A Study on Image Segmentation in Curvelet Domain Using Snakes and Fractals for Cancer Detection in Mammograms

  • R. Roopa ChandrikaEmail author
  • S. KarthikEmail author
  • N. KarthikeyanEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 898)


Breast cancer is one of the common diseases that affect the quality of life in women. To improve the mortality rate, screening procedures are recommended by medical practitioners. The procedure involves detecting and diagnosing the suspected regions. Detecting the suspected regions in the digital mammograms is a challenging task. In this work, multiresolution analysis is done to resolve the curve discontinuities and procedures like fractals and snakules are applied for segmenting the suspected region. The algorithms have been tested on mini-MIAS and observed that snakes have identified the regions better than fractals. Snakes are able to converge within few iterations determining the circumference of the suspected region and also able to differentiate tumorous and non-tumorous regions.


Curvelet Fractals Snakes Mammogram Breast cancer 


  1. 1.
    Miller, K.D., Jemal, A., Siegel, R.L.: Cancer statistics CA. A Cancer J. Clin. 66(7–30). (2016)Google Scholar
  2. 2.
    Cheng, H.D., Shi, X.J., Min, R., Hu, L.M., Cai, X.P., Du, H.N.: Approaches for automated detection and classification of masses in mammograms. Pattern Recogn. 39(4), 646–668 (2006)CrossRefGoogle Scholar
  3. 3.
    Pal, S.K.: Fuzzy image processing and recognition: uncertainty handling and applications. Int. J. Image Graph. 1(02), 169–195 (2001)CrossRefGoogle Scholar
  4. 4.
    Liu, S., Babbs, C.F., Delp, E.J.: Multiresolution detection of spiculated lesions in digital mammograms. IEEE Trans. Image Process. 10(6), 874–884 (2001)CrossRefGoogle Scholar
  5. 5.
    Donoho, D.L., Duncan, M.R.: Digital curvelet transform: strategy, implementation, and experiments. In: Proceedings of SPIE 4056, Wavelet Applications VII (2000)Google Scholar
  6. 6.
    Eltoukhy, M.M., Samir, B.B., Faye, I.: Breast cancer diagnosis in digital mammogram using multiscalecurvelet transform. Comput. Med. Imaging Graph. 34(4), 269–276 (2010)CrossRefGoogle Scholar
  7. 7.
    Candes, E.J., Donoho, D.L.: Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges. Stanford Univ Ca Dept of Statistics (2000)Google Scholar
  8. 8.
    Rangayyan, R.M., Nguyen, T.M.: Fractal analysis of contours of breast masses in mammograms. J. Digit. Imaging 20(3), 223–237 (2007)CrossRefGoogle Scholar
  9. 9.
    Tuyet V.T.H.: Active contour based on curvelet domain in medical images. In: Vinh, P., Barolli, L. (eds.) Nature of Computation and Communication. ICTCC 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 168. Springer, Cham (2016)Google Scholar
  10. 10.
    Muralidhar, G.S., et al.: Snakules: a model-based active contour algorithm for the annotation of spicules on mammography. IEEE Trans. Med. Imaging 29(10), 1768–1780 (2010)CrossRefGoogle Scholar
  11. 11.
    Suckling, J.: The mammographic image analysis society digital mammogram database. In: Exerpta Medica International Congress Series 1069, pp. 375–378 (1994)Google Scholar
  12. 12.
    Wirth, M.A., Stapinski, A.: Segmentation of the breast region in mammograms using active contours. In: VCIP, pp. 1995–2006 (2003)Google Scholar
  13. 13.
    Ma, J., Plonka, G.: The curvelet transform. IEEE Signal Process. Mag. 27(2), 118–133 (2010)CrossRefGoogle Scholar
  14. 14.
    Starck, J.-L., Candès, E.J., Donoho, D.L.: The curvelet transform for image denoising. IEEE Trans. Image Process. 11(6), 670–684 (2002)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Graps, A.: An introduction to wavelets. IEEE Comput. Sci. Eng. 2(2), 50–61 (1995)CrossRefGoogle Scholar
  16. 16.
    Chandrika, R.R., Karthikeyan, N., Karthik, S.: Simplified contrast enhancement fuzzy technique in digital mammograms for detecting suspicious cells. J. Med. Imaging Health Inf. 7(2), 316–322 April (2017).CrossRefGoogle Scholar
  17. 17.
    Vuduc, R.: Image segmentation using fractal dimension. Report on GEOL 634 (1997)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Information TechnologyMalla Reddy College of Engineering and TechnologyHyderabadIndia
  2. 2.Department of Computer ApplicationsSNS College of Engineering and TechnologyCoimbatoreIndia
  3. 3.Department of Computer ApplicationsSNS College of EngineeringCoimbatoreIndia

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