Advertisement

Accurate Classification of Cancer in Mammogram Images

  • M. Parisa BehamEmail author
  • R. Tamilselvi
  • S. M. Mansoor Roomi
  • A. Nagaraj
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 65)

Abstract

In the last decade, machine learning plays a vital role in the detection of breast cancer. Mammography is a proficient tool for early stage detection of breast cancer. In this work, a simple technique for breast cancer image classification in l mammogram images is proposed. Highly discriminant local binary patterns are extracted from the wavelet normalized mammogram images. K-nearest neighbor classifier is used to categorize the abnormal cancer cell images. A mammogram database is created to evaluate the efficacy of our algorithm. From the experimental results, the performance of our algorithms is comparatively good with very less computational time.

Keywords

Mammogram database Cancer cell detection Benign and malignant LBP K-NN classifier 

Notes

Ethical Approval

The mammogram database used in this paper is provided by Pixel scans, Trichy. The ethical committee of Pixel scans has reviewed and approved to conduct research using this mammogram database and publish papers based on the results using that biomedical images.

References

  1. 1.
    Sapate S, Talbar S et al. (2005) Pectoral muscle extraction algorithms applied to digital mammograms. Institution Politecnico Nacional, Centro de Innovation y Desarrollo Technology en Computer, Mexico 7(2)3:5–6Google Scholar
  2. 2.
    Sivakumar R et al. (2007) Diagnose breast cancer through mammogram enhancement method and nipple position. Inf Commun Technol Res Mumbai 8(3)Google Scholar
  3. 3.
    Maitra IK et al. (2007) Automated digital mammogram segmentation for detection of abnormal masses. Indian J Comput Sci Eng (IJCSE) 1(1). ISSN 2007-3689Google Scholar
  4. 4.
    Nithya R et al. (2011) Improving performance of breast cancer detection for mammography image. Int J Adv Comput Sci Appl 25(5)Google Scholar
  5. 5.
    Percha B et al. (2012) Computer aided detection algorithm for digital mammogram images. Int J Comput Trends Technol (IJCTT) 2(2)Google Scholar
  6. 6.
    Rubin D et al. (2013) Identification of abnormal masses in digital mammography images. In: 13th International Arab conference on information technology ACIT 2013Google Scholar
  7. 7.
    Angayarkanni N et al (2014) The application of image processing techniques for detection and classification of cancerous tissue in digital mammograms. J Pharm Sci Res 8(10):1179–1183Google Scholar
  8. 8.
    Kuzmiak et al. CM (2000) Automatic classification of mammography report by bi-rad breast tissue composition class. University of North Carolina, USA, 4(6)Google Scholar
  9. 9.
    Gonzalez-Patino D, Villuendas-Rey Y, Argüelles-Cruz AJ (2015) Mammogram image segmentation using bioinspired novel bat swarm clustering. Instituto Politecnico Nacional, Centro de Investigation en Computation, Mexico City, Mexico 67(2):12–16Google Scholar
  10. 10.
    Lipson J (2016) An efficient image processing methods for mammogram breast cancer detection. J Theor Appl Inf Technol 69(1)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • M. Parisa Beham
    • 1
    Email author
  • R. Tamilselvi
    • 1
  • S. M. Mansoor Roomi
    • 2
  • A. Nagaraj
    • 1
  1. 1.Department of ECESethu Institute of TechnologyVirudhunagarIndia
  2. 2.Department of ECEThiagarajar College of EngineeringMaduraiIndia

Personalised recommendations