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Article:An Adaptive K-means Clustering Algorithm for Breast Image Segmentation

by Bhagwati Charan Patel, Dr. G.R.Sinha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Number 4
Year of Publication: 2010
Authors: Bhagwati Charan Patel, Dr. G.R.Sinha
10.5120/1467-1982

Bhagwati Charan Patel, Dr. G.R.Sinha . Article:An Adaptive K-means Clustering Algorithm for Breast Image Segmentation. International Journal of Computer Applications. 10, 4 ( November 2010), 35-38. DOI=10.5120/1467-1982

@article{ 10.5120/1467-1982,
author = { Bhagwati Charan Patel, Dr. G.R.Sinha },
title = { Article:An Adaptive K-means Clustering Algorithm for Breast Image Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { November 2010 },
volume = { 10 },
number = { 4 },
month = { November },
year = { 2010 },
issn = { 0975-8887 },
pages = { 35-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume10/number4/1467-1982/ },
doi = { 10.5120/1467-1982 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:58:53.677208+05:30
%A Bhagwati Charan Patel
%A Dr. G.R.Sinha
%T Article:An Adaptive K-means Clustering Algorithm for Breast Image Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 10
%N 4
%P 35-38
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Breast cancer is one of the major causes of death among women. Small clusters of micro calcifications appearing as collection of white spots on mammograms show an early warning of breast cancer. Early detection performed on X-ray mammography is the key to improve breast cancer diagnosis. In order to increase radiologist’s diagnostic performance, several computer-aided diagnosis (CAD) schemes have been developed to improve the detection of primary identification of this disease. In this paper, an attempt is made to develop an adaptive k-means clustering algorithm for breast image segmentation for the detection of micro calcifications and also a computer based decision system for early detection of breast cancer. The method was tested over several images of image databases taken from BSR APPOLO for cancer research and diagnosis, India. The algorithm works faster so that any radiologist can take a clear decision about the appearance of micro calcifications by visual inspection of digital mammograms and detection accuracy has also improved as compared to some existing works.

References
  1. Garcia, M., Jemal, 2007. A., Ward, E., Center, M., Hao, Y., Siegel, R., and Thun, M. Global Cancer Facts & Figures 2007, American Cancer Society.
  2. Network of Strength. Breast Cancer Statistics. 2009. http://www. networkofstrength.org/i nformation/bcnews/stats.php.
  3. Cheng, H.D., Cai, X., Chen, X., Hu, L., and Lou, X. 2003 Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognition 36, vol. 12, p. 2967-2991.
  4. American Cancer Society. Jan. 2010. What Are the Key Statistics for Breast Cancer? http://www.cancer.org/docroot/cri/content/cri_2_4_1x_what_are_the_key_statistics_for_breast_cancer_5.asp.
  5. Laine, H.,Rainio, J., Arko, H. and Tukeva, T. 1995. Comparison of breast structure and findings by X-ray mammography, ultrasound, cytology and histology: a retrospective study. European Journal of Ultrasound 2, vol. 2 p. 107-115
  6. Hieken, T., Harrison, J. Herreros, J., and Velasco, J.2001. Correlating sonography, mammography, and pathology in the assessment of breast cancer size. American Journal of Surgery 182, vol. 4 , p. 351-354.
  7. Saarenmaa, I., Salminen, T., Geiger, U. Heikkinen, P., Hyv rinen, S., Isola, J., Kataja, V., Kokko, M., Kokko, R., and Kumpulainen, E.2001. The effect of age and density of the breast on the sensitivity of breast cancer diagnostic by mammography and ultasonography.Breast Cancer Research and Treatment 67, vol. 2 , p. 117-123.
  8. Wikipedia.Medicalimaging.2010. http://en.wikipedia.org/wiki/Medical/imageprocessingultrasound
  9. R. Mousa, Q. Munib, and A. Moussa,2005.Breast Cancer Diagnosis System based on Wavelet Analysis and Fuzzy-Neural, Expert Systems with Applications, vol. 28, pp. 713-723.
  10. S. K. Lee, C-S. Lo, C-M. Wang, and P-C. Chung, 2000 .A Computer-Aided Design Mammography Screening System for Detection and Classification of Microcalcifications.” International Journal of Medical Informatics, vol. 60, pp. 29-57.
  11. Jianbo Shi & Jitendra Malik (1997) Normalized Cuts and Image Segmentation, Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 731-737.
  12. T. Kanungo, D. M. Mount, N. Netanyahu, C. Piatko, R. Silverman, & A. Y.Wu (2002) An efficien k-means clustering algorithm: Analysis and implementation Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp.881-892.
  13. Lloyd, S. P. (1957).Least square quantization in PCM. Bell Telephone Laboratories Paper. Published in journal much later: Lloyd., S. P. (1982). Least squares quantization in PCM . IEEE Transactions on Information Theory, vol. 28 (2), p. 129–137. http://www.cs.toronto.edu/~roweis/csc2515-2006/readings/lloyd57.pdf.
  14. Kanungo, T.; Mount, D. M.; Netanyahu, N. S.; Piatko, C. D.; Silverman, R.; Wu, A. Y. 2002. "An efficient k-means clustering algorithm: Analysis and implementation". IEEE Trans. Pattern Analysis and Machine Intelligence 24, p. 881–892. http://www.cs.umd.edu/~mount/Papers/pami02.pdf.
Index Terms

Computer Science
Information Sciences

Keywords

K-mean breast image segmentation detection CAD

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