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Accurate Breast Contour Detection Algorithms in Digital Mammogram

by Indra Kanta Maitra, Dr.S.Sumathi, Samir K Bandyopadhyay
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 25 - Number 5
Year of Publication: 2011
Authors: Indra Kanta Maitra, Dr.S.Sumathi, Samir K Bandyopadhyay
10.5120/3031-4109

Indra Kanta Maitra, Dr.S.Sumathi, Samir K Bandyopadhyay . Accurate Breast Contour Detection Algorithms in Digital Mammogram. International Journal of Computer Applications. 25, 5 ( July 2011), 1-13. DOI=10.5120/3031-4109

@article{ 10.5120/3031-4109,
author = { Indra Kanta Maitra, Dr.S.Sumathi, Samir K Bandyopadhyay },
title = { Accurate Breast Contour Detection Algorithms in Digital Mammogram },
journal = { International Journal of Computer Applications },
issue_date = { July 2011 },
volume = { 25 },
number = { 5 },
month = { July },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume25/number5/3031-4109/ },
doi = { 10.5120/3031-4109 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:10:56.163207+05:30
%A Indra Kanta Maitra
%A Dr.S.Sumathi
%A Samir K Bandyopadhyay
%T Accurate Breast Contour Detection Algorithms in Digital Mammogram
%J International Journal of Computer Applications
%@ 0975-8887
%V 25
%N 5
%P 1-13
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Computer Aided Diagnosis (CAD) systems have improved diagnosis of abnormalities in mammogram images. The principal feature within the breast region is the breast contour. Extraction of the breast region and delineation of the breast contour allows the search for abnormalities to be limited to the region of the breast without undue influence from the background of the mammogram. After performing an essential pre-processing step to suppress artifacts and accentuate the breast region, the exact breast region as the region of interest (ROI), has to be segmented. In this paper we present a fully automated segmentation and boundary detection method for mammographic images. In this research paper we have proposed a new homogeneity enhancement process namely Binary Homogeneity Enhancement Algorithm (BHEA) for digital mammogram. This is followed by a novel approach for edge detection (EDA) and finally obtaining the breast boundary by using our proposed Breast Border Boundary Enhancement Algorithm. This composite method have been implemented and applied to mini-MIAS, one of the most well-known mammographic database consisting of 322 medio-lateral oblique (MLO) view obtained via a digitization procedure. To demonstrate the capability of our segmentation algorithm it was extensively tested on mammograms using ground truth images and quantitative metrics to evaluate its performance characteristics. The experimental results indicate that the breast boundary regions were extracted accurately characterize the corresponding ground truth images. The algorithm is fully autonomous, and is able to preserve, skin and nipple (if in profile), a task very few existing mammogram segmentation algorithms can claim.

References
  1. Norum J., "Breast cancer screening by mammography in Norway. Is it cost-effective?", Annals of Oncology, Volume 10, Issue 2, pp. 197-203, 1999.
  2. Breast Cancer Facts & Figures, 2009-2010, American Cancer Society.
  3. Sterns EE, “Relation between clinical and mammographic diagnosis of breast problems and the cancer/ biopsy rate,” Can J Surg. 1996 Apr; 39(2):128-32.
  4. Highnam R and Brady M, “Mammographic Image Analysis”, Kluwer Academic Publishers, 1999. ISBN: 0-7923- 5620-9.
  5. Kekre HB, Sarode Tanuja K and Gharge Saylee M, "Tumor Detection in Mammography Images using Vector Quantization Technique", International Journal of Intelligent Information Technology Application, 2009, 2(5):237-242
  6. National Cancer Institute (NCI) Web site, http://www.cancernet.gov
  7. Suckling J., Parker J., Dance D.R., Astley S., Hutt I., Boggis C.R.M., Ricketts I., Stamatakis E., Cernaez N., Kok S.L., Taylor P., Betal D., Savage J., "The Mammographic Image Analysis Society Digital Mammogram Database", Proceedings of the 2nd International Workshop on Digital Mammography, York, England, 10–12 July 1994, Elsevier Science, Amsterdam, The Netherlands, pp. 375-378.
  8. Heath M., Bowyer K., Kopans D., Moore R., Kegelmeyer P. Jr., "The Digital Database for Screening Mammography", Proceedings of the 5th International Workshop on Digital Mammography, Toronto, Canada, 11-14 June 2000, Medical Physics Publishing, 2001, pp. 212-218.
  9. U. Bick, M.L. Giger, R.A. Schmidt, R.M. Nishikawa, D.E. Wolverton, and K. Doi. “Automated Segmentation of Digitized Mammograms”, Academic Radiology, 2(2):1-9. 1995.
  10. F.-F. Yin, M.L. Giger, K. Doi, C.E. Metz, C.J. Vyborny, R.A. and Schmidt, “Computerized Detection of Masses in Digital Mammograms: Analysis of Bilateral Subtraction Images”, Medical Physics, 18(5):955-963. 1991.
  11. A.J. Mendez, P.J. Tahoces, M.J. Lado, M. Souto, J.L. Correa, and J.J. Vidal, “Automatic Detection of Breast Border and Nipple in Digital Mammograms”, Computer Methods and Programs in Biomedicine, 49:253-262. 1996.
  12. R. Chandrasekhar, and Y. Attikiouzel, “Automatic Breast Border Segmentation by Background Modeling and Subtraction”, In proceedings of the 5th ICGST-GVIP Journal, Volume 5, Issue2, Jan. 2005 International Workshop on Digital Mammography, 560-565. 2000.
  13. M.A. Wirth, and A. Stapinski, “Segmentation of the Breast Region in Mammograms using Active Contours”, In proceedings of Visual Communications and Image Processing, 5150:1995-2006. 2003.
  14. J.L. Semmlow, A. Shadagopappan, L.V. Ackerman, W. Hand, and F.S. Alcorn., “A Fully Automated System for Screening Xeromammograms”, Computers and Biomedical Research. 13:350-362. 1980.
  15. M. Masek, Y. Attikiouzel, and C.J.S. deSilva Skinair, “Interface Extraction from Mammograms Using an Automatic Local Thresholding Algorithm”, In proceedings of the 15th Biennial International Conference Biosignal, 204-206. 2000.
  16. M. Abdel-Mottaleb, C.S. Carman, C.R. Hill, and S.Vafai, “Locating the Boundary between the Breast Skin Edge and the Background in Digitized Mammograms”, In proceedings of the 3rd International Workshop on Digital Mammography, 467-470. 1996.
  17. S.L. Lou, H.D. Lin, K.P. Lin, and D. Hoogstrate, “Automatic Breast Region Extraction from Digital Mammograms for PACS and Telemammography applications”, Computerized Medical Imaging and Graphics, 24:205-220. 2000.
  18. K.J. McLoughlin, and P.J. Bones, “Location of the Breast-air Boundary for a Digital Mammogram Image”, In proceedings of Image and Vision Computing . 2000.
  19. J. Liang, T. McInerney, and D. Terzopoulos, “United Snakes”, In proceedings of the IEEE International Conference on Computer Vision, 933-940. 1999.
  20. T. Ojala, J. Liang, J. Näppi, and O. Nevalainen, “Interactive Segmentation of the Breast Region from Digitized Mammograms with United Snakes”, Technical Report 315. University of Turku, Turku, Finland. 1999.
  21. M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active Contour Models”, International Journal of Computer Vision, 1:321-331. 1998.
  22. T. Ojala, J. Näppi, and O. Nevalainen, “Accurate Segmentation of the Breast Region from Digitized Mammograms. Computerized”, Medical Imaging and Graphics, 25(1):47-59. 2001.
  23. D.J. Williams, and M. Shah, “A Fast Algorithm for Active Contours and Curvature Estimation”, CVGIP: Image Understanding, 55(1):14-26. 92.
  24. R. Chandrasekhar, and Y. Attikiouzel, “Gross Segmentation of Mammograms using a Polynomial Model”, In proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, 1056-1058. 1996.
  25. M. Wirth, D. Nikitenko, J.Lyon, “Segmentation of the Breast Region in Mammograms using a Rule-Based Fuzzy Reasoning Algorithm”, ICGST- GVIP Journal, 5(2): 45-54. 2005.
  26. M.Masek, Electronic University of Western Australia, School of Electrical, Computer Engineering and University of Western Australia Centre for Information Processing Systems. “Hierarchical Segmentation of Mammograms Based on Pixel Intensity”. PhD thesis, 2004.
  27. A.D.Brink and N.E.Pendock, “Minimum Cross-entropy Threshold Selection, Pattern Recognition”, 29(1): 179-188, 1996.
  28. Gonzalez, R.C., Woods, R.E. (1992), “Digital image processing”.
  29. S.M. Pizer, “Psychovisual issues in the display of medical images”, Hoehne KH (ed), Pictoral Information systems in Medicine, Berlin, Germany, Springer-Verlag, 1985, PP 211-234.
  30. Pisano ED, et al, “Contrast Limited Adaptive Histogram Equalization Image Processing to Improve the Detection of Simulated Spiculations in Dense Mammograms”, Journal of Digital Imaging, Publisher Springer New York, Issue 11(4): 193-200, 1998.
  31. Wanga X, Wong BS, Guan TC, “Image enhancement for radiography inspection”, International Conference on Experimental Mechanics, 2004, pp 462-468.
  32. Ball JE, “Digital mammogram spiculated mass detection and spicule segmentation using level sets”, Proceedings of the 29th Annual International Conference of the IEEE EMBS, 2007, pp. 4979-4984.
  33. Antonis Daskalakis, et al, “An Efficient CLAHE-Based, Spot-Adaptive, Image Segmentation Technique for Improving Microarray Genes' Quantification”, 2nd International Conference on Experiments/Process/System Modelling/Simulation & Optimization, Athens, 4-7 July, 2007.
  34. Maysam Shahedi B K, Rassoul Amirfattahi, Farah Torkamani Azar and Saeed Sadri, “Accurate Breast Region Detection in Digital Mammograms using a Local Adaptive Thresholding Method”, Eight International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS '07), Santorini, Greece, pp 26-29.
  35. Roshan Dharshana Yapa, Koichi Harada, “Breast Skin-Line Estimation and Breast Segmentation in Mammograms using Fast-Marching Method”, International Journal of Biological and Life Sciences 3:1 2007, pp 54-62.
Index Terms

Computer Science
Information Sciences

Keywords

Mammogram Segmentation Contrast Limited Adaptive Histogram Equalization (CLAHE) Binary Homogeneity Enhancement Algorithm (BHEA) Edge Detection Algorithm (EDA) Breast Boundary Detection Algorithm (BBDA)

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