Signal, Image and Video Processing

, Volume 12, Issue 7, pp 1285–1292 | Cite as

Detection of architectural distortion from the ridges in a digitized mammogram

  • Yusuf Akhtar
  • Dipti Prasad Mukherjee
Original Paper


Architectural distortion (AD) has been described as a focal retraction of the breast tissue. Blood vessels, milk ducts and spicules in the breast tissue appear as ridges in the mammogram. We hypothesize that radiating ridges are an indicator of an AD site. Using a window-based approach, features derived from the ridges have been utilized in a radial basis function support vector machine to classify regions as containing or not containing AD. The classification is performed on the Mammographic Image Analysis Society (MIAS) database and on the Digital Database For Screening Mammography (DDSM). The proposed approach reports peak performance of a sensitivity of 90% (93%) at 26 (17) false positives per mammogram in the MIAS (DDSM) database.


Architectural distortion Spicule Radiating pattern Ridge detector 


Compliance with ethical standards

Conflict of interest

The authors have no potential conflicts of interest to disclose.


  1. 1.
  2. 2. Accessed as in Nov 2017
  3. 3.
    Zonderland, H., Smithuis, R.: Mammography—breast imaging lexicon. Accessed as in May 2017
  4. 4.
    Knutzen, A.M., Gisvold, J.J.: Likelihood of malignant disease for various categories of mammographically detected, nonpalpable breast lesions. In: Mayo Clinic Proceedings, vol. 68, pp. 454–460 (1993)Google Scholar
  5. 5.
    Suckling, J., et al.: The mammographic image analysis society digital mammogram database. In: Exerpta Medica, International Congress Series (1994)Google Scholar
  6. 6.
    Ayres, F.J., Rangayyan, R.M.: Characterization of architectural distortion in mammograms. IEEE Eng. Med. Biol. Mag. 24, 59–67 (2005)CrossRefGoogle Scholar
  7. 7.
    Biswas, S.K., Mukherjee, D.P.: Recognizing architectural distortion in mammogram: a multiscale texture modeling approach with GMM. IEEE Trans. Biomed. Eng. 58, 2023–2030 (2011)CrossRefGoogle Scholar
  8. 8.
    Ichikawa, T., Matsubara, T., Hara, T., Fujita, H., Endo, T., Iwase, T.: Automated detection method for architectural distortion areas on mammograms based on morphological processing and surface analysis. In: Proceedings of SPIE Medical Imaging: Image Process, pp. 920–925 (2004)Google Scholar
  9. 9.
    Sampat, M.P., Whitman, G.J., Markey, M.K., Bovik, A.C.: Evidence based detection of spiculated masses and architectural distortion. In: Proceedings of SPIE Medical Imaging: Image Processing, pp. 26–37 (2005)Google Scholar
  10. 10.
    Cigaroudy, L.S., Aghazadeh, N.: A multiphase segmentation method based on binary segmentation method for Gaussian noisy image. SIViP 11, 825–831 (2017)CrossRefzbMATHGoogle Scholar
  11. 11.
    Soomro, T.A., Khan, M.A.U., Gao, J., Khan, T.M., Paul, M.: Contrast normalization steps for increased sensitivity of a retinal image segmentation method. SIViP 11, 1509–1517 (2017)CrossRefGoogle Scholar
  12. 12.
    Khoubani, S., Nadjar, H.S., Fatemizadeh, E., Mohammadi, E.: A two layer texture modeling based on curvelet transform and spiculated lesion filters for recognizing architectural distortion in mammograms. In: Middle East Conference on Biomedical Engineering, Doha, Qatar, pp. 21–24 (2014)Google Scholar
  13. 13.
    Jasionowska, M., Przelaskowski, A., Rutczynska, A., Wroblewska, A.: A two-step method for detection of architectural distortions in mammograms. Inf. Technol. Biomed. 69, 73–84 (2010)CrossRefGoogle Scholar
  14. 14.
    Shanthi, S., Muralibhaskaran, V.: Automatic detection and classification of microcalcification, mass, architectural distortion and bilateral asymmetry in digital mammogram. Int. J. Med. Health Biomed. Bioeng. Pharm. Eng. 8, 818–823 (2014)Google Scholar
  15. 15.
    Rangayyan, R.M., Banik, S., Desautels, J.E.: Computer-aided detection of architectural distortion in prior mammograms of interval cancer. J. Digit. Imaging 23, 611–631 (2010)CrossRefGoogle Scholar
  16. 16.
    Matsubara, T., Ito, A., Tsunomori, A., Hara, T., Muramatsu, C., Endo, T., Fujita, H.: An automated method for detecting architectural distortions on mammograms using direction analysis of linear structures. In: Engineering in Medicine and Biology Society, 37th Annual International Conference of the IEEE, Milano, Italy, pp. 2661–2664 (2015)Google Scholar
  17. 17.
    Lakshmanan, R., Shiji, T.P., Thomas, V., Jacob, S.M., Pratab, T.: A preprocessing method for reducing search area for architectural distortion in mammographic images. In: Fourth International Conference on Advances in Computing and Communications, Kochi, Kerala, India, pp. 101–104 (2014)Google Scholar
  18. 18.
    Netprasat, O., Auephanwiriyakul, S., Theera-Umpon, N.: Architectural distortion detection from mammograms using support vector machine. In: International Joint Conference on Neural Networks. Beijing, China, pp. 3258–3264 (2014)Google Scholar
  19. 19.
    Mohammadi, E., Fatemizadeh, E., Sheikhzadeh, H., Khoubani, S.: A textural approach for recognizing architectural distortion in mammograms. In: 8th Iranian Conference on Machine Vision and Image Processing, Zanjan, Iran, pp. 136–140 (2013)Google Scholar
  20. 20.
    Zwiggelaar, R., Astley, S.M., Boggis, C.R.M., Taylor, C.J.: Linear structures in mammographic images: detection and classification. IEEE Trans. Med. Imaging 23, 1077–1086 (2004)CrossRefGoogle Scholar
  21. 21.
    Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, W.P.: The digital database for screening mammography. In: Proceedings of the Fifth International Workshop on Digital Mammography, pp. 212–218 (2001)Google Scholar
  22. 22.
    Banik, S., Rangayyan, R.M., Desautels, J.E.L.: Detection of architectural distortion in prior mammograms. IEEE Trans. Med. Imaging 30, 279–294 (2011)CrossRefGoogle Scholar
  23. 23.
    Rangayyan, R.M., Banik, S., Chakraborty, J., Mukhopadhyay, S., Desautels, J.E.: Measures of divergence of oriented patterns for the detection of architectural distortion in prior mammograms. Int. J. Comput. Assist. Radiol. Surg. 8, 527–545 (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Indian Statistical InstituteKolkataIndia

Personalised recommendations