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A Novel Approach Towards Detection and Identification of Stages of Breast Cancer

  • M. Varalatchoumy
  • M. Ravishankar
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 43)

Abstract

A robust and efficient CAD system to detect and classify breast cancer at its early stages is an essential requirement of radiologists. This paper proposes a system that detects, classifies and also recognizes the stage of the detected tumor which helps radiologists in reducing false positive predictions. A MRM image is preprocessed using histogram equalization and dynamic thresholding approach. Segmentation of the preprocessed image is carried out using a novel hybrid approach, which is a hybridization of PSO and K-Means clustering. Fourteen textural features are extracted from the segmented region in order to classify the tumor using Artificial Neural Network. If the tumor is classified as malignant then the stage of the tumor is identified using size as a key parameter.

Keywords

Robust and efficient CAD system Histogram equalization and dynamic thresholding Novel hybrid approach of PSO and K-Means clustering Textural features Artificial neural network Size of tumor 

References

  1. 1.
    Tang, J., Rangayyan, R.M., Xu, J., El Naqa, I.: Computer aided detection and diagnosis of breast cancer with mammography: recent advances. IEEE Trans. Inf. Technol. Biomed. 13 (2009)Google Scholar
  2. 2.
    Nagi, J., Kareem, S.A., Nagi, F., Ahmed, S.K.: Automated breast profile segmentation for ROI detection using digital mammograms. IEEE EMBS Conf. Biomed. Eng. Sci. (2010)Google Scholar
  3. 3.
    Ganesan, K., Acharya, U.R., Chua, C.K., Min, L.C.: Computer-aided breast cancer detection using mammograms: a review. IEEE Rev. Biomed. Eng. 6, 77–98 (2013)Google Scholar
  4. 4.
    Narayan Ponraj, D., Evangeline Jenifer, M., Poongodi, P., Samuel Manoharan, J.: A survey on the preprocessing techniques of mammogram for the detection of breast cancer. J. Eng. Trends Comput. Inf. Sci. 2, (2011)Google Scholar
  5. 5.
    Egmont-Petersen, M., de Ridder, D., Handels, H.: Image processing with neural networks-a review. Pattern Recogn. 35, 2279–2301 (2002)Google Scholar
  6. 6.
    Schaefer, G., Zavisek, M., Nakashima, T.: Thermography based breast cancer analysis using statistical features and fuzzy classification. Pattern Recogn. 47, 1133–1137 (2009)CrossRefGoogle Scholar
  7. 7.
    Kocur, C.M., Rogers, S.K., Myers, L.R.: Thomas burns: using neural networks to select wavelet featuers for breast cancer diagnosis. IEEE Eng. Med. Biol. 0739–5175 (1996)Google Scholar
  8. 8.
    Hamed, N.B., Taouil, K., Bouhlel, M.S.: Exploring wavelets towards an automatic microclacification detection in breast cancer. In: IEEE (2006)Google Scholar
  9. 9.
    Malek, J., Sebri, A., Mabrouk, S.: Automated breast cancer diagnosis based on GVF-Snake segmentation, wavelet features extraction and fuzzy classification. J. Sign. Process Syst. (2008)Google Scholar
  10. 10.
    Gopi Raju, N., Nageswara Rao, P.: Particle swarm optimization method for image segmentation applied in mammography. Int. J. Eng. Res. Appl. 3, (2013)Google Scholar
  11. 11.
    Tandan, A., Raja, R., Chouhan, Y.: Image segmentation based on particle swarm optimization. Int. J. Sci. Eng. Technol. Res. 3(2), (2014)Google Scholar
  12. 12.
    Mohessen, F., Hadhoud, M., Mostafa, K., Amin, K.: A new image segmentation method based on particle swarm optimization. Int. Arab J. Inf. Technol. 9, (2012)Google Scholar
  13. 13.
    Ghamisi, P., Couceiro, M.S., Benediktsson, J.A.: An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Syst. Appl. (2012)Google Scholar
  14. 14.
    Haralick, R.M., Shanmugam, K.: Textural features for image classification. IEEE Trans.Google Scholar
  15. 15.
    Guardiola, M., Capdevila, S., Romeu, J., Jofre, L.: 3-D microwave magnitude combined tomography for breast cancer detection using realistic breast models. IEEE Antennas Wirel. Propogat. Lett. 11, 1622–1625 (2012)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Information Science and EngineeringDayananda Sagar College of EngineeringBangaloreIndia
  2. 2.Vidya Vikas Institute of Engineering and TechnologyMysoreIndia

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