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Reseach Article

Breast Cancer Mass Detection in Mammograms using K-means and Fuzzy C-means Clustering

by Nalini Singh, Ambarish G Mohapatra, Gurukalyan Kanungo
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 22 - Number 2
Year of Publication: 2011
Authors: Nalini Singh, Ambarish G Mohapatra, Gurukalyan Kanungo
10.5120/2557-3507

Nalini Singh, Ambarish G Mohapatra, Gurukalyan Kanungo . Breast Cancer Mass Detection in Mammograms using K-means and Fuzzy C-means Clustering. International Journal of Computer Applications. 22, 2 ( Feb 2011), 15-21. DOI=10.5120/2557-3507

@article{ 10.5120/2557-3507,
author = { Nalini Singh, Ambarish G Mohapatra, Gurukalyan Kanungo },
title = { Breast Cancer Mass Detection in Mammograms using K-means and Fuzzy C-means Clustering },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2011 },
volume = { 22 },
number = { 2 },
month = { Feb },
year = { 2011 },
issn = { 0975-8887 },
pages = { 15-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume22/number2/2557-3507/ },
doi = { 10.5120/2557-3507 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:08:21.595976+05:30
%A Nalini Singh
%A Ambarish G Mohapatra
%A Gurukalyan Kanungo
%T Breast Cancer Mass Detection in Mammograms using K-means and Fuzzy C-means Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 22
%N 2
%P 15-21
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Mammography is a special case of CT scan who adopts X-ray method & uses the high resolution film so that it can detect well the tumors in the breast. Low radiation is the strength of this method. Mammography is especially used only in the breast tumor detection Mammogram breast cancer images have the ability to assist physicians in detecting disease caused by cells normal growth. Developing algorithms and software to analyse these images may also assist physicians in there daily work. This study that shows the outcome of applying image processing threshold, edge based and watershed segmentation on mammogram breast cancer image and also presents a case study between them based on time consuming and simplicity. The real-time implementation of this paper can be implemented using data acquisition hardware and software interface with the mammography systems.

References
  1. “Detection of breast cancer tumor using mathematical morphology and wavelet analysis”, Mohiy Hadhoud, ohamed Amin, Walid Dabbour in GVIP 05 conference, 19-21 December 2005, CICC, Cairo, Egypt.
  2. Characterization of micro-calcifications and mass on female breast using processing in full field digital mammography (FFDM)”, Kanaga, K.C.1, Anandan, S.2, Chin, M.Y.1 & Laila, S.E.1, symposium sains kesihatan kebangsaan ke 7, hotel legend, Kuala Lumpur, 18-20 June 2008: 183-187.
  3. M. Bhattacharya & A. Das, “Fuzzy logic based segmentation of Micro calcification in Breast using Digital Mammograms considering Mutiresolution.
  4. M.J. Bottema, G.N.Lee and S.Lu, “Automatic image feature extraction for diagnosis and prognosis of breast cancer,” Artificial intelligence techniques in breast cancer diagnosis and prognosis, Series in machine perception and artificial intelligence, Vol.39, World Scientific Publishing Co.Pte.Ltd, 2000, pp. 17-54.
  5. T. C. Wang and N. B. Karayiannis, " Detection of microcalcifications in digital mammograms using wavel- ets", IEEE Transaction on Medical im aging, vol. 17, no. 4, pp. 498-509,AUGUST 1998. GVIP 05 Conference, 19-21 December 2005, CICC, Cairo, Egypt.
  6. Aijuan Dong and Baoying Wang “Feature selection and analysis on mammogram classification’’, Communications, Computers and Signal Processing, 2009. Pac Rim 2009. IEEE Pacific Rim Conference on 23-26 Aug. 2009.
  7. Thangavel, K.Mohideen, A.K. Dept. of Comp.Sci., Periyar Univ., Salem, India, “Semi-supervised k-means clustering for outlier detection in mammogram classification” appears in Trandz in information sciences & computing, (2010).
  8. Cahoon, T.C .Sutton, M .A. Bezdek “Brest cancer detection using image processing techniques”, J.C.Dept.of Comp.Sci.Univ.of West Florida, Pensacola, FL Fuzzy IEEE 2000.The ninth IEEE conference.
  9. “Microcalcification detection in digital mammograms based on wavelet analysis and neural networks” by Jasmine, J.S.L. Govardhan, A. Baskaran, S. Dept. of CSE, Velammal Eng. Coll., Chennai, India on Control, Automation, Communication and Energy Conservation, INCACEC 2009. International Conference.
  10. “A Edge Detection Method for Microcalfication Clusters in Mammograms” by Yu Guang Zhang Wen Lu Fu Yun Cheng Li Song Taishan Med.Univ. Taian, China in 2nd international conference on biomedical engineering and informatics, 2009.
  11. “Image feature extraction in the last screening mammograms prior to detection of breast cancer”by Sameti,M.;Ward,R.K.;Morgan-Parkes,J.;Palcic,B.;in IEEE journal 2009.
  12. Bajger, M.; Fei Ma; Williams, S.; Bottema, M.; “Mammographic mass detection with stastical region merging”on Digital image computing:Techniques & applications(DICTA) in 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Image processing CT scan Low Radiation Watershed Image Segmentation Data acquisition Mammography X-ray