Angular Resolution Study of Vectors Representing Subtle Spiculated Structures in Mammograms

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 283)

Abstract

In this paper various multiscale transformations, such as contourlets, curvelets, tensor and complex wavelets, were examined in terms of the precise representation of texture directionality in medical images. In particular, subtle radiating and spiculated structures in mammograms were modeled with sparse vectors of the image linear expansions. Important properties of angular resolution, angular selectivity and shift invariance have been evaluated with simple phantoms. According to the experimental results, the complex wavelets have been proved to be the most effective tool in mammogram preprocessing to extract and uniquely represent relevant spicular symptoms for accurate diagnosis.

Keywords

angular resolution and selectivity shift (rotate) invariant multiscale transform spiculed structures enhancement 

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References

  1. 1.
    Adel, M., Zuwala, D., Rasigni, M., Bourennane, S.: Noise reduction on mammographic phantom images. Electronic Letters on Computer Vision and Image Analysis 5(4), 64–74 (2006)Google Scholar
  2. 2.
    Barnes, G.T., Chakraborty, D.P.: Radiographic mottle and patient exposure in mammography. Radiology 145, 815–821 (1982)Google Scholar
  3. 3.
    American College of Radiology (ACR): Breast Imaging Reporting and Data System BI-RADS, 3rd edn. American College of Radiology (1998)Google Scholar
  4. 4.
    Chakraborty, J., Rangayyan, R.M., Banik, S., Mukhopadhyay, S., Desautels, J.E.L.: Statistical Measures of Orientation of Texture for the Detection of Architectural Distortion in Prior Mammograms of Interval Cancer. Journal of Electronic Imaging 21(3), 033010:1–13 (2012)Google Scholar
  5. 5.
    Dziukowa, R.J.: Mammografia w diagnostyce raka sutka, Warszawa (1998)Google Scholar
  6. 6.
    Ichikawa, T., Matsubara, T., Hara, T., Fujita, H., Endo, T., Iwase, T.: Automated detection method for architectural distorion areas on mammograms based on morphological processing and surface analysis. In: Medical Imaging: Image Processing, vol. 5370, pp. 920–925 (2004)Google Scholar
  7. 7.
    Jasionowska, M., Przelaskowski, A., Rutczynska, A., Wroblewska, A.: A two - step method for detection of architectural distortions in mammograms. In: Piętka, E., Kawa, J. (eds.) Information Technologies in Biomedicine. AISC, vol. 69, pp. 73–84. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Jasionowska, M., Przelaskowski, A.: Subtle directional mammographic findings in multiscale domain. In: Piętka, E., Kawa, J. (eds.) ITIB 2012. LNCS, vol. 7339, pp. 77–84. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Jasionowska, M., Przelaskowski, A., Jóźwiak, R.: Charaxteristics of architectural distortions in mammograms - extraction of texture orientation with Gabor filters. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010, Part I. LNCS, vol. 6374, pp. 420–430. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Jiang, H., Tiu, W., Yamamoto, S., Iisaku, S.: Automatic recognition of spicules in mammograms. In: Del Bimbo, A. (ed.) ICIAP 1997. LNCS, vol. 1311, pp. 396–403. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  11. 11.
    Jiang, H., Tiu, W., Yamamoto, S., Iisaku, S.: Detection of spicules in mammograms. In: Proc. of IEEE ICIP 1997, vol. III, pp. 520–523 (1997)Google Scholar
  12. 12.
    Jiang, H., Tiu, W., Yamamoto, S., Iisaku, S.: A method for automatic detection of spicules in mammograms. Journal of Computer Aided Diagnosis of Medical Images 2, 23–31 (1998)Google Scholar
  13. 13.
    Karssemeijer, N., Te Brake, G.M.: Detection of stellate distortions in mammograms. IEEE Transactions on Medical Imaging 15(5), 611–619 (1996)CrossRefGoogle Scholar
  14. 14.
    Kegelmeyer Jr., W.P.: Evalution of stellate lesion detection in a standard mammogram data set. In: Bowyer, K.W., Astley, S. (eds.) State of the Art in Digital Mammographic Image Analysis, pp. 262–279. World Scientific (1993)Google Scholar
  15. 15.
    Kegelmeyer Jr., W.P.: Computer detection of stellate lesions in mammograms. In: Proc. 1992 SPIE, Conf. on Biomedical Image Processing and 3-D Microscopy, vol. 1660, pp. 446–454 (1992)Google Scholar
  16. 16.
    Kobatake, H., Yoshinaga, Y.: Detection of stellate distortion in mammograms. IEEE Trans. Med. Imaging MI-15(3), 235–245 (1996)CrossRefGoogle Scholar
  17. 17.
    Matsubara, T., Fukuoka, D., Yagi, N., Hara, T., Fujita, H., Inenaga, Y., Kasai, S., Kano, A., Endo, T., Iwase, T.: Detection method of architectural distortion based on analysis of structure of mammary gland on mammograms. International Congress Series, vol. 1281, pp. 1036–1040 (2005)Google Scholar
  18. 18.
    Nemoto, M., Honmura, S., Shimizu, A.: A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows. Int. J. CARS 4, 2736 (2009)CrossRefGoogle Scholar
  19. 19.
    Pachoud, M., Lepori, D., Valley, J.F., Verdun, F.R.: A new test phantom with different breast tissue compositions for image quality assessment in conventional and digital mammography. Physics in Medicine and Biology 49, 5267–5281 (2004)CrossRefGoogle Scholar
  20. 20.
    Rangayyan, R.M., Banik, S., Desautels, J.E.L.: Computer-Aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer. Journal of Digital Imaging 23(5), 611–631 (2010)CrossRefGoogle Scholar
  21. 21.
    Sampat, M.P., Markey, M.K., Bovik, A.C.: Computer-aided detection and diagnosis in mammography. IEEE Publication (2004); Selesnick, I.W., Baraniuk, R.G., Kingsbury, N.G.: The Dual-Tree Complex Wavelet Transform. IEEE Signal Processing Magazine, 124–151 (November 2005)Google Scholar
  22. 22.
    Webb, S.: The Physics of Medical Imaging. IOP Publishing, Adam Hilger, Bristol, Philadelphia (1988)CrossRefGoogle Scholar
  23. 23.
    Zwinggelaar, R., Astley, S.M., Boggis, C.R.M., Taylor, C.J.: Linear structures in mammographic images: detection and classification. IEEE Trans. on Medical Imaging 23(9), 1077–1086 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.The Faculty of Electronics and Information TechnologyWarsaw University of TechnologyWarsawPoland
  2. 2.The Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland

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