CFP last date
22 April 2024
Reseach Article

Comparison of Two Segmentation Methods for Mammographic Image

by Priyanka Jagya, R.B. Dubey
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 126 - Number 1
Year of Publication: 2015
Authors: Priyanka Jagya, R.B. Dubey
10.5120/ijca2015905979

Priyanka Jagya, R.B. Dubey . Comparison of Two Segmentation Methods for Mammographic Image. International Journal of Computer Applications. 126, 1 ( September 2015), 31-43. DOI=10.5120/ijca2015905979

@article{ 10.5120/ijca2015905979,
author = { Priyanka Jagya, R.B. Dubey },
title = { Comparison of Two Segmentation Methods for Mammographic Image },
journal = { International Journal of Computer Applications },
issue_date = { September 2015 },
volume = { 126 },
number = { 1 },
month = { September },
year = { 2015 },
issn = { 0975-8887 },
pages = { 31-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume126/number1/22518-2015905979/ },
doi = { 10.5120/ijca2015905979 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:16:19.877208+05:30
%A Priyanka Jagya
%A R.B. Dubey
%T Comparison of Two Segmentation Methods for Mammographic Image
%J International Journal of Computer Applications
%@ 0975-8887
%V 126
%N 1
%P 31-43
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Currently mammography is the method of choice for early detection of breast cancer. The image segmentation aims to separate the structure of interest objects from background and other objects. Detection of breast cancer is a very crucial step in mammograms and therefore needs an accurate and standard technique for breast tumor segmentation. In the last few years, a number of algorithms have been published in the literature. Each one has their own merits and de-merits. Fuzzy-level set and wavelet with level set is proposed to facilitate mammogram image segmentation. The existing active contour models can be classified as edge-based models and region-based model. In fuzzy level set, edge based active contour model is used while, in wavelet with level set, a region-based image segmentation technique using active contours with signed pressure force function is used. Furthermore, after evaluating various parameters wavelet with level set is considered to be better than fuzzy level set, as segmentation of mass area is more effective having less error value and it shows higher PSNR as compared to other method.

References
  1. R. Ramani, S. Suthanthiravanitha and S. Valarmathy, “A survey of current image segmentation techniques for detection of breast cancer” International Journal of Engineering Research and Applications (IJERA), vol. 2, Issue 5, pp. 1124-1129, 2012.
  2. D.L. Pharm, C. Xu, J. L. Prince, “current methods in medical image segmentation” Annual review of bio-medical engineering, 2000.
  3. R. Ramani, Dr. S. Suthanthiravanitha and S.Valarmathy, “A survey of current image segmentation techniques for detection of breast cancer” International Journal of Engineering Research and Applications (IJERA), vol. 2, Issue 5, pp.1124-1129, 2012.
  4. R. Sura. Shareef “Breast cancer detection based on watershed transformation” IJCSI International Journal of Computer Science Issues, vol. 11, Issue 1, no. 1, pp. 237-245, 2014.
  5. H. Moradmand, S. Setayeshi and H. K. Targhi “Comparing methods for segmentation of microcalcification clusters in digitized mammograms” IJCSI International Journal of Computer Science Issues, vol. 8, Issue 6, no.1, pp. 104-108,2011.
  6. Varsha J. Gaikwad “Marker controlled watershed transform in digital mammogram segmentation” International Journal for Research in Applied Science & Engineering Technology (IJRASET). vol. 3, pp. 18-21, 2015.
  7. S.ong yang Yu and Ling Guan “A CAD system for the automatic detection of clustered micro calcifications in digitized mammogram films” IEEE Transactions on Medical Imaging, vol. 19, no. 2, pp. 115-126,2000.
  8. Cheng, H. D., Shi, X. J., Min, R., Hu, L. M., Cai, X. P., and Du, H. N.“Approaches for automated detection and classification of masses in mammograms”Pattern Recognition 39, 646-668, 2006.
  9. Oliver, A., Freixenet, J., Marti, J., Perez, E., Pont, J., Denton, E., and Zwiggelaar, R. “A review of automatic mass detection and segmentation in mammographic images” Medical Image Analysis.14, 87-110, 2010.
  10. L.P. Clarke, R.P. Velthuizen, M.A. Camacho, J.J. Heine, M. Vaidyanathan, L.O. Hall, R.W. Thatcher and M.L. Silbiger, “MRI Segmentation: Methods and Applications” Magnetic Resonance Imaging, vol. 13, no. 3, pp. 343-368, 1995
  11. Z. A. Jaffery, Zaheeruddin and L. Singh “Performance analysis of image segmentation methods for the detection of masses in mammograms” International Journal of Computer Applications, Vol. 82, no.2, pp. 44-50, 2013.
  12. Dunn, J. 1974 “a fuzzy relative of the iso-dataprocess and its use in detecting compact well separated clusters” J.cybernectics.3, 32-57.
  13. Dominquez, J.Q., Magana, B.O., Januchs, M.G., Ruelas, R., Corona, A.V., Andina, D. “Image segmentation by fuzzy and possibility clustering algorithms for the identification of micro-calcification” Scientia Irenic D. 18,580-589, 2011.
  14. L, Y. C., Tsai, Y.P., Hung, Y.P., and Shih, Z.C., “Comparison between immersion based and toboggan based watershed image segmentation”IEEE Trans. Image Processing.15, 632-640, 2006.
  15. Vincent, L., Soille, P. “Watersheds in digital spaces: an efficient algorithm based on immersion simulations”IEEE Trans. Pattern and Machine Intelligence.13, 583-598, 1991.
  16. Adams, R., and Bishop, L, “Seeded region growing”.IEEE Trans. Pattern and Machine Intelligence.16 (6), 641-647, 1994.
  17. S. Sasikala, M. Ezhilarasi, P.Sudharsan, C.L.Yashwanthi Sivakumari “ Performance analysis of various segmentation techniques in breast mammogram images” International conference on intelligent computing Applications, 2014.
  18. Mohamed Ali HAMDI, K. S. Ettabaa and Mohamed Lamine HARABI “A new Mammography segmentation technique based on watershed, wavelet and curve let transform.” Computers, Automatic Control Signal Processing and Systems Science.
  19. S. Dalmiya, A. Dasgupta and S. K. Datta “Application of wavelet based K-means algorithm in mammogram segmentation” International Journal of Computer Applications,vol. 52, no.15, 2012.
  20. Gokila Deepa.G “Mammogram image segmentation using Fuzzy Hybrid with Particle Swarm Optimization(PSO)” International Journal of Engineering and Innovative Technology, vol. 2, 2011
  21. M. Cass, A. Wit kin, and D. Terzopoulos, “Snakes: active contour models,” International Journal of Computer Vision, vol. 1, no. 4, pp. 321–331, 1988.
  22. V. Casella’s, R. Kimmel, and G. Spiro, “Geodesic active contours,” International Journal of Computer Vision, vol. 22, no. 1, pp. 61–79, 1997.
  23. C. Li, C. Xu, C. Gui, and M. D. Fox, “Level set evolution without re-initialization: a new variation formulation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition , pp. 430–436, 2005.
  24. N. Paragios and R. Deriche, “Geodesic active regions and level set methods for supervised texture segmentation” International Journal of Computer Vision, vol.46, no.3, pp.223–247, 2002.
  25. T. F. Chan and L. A. Vase, “Active contours without edges,” IEEE Transactions on Image Processing, vol. 10, no. 2, pp. 266–277, 2001.
  26. K. Zhang, L. Zhang, H. Song, and W. Zhou, “Active contours with selective local or global segmentation: a new formulation and level set method,” Image and Vision Computing, vol. 28, no. 4, pp. 668–676, 2010.
  27. D. Mumford and J. Shah, “Optimal approximations by piecewise smooth functions and associated variation problems,” Communications on Pure and Applied Mathematics, vol. 42, no. 5, pp. 577–685, 1989.
  28. J. Lie, M. Lysaker, and X.-C. Tai, “A binary level set model and some applications to Mumford-Shah image segmentation,” IEEE Transactions on Image Processing, vol. 15, no. 5, pp. 1171–1181, 2006.
  29. L. A. Vase and T. F. Chan, “A multiphase level set framework for image segmentation using the Mumford and Shah model,” International Journal of Computer Vision, vol. 50, no. 3, pp. 271–293, 2002.
  30. D. Cremer’s, “A multiphase level set framework for motion segmentation,” in Scale Space Methods in Computer Vision: 4th International Conference, Scale Space 2003 Isle of Skye, UK, June 10–12, 2003Proceedings, vol. 2695 of Lecture Notes in Computer Science, pp. 599–614, Springer, Berlin, Germany, 2003.
  31. R. Ranford, “Region-based strategies for active contour models,” International Journal of Computer Vision, vol. 13,no. 2, pp. 229–251, 1994.
  32. C. Li, C.-Y. Kao, J. C. Gore, and Z. Ding, “Implicit active contours driven by local binary fitting energy,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '07), pp. 1–7, Washington, DC, USA, June 2007.
  33. H. Jiang, R. Feng, and X. Gao, “Level set based on signed pressure force function and its application in liver image segmentation,” Wuhan University Journal of Natural Sciences,vol. 16, no. 3, pp. 265–270,2011.
  34. J. Gomes and O. Faugeras, “Reconciling distance functions and level sets” Journal of Visual Communication and Image Representation, vol. 11, no. 2, pp. 209–223, 2000.
  35. F. Akram, J. H. Kim, C-G Lee and K. N. Choi “Segmentation of Regions of Interest Using Active Contours with SPF Function”, 2015.
  36. B. N. Li, C.K. Chui and S. H. Ong, “Integrating FCM and level set for liver tumor segmentation, in: proceedings of the 13th international conference on biomedical engineering, IFMBE Proceedings, vol. 23, pp. 202-205, 2009.
  37. J. S. Suri, K. Liu, S. Singh, S. N. laxminarayan, X. Zeng and L. Reden, “Shape recovery algorithm using level set in 2-D/3-D medical imaginary: a state of art review” IEEE Transactions on medical imaging 22, 773-776, 2003.
  38. N. Paragios , “A level set approach for shape –driven segmentation and tracking of left ventricle”IEEE Transaction on medical imaging 22,773-776, 2003.
  39. I. M. Mitchell, “The flexible, extensible and efficient toolbox of level set methods” Journal of scientific computing 35, 300-329, 2008.
  40. J. S. Suri, “ Two dimensional fast magnetic resonance brain segmentation” IEEE Engineering in Medicine and Biology Magazine 20, 84-95, 2001.
  41. S. Ho, E. Bullitt and G. Gerig, “level set evolution with region competition: automatic 3-D segmentation of brain tumours” in: Proceedings of the international conference on pattern Recognition, 532-535,2002.
  42. P. A. Yushkevich, J. Piven, H. C. Hazlett, R. G. Smith and S. Ho, J. C. Gee, et al, “user guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability”, NeuroImage, vol. 31, pp. 1116-1128, 2006.
  43. Bing, N. Li, C. K. Chui, S. Chang and S. H. Ong “Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation” Computers in Biology and Medicine, 2011.
  44. R. Ramani, S. Suthanthiravanitha and S. Valarmathy “ A survey of current image segmentation techniques for detection of breast cancer” International Journal of Engineering Research and Applications (IJERA), vol. 2, Issue 5, pp.1124-1129, 2012.
  45. D. L. Pharm, C. Xu and J. L. Prince “current methods in medical image segmentation” Annual review of bio-medical engineering, 2000.
  46. K. Levinski, A. Sourin and V. Zagorodnav “Interactive surface-guided segmentation of brain MRI data” Computers in Biology and Medicine, pp. 1153-1160, 2009.
  47. W. Cai, S. Chen and D. Zhang “Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation” Pattern recognition, pp. 825-838, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Segmentation fuzzy- level set wavelet with level set active contour region of interest.