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Contrast Enhancement of Breast MRI Images Based on Fuzzy Type-II

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Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 87))

Abstract

The high incidence of breast cancer in women has increased significantly in the recent years. Breast MRI involves the use of magnetic resonance imaging to look specifically at the breast. Contrast-enhanced breast MRIs acquired by contrast injection have been shown to be very sensitive in the detection of breast cancer, but are also time-consuming and cause waste of medical resources. This paper utilizes the use of type-II fuzzy sets to enhance the contrast of the breast MRI image. To evaluate the performance of our approach, we run tests over different MRI breast images and show that the overall accuracy offered by the employed approach is high.

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References

  1. Hassanien, A., Al-Qaheri, H., El-Dahshan El-Sayed, A.: Prostate boundary detection in ultrasound images using biologically-inspired spiking neural network. Applied Soft Computing (2010), doi:10.1016/j.asoc.2010.07.001

    Google Scholar 

  2. Hassanien, A.: Fuzzy-rough hybrid scheme for breast cancer detection. Image and Computer Vision Journal 25(2), 172–183 (2007)

    Article  Google Scholar 

  3. Hassanien, A., Abraham, A., Peters, J.F., Schaefer, G., Henry, C.: Rough sets and near sets in medical imaging:areview. IEEE Trans. Inform.Technol. Biomed. 13(6), 955–968 (2009)

    Article  Google Scholar 

  4. Hassanien, A.: Intelligence techniques for prostate ultrasound image analysis. Int. J. Hybrid Intell. Syst. 6(3), 155–160 (2009)

    Google Scholar 

  5. Schaefer, G., Hassanien, A., Jiang, J.: Computational Intelligence in Medical Imaging Techniques and Applications. CRC Press, Boca Raton (2008)

    Google Scholar 

  6. De Martini, W.B., Lehman, C.D., Peacock, S., Russell, M.T.: Computer-Aided Detection Applied to Breast MRI: Assessment of CAD-Generated Enhancement and Tumor Sizes in Breast Cancers Before and After Neoadjuvant Chemotherapy. Academic Radiology 12(7), 806–814 (2005)

    Article  Google Scholar 

  7. Wendy, D., Constance, L., Savannah, P.: Breast MRI for Cancer Detection and Characterization: A Review of Evidence-Based Clinical Applications. Academic Radiology 15(4), 408–416 (2008)

    Article  Google Scholar 

  8. Maryellen Giger, L.: Computerized analysis of images in the detection and diagnosis of breast cancer. Seminars in Ultrasound, CT, and MRI 25(5), 411–418 (2004)

    Article  Google Scholar 

  9. Al-Manea, A., El-Zaart, A.: Contrast Enhancement of MRI Images. In: The 3rd Kuala Lumpur IFMBE Proceedings of the International Conference on Biomedical Engineering, 15, Part 8, pp. 255–258 (2007)

    Google Scholar 

  10. Ensafi, P., Tizhoosh, H.R.: Type-2 fuzzy image enhancement. In: Kamel, M.S., Campilho, A.C. (eds.) ICIAR 2005. LNCS, vol. 3656, pp. 159–166. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

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© 2011 Springer-Verlag Berlin Heidelberg

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Hassanien, A.E., Soliman, O.S., El-Bendary, N. (2011). Contrast Enhancement of Breast MRI Images Based on Fuzzy Type-II. In: Corchado, E., Snášel, V., Sedano, J., Hassanien, A.E., Calvo, J.L., Ślȩzak, D. (eds) Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011. Advances in Intelligent and Soft Computing, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19644-7_9

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  • DOI: https://doi.org/10.1007/978-3-642-19644-7_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19643-0

  • Online ISBN: 978-3-642-19644-7

  • eBook Packages: EngineeringEngineering (R0)

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