Segmentation of regions of interest in mammograms in a topographic approach

IEEE Transactions on Information Technology in Biomedicine

Volumn 14, Issue 1, 2010, Pages 129-139

  Hong, Byung Woo ^a   Sohn, Bong Soo ^a  

^a Chung Ang University   (South Korea)

Author keywords

Digital mammography; Isocontour; Mass detection; Segmentation; Topographic representation

Indexed keywords

BREAST BOUNDARY; DENSE REGION; DIGITAL MAMMOGRAPHY; GEOMETRICAL STRUCTURE; GLOBAL STRUCTURE; HIERARCHICAL REPRESENTATION; MASS DETECTION; MULTISCALE SCHEMES; MULTISCALES; NOVEL METHODS; PECTORAL MUSCLES; REGIONS OF INTEREST; SALIENT REGIONS; TOPOGRAPHIC REPRESENTATION;

MUSCLE; X RAY SCREENS;

MAMMOGRAPHY;

ALGORITHM; ARTICLE; BREAST TUMOR; FEMALE; HUMAN; IMAGE PROCESSING; MAMMOGRAPHY; METHODOLOGY; PECTORALIS MAJOR MUSCLE; RADIOGRAPHY;

ALGORITHMS; BREAST NEOPLASMS; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAMMOGRAPHY; PECTORALIS MUSCLES;

EID: 76849089516     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2009.2033269     Document Type: Conference Paper

References (82)

1. N. B. C. S. P. W. site. (2005). [Online]. Available: www.cancerscreening. nhs.uk/breastscreen
2. T. N. C. I. W. site. (2005). [Online]. Available: www.cancer.gov
3. E. L. Thurfjell and J. A. Lindgren, "Breast cancer survival rates with mammographic screening: Similar favorable survival rates for women younger and those older than 50 years," Radiology, vol.201, no.2, pp. 421-426, 1996.
4. P. B. Dean, "Overview of breast cancer screening," in Proc. Digital Mammography 1996. Amsterdam, The Netherlands: Elsevier, pp. 19-26.
5. I. Andersson, "Breast cancer screening results," in Proc. Digital Mammography 1998. Norwell, MA: Kluwer, pp. 427-433.
6. R.McLelland, "Screening for breast cancer: Opportunity, status and challenges," Recent Results Cancer Res., vol.119, pp. 29-38, 1990.
7. R. Zwiggelaar, T. C. Parr, J. E. Schumm, I. W. Hutt, C. J. Taylor, S. M. Asley, and C. R. M. Boggis, "Model-based detection of spiculated lesions in mammograms," Med. Image Anal., vol.3, no.1, pp. 39-62, 1999.
8. N. Karssemeijer and G. te Brake, "Detection of stellate distortions in mammograms," IEEE Trans. Med. Imag., vol.5, no.5, pp. 611-619, Oct. 1996.
9. E. E. Sterns, "Relation between clinical and mammographic diagnosis of breast problems and the cancer/biopsy rate," Can. J. Surg., vol.39, no.2, pp. 128-132, 1996.
10. A. Forrest and R. Aitken, "Mammography screening for breast cancer," Annu. Rev. Med., vol.41, pp. 117-132, 1990.
11. P. Dean, "Overviewof breast cancer screening," ExcerptaMed., vol.1119, pp. 19-26, 1996.
12. P. T. Huynh, A. M. Jarolimek, and S. Daye, "The false-negative mammogram," Radiographics, vol.18, no.5, pp. 1137-1154, 1998.
13. L. J. W. Burhenne, S. A. Wood, C. J. D'Orsi, S. A. Feig, D. B. Kopans, K. F. O'Shaugnessy, E. A. Sickles, L. Tabar, C. J. Vyborny, and R. A. Castelino, "The potential contribution of computer aided detection to the sensitivity of screeningmammography," Radiology, vol.215, pp. 554- 562, 2000.
14. P. D. Britton, J. McCann, D. O. Driscoll, G. Hunnam, and R.M. L.Warren, "Interval cancer peer review in East Anglia: Implications for monitoring doctors as well as the NHS breast screening programme," Clin. Radiol., vol.56, no.1, pp. 44-49, 2001.
15. S. M. Astley, C. R. M. Boggis, K. Walker, S. Wallace, S. Tomkinson, V. Hillier, and J. Morris, "An evaluation of a commercial prompting system in a busy screening centre," in Proc. 6th Int. Workshop Digital Mammography, 2002, pp. 471-475.
16. S. Field, "Breast screening issues â a report into workforce, funding, litigation and morale," Newslett. Roy. College Radiol., vol.54, pp. 12-14, 1998.
17. H.-P. Chan, K. Doi, C. J. Vyborny, R. A. Schmidt, C. E. Metz, K. L. Lam, T. Ogura, Y.Wu, and H. MacMahon, "Improvement in radiologists' detection of clustered microcalcifications on mammograms," Invest. Radiol., vol.25, pp. 1102-1110, 1990.
18. S. M. Astley, C. J. Taylor, C. R. M. Boggis, D. L. Asbury, and M.Wilson, "Cue generation combination for mammographic screening," in Visual Search, vol.II, D. Brogan, K. Carr, A. G. Gale, Eds. London: Taylor & Francis, 1993.
19. N. Karssemeijer, "Adaptive noise equalization and recognition of microcalcification clusters in mammograms," Mach. Percept. Artif. Intell., vol.9, pp. 148-166, 1994.
20. I. W. Hutt, "The computer-aided detection of abnormalities in digital mammograms," Ph.D. dissertation, The Univ. Manchester, Manchester, U.K., 1996.
21. S. M. Astley, R. Zwiggelaar, C.Wolstenholme, and C. J. Taylor, "Prompting in mammography: How accurate must prompt generators be?," in Proc. 4th Int. Workshop Digital Mammography, 1998, pp. 347-354.
22. T. Freer and M. J. Ulissey, "Screening mammography with computeraided detection: Prospective study of 12 860 patients in a community breast center," Radiology, vol.220, no.3, pp. 781-786, 2001.
23. M. L. Giger, "Computer-aided diagnosis in radiology," Acad. Radiol., vol.9, no.1, pp. 1-3, 2002.
24. F. Winsberg, M. Elkin, J. Macy, V. Bordaz, and W. Weymouth, "Detection of abnormalities in mammograms by means of optical scanning and computer analysis," Radiology, vol.89, pp. 211-215, 1967.
25. C. Kimme, B. J. O'Loughlin, and J. Sklansky, "Automatic detection of suspicious abnormalities in breast radiographs," in Proc. Conf. Comput. Graph., Pattern Recognit. Data Struct., 1975, pp. 84-88.
26. W. Hand, J. L. Semmlow, L. V. Ackerman, and F. S. Alcorn, "Computer screening of xeromammograms: A technique for defining suspicious areas of the breast," Comp. Biomed. Res., vol.12, pp. 445-460, 1979.
27. T. K. Lau andW. F. Bischof, "Automated detection of breast tumors using the asymmetry approach," Comp. Biomed. Res., vol.24, pp. 273-295, 1991.
28. F. Yin, M. Giger, K. Doi, C. Metz, C. Vyborny, and R. Schmidt, "Computerized detection of masses in digital mammograms: Analysis of bilateral subtraction images," Med. Phys., vol.18, pp. 955-963, 1991.
29. F. Yin, M. Giger, K. Doi, C. Vyborny, and R. Schmidt, "Computerized detection of masses in digital mammograms: Automated alignment of breast images and its effect on bilateral subtraction," Med. Phys., vol.21, pp. 445-452, 1994.
30. P. Miller and S. Astley, "Automated detection of mammographic asymmetry using anatomical features," Int. J. Pattern Recognit. Artif. Intell., vol.7, pp. 1461-1476, 1993.
31. U. Bick, M. Giger, R. Schmidt, D. Wolverton, and K. Doi, "Computeraided breast cancer detection in screening mammography," in Proc. 3rd Int. Workshop Digital Mammography, 1996, pp. 97-103.
32. N. Karssemeijer and G. M. te Brake, "Combining single view features and asymmetry for detection of mass lesions," in Proc. 4th Int. Workshop Digital Mammography, 1998, pp. 95-102.
33. X. Zhou and R. Gordon, "Geometric unwarping of digital subtraction mammography," in Proc. Vis. Interface, 1988, pp. 25-30.
34. C. Priebe, R. Lorey, D. Marchette, and J. Solka, "Nonparametric spatiotemporal change point analysis for early detection in mammography," in Proc. 2nd Int. Workshop Digital Mammography, 1994, pp. 111-120.
35. D. Brzakovic, N. Vujovic, V. Neskovic, P. Brzakovic, and B. Fogarty, "Mammogram analysis by comparisonwith previous screenings," in Proc. 2nd Int. Workshop Digital Mammography, 1994, pp. 131-139.
36. W. Zouras, M. Giger, P. Lu, D. Wolverton, C. Vyborny, and K. Doi, "Investigation of a temporal subtraction scheme for computerized detection of breast masses in mammograms," in Proc. 3rd Int. Workshop Digital Mammography, 1996, pp. 411-416.
37. S. K. Wiles, J. M. Brady, and R. P. Highnam, "Comparing mammogram pairs in the detection of lesions," in Proc. 4th Int. Workshop Digital Mammography, 1998, pp. 103-110.
38. M. Y. Sallam and K. W. Bowyer, "Registration and difference analysis of corresponding mammogram images," Med. Image Anal., vol.3, no.2, pp. 103-118, 1999.
39. J. Roehrig, T. Doi, A. Hasegawa, B. Hunt, J. Marshall, A. S. H. Romsdahl, R. Sharbaugh, and W. Zhang, "Clinical results with R2 ImageChecker system," in Proc. Digital Mammography 1998. Nijmegen, The Netherlands: Kluwer, pp. 395-400.
40. R. F. Brem and J. M. Schoonjans, "Radiologist detection of microcalcifications with andwithout computer-aided detection:Acomparative study," Clin. Radiol., vol.56, no.2, pp. 150-154, 2001.
41. B.-W. Hong and J. M. Brady, "A topographic representation for mammogram segmentation," presented at the Med. Image Comput. Comput. Assisted Intervention, Montreal, QC, Canada, Nov. 2003.
42. B.-W. Hong and J. M. Brady, "Structural comparison of mammogram pairs," presented at the Brit. Mach. Vis. Conf., Oxford, U.K., Sep. 2005.
43. R. Highnam and M. Brady, Mammographic Image Analysis. Norwell, MA: Kluwer, 1999.
44. D. Raba, A. Oliver, J. Marti, M. Peracaula, and J. Espuny, "Breast segmentation with pectoral muscle suppression on digital mammograms," in Proc. IbPRIA 2005. (Lecture Notes in Computer Science 3523). New York: Springer-Verlag, pp. 471-478.
45. T. Lau and W. Bischoff, "Automated detection of breast tumors using the asymmetry approach," Comput. Biomed. Res., vol.24, pp. 273-295, 1991.
46. F. Yin and M. Giger, "Computerized detection of masses in digital mammogram: Analysis of bilateral subtraction images," Med. Phys., vol.28, pp. 955-963, 1991.
47. U. Bick and M. Giger, "Automated segmentation of digitized mammograms," Academic Radiol., vol.2, pp. 1-9, 1995.
48. J. Byng and N. Boyd, "Automated analysis of mammographic densities," Med. Phys., vol.41, pp. 909-923, 1996.
49. M. Masek and Y. Attikiouzel, "Skin-air interface extraction from mammograms using an automatic local thresholding algorithm," in Proc. ICB, 2000, pp. 204-206.
50. A. Mendez and P. Tahoces, "Automatic detection of breast border and nipple in digital mammograms," Comput. Methods Programs Biomed., vol.49, pp. 253-262, 1996.
51. A. Morton, H. Chan, and M. Goodsitt, "Automated model-guided breast segmentation algorithm," Med. Phys., vol.23, pp. 1107-1108, 1996.
52. C. Zhou andH. Chan, "Computerized image analysis: Estimation of breast density on mammograms," Med. Phys., vol.28, pp. 1056-1069, 2001.
53. J. Hein and M. Kallargi, "Multiresolution wavelet approach for separating the breast region from the background in high resolution digital mammography," in Proc. Digital Mammography 1996. Amsterdam, The Netherlands: Elsevier, pp. 467-470.
54. J. Hein andM. Kallargi, "Multiresolution wavelet approach for separating the breast region from the background in high resolution digital mammography," in Proc. Digital Mammography 1998. Norwell, MA: Kluwer, pp. 295-298.
55. K. McLoughlin and P. Bones, "Locating the breast-air boundary for a digital mammogram image," in Proc. Image Vis. Comput. New Zealand, Hamilton, 2000, pp. 228-233.
56. M. Wirth and A. Stapinski, "Segmentation of the breast region in mammograms using snakes," in Proc. Can. Conf. Comput. Robot Vis., 2004, pp. 385-392.
57. R. Ferrari, R. Rangayyan, J. Desautels, R. Borges, and A. Frere, "Identification of the breast boundary in mammograms using active contour models," Med. Biol. Eng. Comput., vol.42, pp. 201-208, 2004.
58. S. Kwok, R. Chandrasekhar, and Y. Attikiouzel, "Automatic pectoral muscle segmentation on mammograms by straight line estimation and cliff detection," in Proc. IIS Conf., 2001, pp. 67-72.
59. M. Yam and M. Brady, "Three-dimensional reconstruction of microcalcifications clusters from two mammographic views," in Proc. Med. Image, 2001, vol.20, pp. 479-489.
60. S. Kwok, R. Chandrasekhar, and Y. Attikiouzel, "Automatic pectoral muscle segmentation on mediolateral oblique view mammograms," IEEE Trans. Med. Imag., vol.23, no.9, pp. 1129-1140, Sep. 2004.
61. R. Ferrari, R. Rangayyan, J. Desautels, R. Borges, and A. Frere, "Automatic identification of the pectoral muscle in mammograms," IEEE Trans. Med. Imag., vol.23, no.2, pp. 232-245, Feb. 2004.
62. H. Li,M. Kallergi, L. Clarke, V. Jain, and R. Clark, "Markov random field for tumour detection in digital mammography," IEEE Trans. Med. Imag., vol.14, no.3, pp. 565-576, Sep. 1995.
63. M. Kupinsky and M. Giger, "Automated seeded segmentation on digital mammograms," IEEE Trans. Med. Imag., vol.17, no.4, pp. 510-517, Aug. 1998.
64. S. Ng and W. Bischof, "Automated detection and classification of breast tumours," Comput. Biomed. Res., vol.25, pp. 218-237, 1992.
65. W. Polakowski, D. Cournoyer, S. Rogers,M.DeSimio,D.Ruck, J. Hoffmiester, and J. Raines, "Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency," IEEE Trans. Med. Imag., vol.16, no.6, pp. 811-819, Dec. 1997.
66. R. Zwiggelaar, J. E. Schumm, and C. J. Taylor, "The detection of abnormal masses in mammograms," in Proc. Med. Image Understanding Anal., Oxford, U.K., Jul.1997.
67. B. R. Groshong andW. P. Kegelmeyer, "Evaluation of a hough transform method for circumscribed lesion detection," in Digital Mammography, K. Doi, M. L. Giger, R. M. Nishikawa, and R. A. Schmidt, Eds. Amsterdam, The Netherlands: Elsevier, 1996, pp. 361-366.
68. G. te Brake and N. Karssemeijer, "Single and multiscale detection of masses in digital mammograms," IEEE Trans. Med. Imag., vol.18, no.7, pp. 628-639, Jul. 1999.
69. L. Li, W. Qian, and L. P. Clarke, "Digital mammography: Computer assisted diagnosis method for mass detection with multiorientation and multiresolution wavelet transforms," Acad. Radiol., vol.11, no.4, pp. 724- 731, 1997.
70. C. H. Chen and G. G. Lee, "On digital mammogram segmentation and microcalcification detection using multiresolution wavelet analysis," Graph. Models Image Process., vol.59, no.5, pp. 349-364, 1997.
71. L. Miller andN.Ramsey, "The detection ofmalignant masses by nonlinear multiscale analysis," in Digital Mammography, K. Doi, M. L. Giger, R. M. Nishikawa, and R. A. Schmidt, Eds. Amsterdam, The Netherlands: Elsevier, 1996, pp. 335-340.
72. D. Brzakovic and M. Neskovic, "Mammogram screening using multiresolution based image segmentation," in State of the Art in Digital Mammographic Image Analysis (Machine Perception and Artificial Intelligence Series 9), K. W. Bowyer and S. M. Astley, Eds. New York: World Scientific, 1994, pp. 103-127.
73. C. M. Chang and A. Laine, "Coherence of multiscale features for enhancement of digital mammograms," IEEE Trans. Inf. Technol. Biomed., vol.3, no.1, pp. 32-46, Mar. 1999.
74. R. Guissin and J. M. Brady, "Iso-intensity contours for edge detection," Dept. Eng. Sci., Oxford Univ., Oxford, U.K., Tech. Rep. OUEL 1935/92, 1992.
75. L. Hadjiiski, B. Sahiner, H.-P. Chan, N. Petrick, and M. Helvie, "Classification ofmalignant and benignmasses based on hybrid art2lda approach," IEEE Trans. Med. Imag., vol.18, no.12, pp. 1178-1187, Dec. 1999.
76. N. R. Mudigonda, R.M. Rangayyan, and J. E. L. Desautels, "Gradient and texture analysis for the classification of mammographic masses," IEEE Trans. Med. Imag., vol.19, no.10, pp. 1032-1043, Oct. 2000.
77. R. M. Rangayyan, N. R. Mudigonda, and J. E. L. Desautels, "Boundary modeling and shape analysis methods for classification of mammographic masses," Med. Biol. Eng. Comput., vol.38, pp. 487-496, 2000.
78. S.-C. B. Lo, H. Li, Y.Wang, L. Kinnard, and M. T. Freedman, "A multiple circular path convolution neural network system for detection of mammographic masses," IEEE Trans. Med. Imag., vol.21, no.2, pp. 150-158, Feb. 2002.
79. J. Weickert, A Review of Nonlinear Diffusion Filtering. NewYork: Springer-Verlag, 1997.
80. P. Johns and M. Yaffe, "X-ray characterization of normal and neoplastic breast tissues," Phys. Med. Biol., vol.32, pp. 675-695, 1987.
81. M. Heath, K. Bowyer, D. Kopans, R. Moore, and P. Kegelmeyer Jr., "The digital database for screening mammography," In IWDM 2000, Toronto, ON, Canada.
82. T. Chan and L.Vese, "Active contourswithout edges," IEEE Trans. Image Process., vol.10, no.2, pp. 266-277, Feb. 2001.