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Mass Segmentation in Mammograms Based on the Combination of the Spiking Cortical Model (SCM) and the Improved CV Model

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Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9475))

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Abstract

In this paper, a novel method based on CV model for the mass segmentation is proposed. Firstly, selecting the largest connected region, seeded region growing, and singular value decomposition (SVD) are used to pre-processing. After that apply the Spiking Cortical Model (SCM) on the pre-processed image to locate the lesion. Finally, the mass boundary is accurately segmented by the improved CV model. The validity of the proposed method is evaluated through two well-known digitized datasets (DDSM and MIAS). The performance of the method is evaluated with detection rate and area overlap. The results indicate the proposed scheme could obtain better performance when compared with several existing schemes.

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References

  1. Jemal, A., Bray, F., Melissa, M., Ferlay, J., Ward, E., Forman, D.: Global cancer statistics. CA Cancer J. Clin. 61(2), 69–90 (2011)

    Article  Google Scholar 

  2. Salmeri, M., Mencattini, A., Rabottino, G., Accattatis, A., Lojacono, R.: Assisted breast cancer diagnosis environment: a tool for dicom mammographic images analysis. In: IEEE International Workshop on Medical Measurements and Applications (MEMEA 2009), pp. 160–165 (2009)

    Google Scholar 

  3. Han, X., Xu, C., Prince, J.L.: A topology preserving level set method for geometric deformable models. IEEE Trans. Patt. Anal. Mach. Intell. 25(6), 755–768 (2003)

    Article  Google Scholar 

  4. Caselles, V., Catté, F., Coll, T.: Françoise Dibos.: a geometric model for active contours in image processing. Numer. Math. 66(1), 1–31 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  5. Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: a level set approach. IEEE Trans. Patt. Anal. Mach. Intell. 17(2), 158–175 (1995)

    Article  Google Scholar 

  6. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Imag. Proc. 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  7. Liu, J., Liu, X., Chen, J., Tang, J.: Mass segmentation in mammograms based on improved level set and watershed algorithm. In: Huang, D.-S., Gan, Y., Gupta, P., Gromiha, M. (eds.) ICIC 2011. LNCS, vol. 6839, pp. 502–508. Springer, Heidelberg (2012)

    Google Scholar 

  8. Li, C., Huang, R., Ding, Z., Gatenby, J.C., Metaxas, D.N., Gore, J.C.: A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans. Image Process. 20(7), 2007–2016 (2011)

    Article  MathSciNet  Google Scholar 

  9. Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S., Taylor, P., Betal, D., Savage, J.: The Mammographic Image Analysis Society digital mammogram database. In: International Workshop on Digital Mammography, pp. 211–221 (1994)

    Google Scholar 

  10. 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 (IWDM), pp. 212–218 (2001)

    Google Scholar 

  11. Mirghasemi, S., Rayudu, R., Zhang, M.: A new image segmentation algorithm based on modified seeded region growing and particle swarm optimization. In: 28th International Conference of Image and Vision Computing (IVCNZ), pp. 382–387 (2013)

    Google Scholar 

  12. Bhattacharya, S., Gupta, S., Subramanian, V.K.: Localized image enhancement. Twentieth National Conference on Communications (NCC), pp. 1–6 (2014)

    Google Scholar 

  13. Ma, Y.-D., Yuan, J.-X., Zhang, H.-J.: Self-adaptive method using SCM for noise removal in color images. J. Univ. Electron. Sci. Technol. China 41(5), 754–758 (2012)

    Google Scholar 

  14. Zhang, H., Fritts, J., Goldman, S.: Image segmentation evaluation: a survey of unsupervised methods. Comput. Vis. Image Underst. 110(2), 260–280 (2008)

    Article  Google Scholar 

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Acknowledgements

This work was jointly supported by the National Natural Science Foundation of China (Grant No.61175012), Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No.20110211110026), the Fundamental Research Funds for the Central Universities of China (Grant No. lzujbky-2013-k06 & -lzujbky-2015-197) and the Central Universities of China under Grant lzujbky-2015-196.

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Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu Province, China

    Xiaoli Gao, Keju Wang, Yanan Guo, Zhen Yang & Yide Ma

Authors
  1. Xiaoli Gao
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  2. Keju Wang
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  3. Yanan Guo
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  4. Zhen Yang
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  5. Yide Ma
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Corresponding author

Correspondence to Yide Ma .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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Gao, X., Wang, K., Guo, Y., Yang, Z., Ma, Y. (2015). Mass Segmentation in Mammograms Based on the Combination of the Spiking Cortical Model (SCM) and the Improved CV Model. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_62

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  • DOI: https://doi.org/10.1007/978-3-319-27863-6_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27862-9

  • Online ISBN: 978-3-319-27863-6

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