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.
Keywords
- Spiking Cortical Model (SCM)
- Mass Segmentation
- Digital Database For Screening Mammography (DDSM)
- Mammographic Image Analysis Society (MIAS)
- MIAS Database
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Jemal, A., Bray, F., Melissa, M., Ferlay, J., Ward, E., Forman, D.: Global cancer statistics. CA Cancer J. Clin. 61(2), 69–90 (2011)
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)
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)
Caselles, V., Catté, F., Coll, T.: Françoise Dibos.: a geometric model for active contours in image processing. Numer. Math. 66(1), 1–31 (1993)
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)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Imag. Proc. 10(2), 266–277 (2001)
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)
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)
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)
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)
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)
Bhattacharya, S., Gupta, S., Subramanian, V.K.: Localized image enhancement. Twentieth National Conference on Communications (NCC), pp. 1–6 (2014)
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)
Zhang, H., Fritts, J., Goldman, S.: Image segmentation evaluation: a survey of unsupervised methods. Comput. Vis. Image Underst. 110(2), 260–280 (2008)
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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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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