IJMLC 2012 Vol.2(1): 7-12 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.81

GUI Based Automatic Breast Cancer Mass and Calcification Detection in Mammogram Images using K-means and Fuzzy C-means Methods

Nalini Singh, Ambarish G Mohapatra, Biranchi Narayan Rath, and Guru Kalyan Kanungo

Abstract—Mammogram breast cancer images have the ability to assist physicians in detecting brest cancer caused by cells abnormal growth.The first step of the cancer signs detection should be a segmentation procedure able to distinguish masses and micro calcifications from background tissue. This study is an attempt to reduce false alarm in Breast cancer detection. This paper presents a research on mammography images using K-means and Fuzzy C – means clustering for detecting cancer tumor mass and micro calcification. The proposed technique shows better results in less time (in Seconds) and user friendly as it is based on Graphical User Interfaces(GUI). The real time implementation of the proposed method can be implemented using data acquisition hardware and software interface with the mammography systems.

Index Terms—Clustering, Fuzzy C–means, GUI, K-means, Mammography, Segmentation.

Nalini Singh, Ambarish G Mohapatra, Biranchi Narayan Rath and Guru Kalyan Kanungo are with the Silicon Institute of Technology, Silicon Hills, Patia, Bhubaneswar-751024, India (email: nalini.singh@silicon.ac.in; ambarish.mohapatra@silicon.ac.in; biranchi.rath@silicon.ac.in; gurukalyan@rocketmail.com).

[PDF]

Cite: Nalini Singh, Ambarish G Mohapatra, Biranchi Narayan Rath, and Guru Kalyan Kanungo, "GUI Based Automatic Breast Cancer Mass and Calcification Detection in Mammogram Images using K-means and Fuzzy C-means Methods," International Journal of Machine Learning and Computing vol. 2, no. 1, pp. 7-12, 2012.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net