CFP last date
22 April 2024
Reseach Article

Breast Density Segmentation based on Fusion of Super Pixels and Watershed Transform

by Abdulrasaq Surajudeen, Zwiggelaar Reyer
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 161 - Number 12
Year of Publication: 2017
Authors: Abdulrasaq Surajudeen, Zwiggelaar Reyer
10.5120/ijca2017913208

Abdulrasaq Surajudeen, Zwiggelaar Reyer . Breast Density Segmentation based on Fusion of Super Pixels and Watershed Transform. International Journal of Computer Applications. 161, 12 ( Mar 2017), 1-7. DOI=10.5120/ijca2017913208

@article{ 10.5120/ijca2017913208,
author = { Abdulrasaq Surajudeen, Zwiggelaar Reyer },
title = { Breast Density Segmentation based on Fusion of Super Pixels and Watershed Transform },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 161 },
number = { 12 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume161/number12/27197-2017913208/ },
doi = { 10.5120/ijca2017913208 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:07:16.367057+05:30
%A Abdulrasaq Surajudeen
%A Zwiggelaar Reyer
%T Breast Density Segmentation based on Fusion of Super Pixels and Watershed Transform
%J International Journal of Computer Applications
%@ 0975-8887
%V 161
%N 12
%P 1-7
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Breast density, defined as the proportion of fibroglandular tissue over the entire breast has been linked with a higher risk of developing breast cancer, in fact it has been suggested that women with a mammographic breast density higher than 75 percent have a four-to six-fold higher risk of developing breast cancer than women with little or no dense tissue. Therefore, automatic methods of measuring breast density could potentially aid clinicians to provide more precise breast cancer risk estimates.This paper proposes a novel method of segmenting breast density, which extracts objects with the same density using fusion of super pixels and a watershed based technique, this idea is based on the principle that both super pixel and watershed often results in over segmentation, for the later algorithm, over segmentation may be due to contours which have been suppressed according to similarity of contrast and topological measures, we took advantage of super pixel to consolidate space information and efficiently process the intensity non-homogeneity problem, afterward, re-introduced this contour with watershed transform to get a better segmentation.

References
  1. V.A. McCormack, I. dos Santos Silva. Breast density and parenchyma patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidermal Biomarkers Prev. 15: 1159- 69, 2006
  2. J.N.Wolfe. Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer, 37:24862492, 1976.
  3. W.He, A.Juette, E.R.E.Denton, A.Oliver, R.Mart, R.Zwiggelaar. A review on automatic mammographic density and parenchymal segmentation. International Journal of Breast Cancer 2015, Article ID 276217, http://dx.doi.org/10.1155/2015/276217, 2016.
  4. N.F. Boyd, H. Guo, L.J. Martin, L.M. Sun, J. Stone, E.R. Fishell, R.A. Jong, G. Hislop, A. Chiarelli, S. Minkin, J.A. Yaffe. Mammographic density and the risk and detection of breast cancer. New England Journal of Medicine. 2007 356:227-236.
  5. L.Pei, W.N Li. Pectoral muscle segmentation in mammograms based on snake model optimized by super pixel. Guangdianzi Jiguang Journal of Optoelectronics 26(8):1633- 1638.
  6. S.Ji, B.Wei, Z.Yu, G.Yang, Y.Yin. A new multistage medical segmentation method based on super pixel and fuzzy clustering. Computational and Mathematical Methods in Medicine 2014, Article ID 747549.
  7. J. Massich, G. Lemaitre, J.Marti, F.Meriaudeau. Breast Ultra- Sound image segmentation an optimization approach based on super-pixels and high-level descriptors. International Conference on quality control and artificial vision (QCAV) 2015, 2015, LeCreusot, France.
  8. J.Mahapatra. Cardiac image segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors Society for Imaging Informatics in Medicine 2013. 227(6): 794804. Published online 2014 June doi:10.1007/s10278-014-9705-0, 2014.
  9. R. Prabha, C. Kohila. Lazy random walks for super pixel segmentation. Karpagam Journal of Engineering Research (KJER) Volume No: II, Special Issue on IEEE Sponsored International Conference on Intelligent Systems and Control (ISCO15).
  10. X. Ren, J. Malik. Learning a classification model for segmentation. Proceedings of the International Conference on Computer Vision, pages 10 17, 2003.
  11. P.F.Felzenszwalb, D.P.Huttenlocher. Efficient graph-based image segmentation. D.P. International Journal of Computer Vision (2004) 59:167. doi:10.1023/B:VISI.0000022288.19776.77.
  12. A. Levinshtein, A. Stere, K. Kutulakos, D. Fleet, S. Dickinson, K.Siddiqi. Turbopixels: fast super pixels using geometric flows. IEEE Trans. Pattern Analysis and Machine Intelligence, 31,(12), pp. 2290-2297, 2009.
  13. S.Beucher. The Watershed Transform Applied to Image Segmentation. Proceedings of the Pfefferkorn Conference on Signal and Image Processing in Microscopy and Microanalysis, pp. 299314, 1991.
  14. R. Achanta, A.Shaji, K. Smith, A. Lucchi, P. Fua super pixels compared to state-of-the-art super pixel methods.IEEE Transactions on Pattern Analysis and Machine Intelligence, 34 (11), pp: 2274-2282 2012.
  15. H. Digabel, C. Lantuejoul. Iterative Algorithms. Proceedings of the 2nd European Symposium Quantitative Analysis of Microstructures in Material Science, Biology and Medicine, 85- 89, 1998.
  16. R. C. Gonzalez and R. E. Woods. Digital Image Processing. Third Edition, 2008.
  17. J. Suckling, J. Parker, D. Dance, S. Astley, I. Hutt, C. Boggis, I. Ricketts, E. Sta-matakis, N. Cerneaz, S. Kok, P. Taylor, D. Betal, J. Savage The mammographic images analysis society digital mammogram database.in: Dance, Gale, Astley, Gairns (Eds.), Excerpta Medica. International Congress Series, Vol. 1069, Elsevier, 1994, pp. 375378
  18. W.E.He, K.Stafford, E.R.E.Denton, R.Zwiggelaar. Mammographic image segmentation and risk classification based on mammographic parenchymal patterns and geometric moments. Biomedical Signal Processing and Control 6 (2011) 321 329.
Index Terms

Computer Science
Information Sciences

Keywords

Watershed Super pixel Mammograms Segmentation