., Huisman, H.J.: Prostate cancer localization with dynamic contrast-enhanced MR imaging and proton MR spectroscopic imaging. RSNA Radiol. 241(2), 449–458 (2006)CrossRefGoogle Scholar
  5. 5.
    Oto, A., Kayhan, A., Jiang, Y., Tretiakova, M., Yang, C., Antic, T.: Prostate cancer: differentiation of central gland cancer from benign prostatic hyperplasia by using diffusion-weighted and dynamic contrast-enhanced MR imaging. RSNA Radiol. 257(3), 715–723 (2010)CrossRefGoogle Scholar
  6. 6.
    Mendez, A., Tahoces, P., Lado, M., Souto, M., Correa, J., Vidal, J.: Automatic detection of breast border and nipple in digital mammograms. Comput. Methods Progr. Biomed. 49(3), 253–262 (1996)CrossRefGoogle Scholar
  7. 7.
    Doyle, S., Rodriguez, C.: Detecting prostatic adenocarcinoma from digitized histology using a multi-scale hierarchical classification approach. In: EMB-IEEE (2006)Google Scholar
  8. 8.
    Bedossa, P.: Liver biopsy. Grastroenterol. Clin. Bio. 32, 4–7 (2008)CrossRefGoogle Scholar
  9. 9.
    Stigliano, R., Marelli, L., Yu, D., Davies, N., Patch, D., Burroughs, A.K.: Seeding following percutaneous diagnostic and therapeutic approaches for hepatocellular carcinoma. what is the risk and the outcome? seeding risk for percutaneous approach of hcc. Cancer Treat. Rev. 33(5), 437–47 (2007)CrossRefGoogle Scholar
  10. 10.
    Brown, J.M., Giaccia, A.J.: The unique physiology of solid tumors: opportunities (and problems) for cancer therapy. Cancer Res. 7, 1408–1416 (1998)Google Scholar
  11. 11.
    Ambrosi, D., Mollica, F.: On the mechanics of a growing tumor. Int. J. Eng. Sci. 40, 1297–1316 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Whitted, T.: An improved illumination model for shaded display. Commun. ACM 13(2), 343–349 (1980)CrossRefGoogle Scholar
  13. 13.
    Rigau, J., FEixas, M., Sbert, M.: New contrast measurements for pixel supersampling. In: Proceedings of CGI 2002 (2002)Google Scholar
  14. 14.
    Canny, J.: A computational approach to edge detection. PAMI 8, 679–698 (1986)CrossRefGoogle Scholar
  15. 15.
    Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423, 623–656 (1948)Google Scholar
  16. 16.
    Abramoff, M., Magalhaes, P., Ram, S.: Image processing with imagej. Biophotonics Int. 11(7), 36–42 (2004)Google Scholar
  17. 17.
    Schulz, J., Skrøvseth, S.O., Tømmerås, V.K., Marienhagen, K., Godtliebsen, F.: A semiautomatic tool for prostate segmentation in radiotherapy treatment planning. BMC Med. Imaging 12(1), 1 (2014)CrossRefGoogle Scholar
  18. 18.
    dos Santos, D.P., Kloeckner, R., Wunder, K., Bornemann, L., Düber, C., Mildenberger, P.: Effect of kernels used for the reconstruction of MDCT datasets on the semi-automated segmentation and volumetry of liver lesions. Tech. Med. Phys. 19, 780–784 (2013)Google Scholar
  19. 19.
    Guo, Y., Feng, Y., Sun, J., Zhang, N., Lin, W., Sa, Y., Wang, P.: Automatic lung tumor segmentation on pet/ct images using fuzzy markov random field model. Comput. Math. Methods Med. 2014, 8–10 (2014)CrossRefzbMATHGoogle Scholar
  20. 20.
    Zhang, J., Barboriak, D.P., Hobbs, H., Mazurowski, M.A.: A fully automatic extraction of magnetic resonance image features in glioblastoma patients. Med. Phys. 41, 1–13 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fernando Yepes-Calderon
    • 1
    Email author
  • Camila Ospina
    • 2
  • Flor Marina
    • 2
  • Jose Abella
    • 2
  1. 1.Division of NeurosurgeryChildren’s Hospital Los AngelesLos AngelesUSA
  2. 2.Fundación Valle del LiliCaliColombia

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