Medical & Biological Engineering & Computing

, Volume 54, Issue 7, pp 1003–1024 | Cite as

Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms

  • Mario Mustra
  • Mislav Grgic
  • Rangaraj M. Rangayyan
Review Article

Abstract

This paper presents a review of recent advances in the development of methods for segmentation of the breast boundary and the pectoral muscle in mammograms. Regardless of improvement of imaging technology, accurate segmentation of the breast boundary and detection of the pectoral muscle are still challenging tasks for image processing algorithms. In this paper, we discuss problems related to mammographic image preprocessing and accurate segmentation. We review specific methods that were commonly used in most of the techniques proposed for segmentation of mammograms and discuss their advantages and disadvantages. Comparative analysis of the methods reported on is made difficult by variations in the datasets and procedures of evaluation used by the authors. We attempt to overcome some of these limitations by trying to compare methods which used the same dataset and have some similarities in approaches to the breast boundary segmentation and detection of the pectoral muscle. In this paper, we will address the most often used methods for segmentation such as thresholding, morphology, region growing, active contours, and wavelet filtering. These methods, or their combinations, are the ones most used in the last decade by the majority of work published in this image processing domain.

Keywords

Mammography Segmentation Breast boundary Pectoral muscle 

References

  1. 1.
    Abdellatif H, Taha TE, Zahran OF, Al-Nauimy W, El-Samie FA (2012) Automatic pectoral muscle boundary detection in mammograms using eigenvectors segmentation. In: 29th national radio science conference. IEEE, pp 633–640Google Scholar
  2. 2.
    ACR (2003) American College of Radiology Breast Imaging Reporting and Data System (BI-RADS). American College of RadiologyGoogle Scholar
  3. 3.
    Adel M, Rasigni M, Bourennane S, Juhan V (2007) Statistical segmentation of regions of interest on a mammographic image. EURASIP J Adv Signal Process 2:1–8. doi:10.1155/2007/49482 Google Scholar
  4. 4.
    Baker JA, Rosen EL, Crockett MM, Lo JY (2005) Accuracy of segmentation of a commercial computer-aided detection system for mammography. Radiology 235(2):385–390. doi:10.1148/radiol.2352040899 CrossRefPubMedGoogle Scholar
  5. 5.
    Bandyopadhyay SK (2010) Pre-processing of mammogram images. Int J Eng Sci Technol 2(11):6753–6758Google Scholar
  6. 6.
    Camilus KS, Govindan VK, Sathidevi PS (2010) Computer-aided identification of the pectoral muscle in digitized mammograms. J Digit Imaging 23(5):562–580. doi:10.1007/s10278-009-9240-6 CrossRefPubMedGoogle Scholar
  7. 7.
    Camilus KS, Govindan VK, Sathidevi PS (2011) Pectoral muscle identification in mammograms. J Appl Clin Med Phys Am Coll Med Phys 12(3):3285Google Scholar
  8. 8.
    Canny J (1986) A computational approach to edge detection. Pattern. IEEE Trans Pattern Anal Mach Intell 6:679–698CrossRefGoogle Scholar
  9. 9.
    Cardoso JS, Domingues I, Amaral I, Moreira I, Passarinho P, Santa Comba J, Correia R, Cardoso MJ (2010) Pectoral muscle detection in mammograms based on polar coordinates and the shortest path. In: 2010 annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp 4781–4784Google Scholar
  10. 10.
    Casti P, Mencattini A, Salmeri M, Ancona A, Mangeri F, Pepe ML, Rangayyan RM (2013) Estimation of the breast skin-line in mammograms using multidirectional Gabor filters. Comput Biol Med 43(11):1870–1881. doi:10.1016/j.compbiomed.2013.09.001 CrossRefPubMedGoogle Scholar
  11. 11.
    Chakraborty J, Mukhopadhyay S, Singla V, Khandelwal N, Bhattacharyya P (2012) Automatic detection of pectoral muscle using average gradient and shape based feature. J Digit Imaging 25(3):387–399. doi:10.1007/s10278-011-9421-y CrossRefPubMedGoogle Scholar
  12. 12.
    Chen Z, Zwiggelaar R (2012) A combined method for automatic identification of the breast boundary in mammograms. In: 5th international conference on biomedical engineering and informatics (BMEI). IEEE, pp 121–125Google Scholar
  13. 13.
    Czaplicka K, Włodarczyk J (2011) Automatic breast-line and pectoral muscle segmentation. Schedae Inform 20:195–209Google Scholar
  14. 14.
    Day N, Oakes S, Luben R, Khaw KT, Bingham S, Welch A, Wareham N (1999) EPIC-Norfolk: study design and characteristics of the cohort. European prospective investigation of cancer. Br J Cancer 80(Suppl 1):95–103PubMedGoogle Scholar
  15. 15.
    Domingues I, Cardoso JS, Amaral I, Moreira I, Passarinho P, Santa Comba J, Correia R, Cardoso MJ (2010) Pectoral muscle detection in mammograms based on the shortest path with endpoints learnt by SVMs. In: Annual international conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp 3158–3161Google Scholar
  16. 16.
    Ferrari RJ, Rangayyan RM, Desautels JEL, Borges RA, Frere AF (2004) Identification of the breast boundary in mammograms using active contour models. Med Biol Eng Comput 42(2):201–208. doi:10.1007/Bf02344632 CrossRefPubMedGoogle Scholar
  17. 17.
    Ferrari RJ, Rangayyan RM, Desautels JE, Borges RA, Frere AF (2004) Automatic identification of the pectoral muscle in mammograms. IEEE Trans Med Imaging 23(2):232–245CrossRefPubMedGoogle Scholar
  18. 18.
    Gonzalez RC, Woods RE (2008) Digital image processing. Pearsons Education Inc, Upper Saddle RiverGoogle Scholar
  19. 19.
    Gonzalez RC, Woods RE, Eddins SL (2009) Digital image processing using MATLAB, vol 2. Gatesmark, KnoxvilleGoogle Scholar
  20. 20.
    Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer WP (2001) The digital database for screening mammography. IWDM 2000:212–218Google Scholar
  21. 21.
    Hong BW, Brady M (2003) A topographic representation for mammogram segmentation. In: Ellis RE, Peters TM (eds) Medical image computing and computer-assisted intervention-MICCAI. Springer, Berlin, Heidelberg, pp 730–737Google Scholar
  22. 22.
    Hong BW, Sohn BS (2010) Segmentation of regions of interest in mammograms in a topographic approach. IEEE Trans Inform Technol Biomed 14(1):129–139. doi:10.1109/TITB.2009.2033269 CrossRefGoogle Scholar
  23. 23.
    Huttenlocher DP, Klanderman GA, Rucklidge WJ (1993) Comparing Images Using the Hausdorff Distance. IEEE Trans Pattern Anal Mach Intell 15(9):850–863. doi:10.1109/34.232073 CrossRefGoogle Scholar
  24. 24.
    Karnan M, Thangavel K (2007) Automatic detection of the breast border and nipple position on digital mammograms using genetic algorithm for asymmetry approach to detection of microcalcifications. Comput Methods Programs Biomed 87(1):12–20. doi:10.1016/j.cmpb.2007.04.007 CrossRefPubMedGoogle Scholar
  25. 25.
    Kinoshita SK, Azevedo-Marques PM, Pereira RR Jr, Rodrigues JA, Rangayyan RM (2008) Radon-domain detection of the nipple and the pectoral muscle in mammograms. J Digit Imaging 21(1):37–49. doi:10.1007/s10278-007-9035-6 CrossRefPubMedGoogle Scholar
  26. 26.
    Kwok SM, Chandrasekhar R, Attikiouzel Y (2001) Automatic pectoral muscle segmentation on mammograms by straight line estimation and cliff detection. In: The seventh Australian and New Zealand intelligent information systems conference. IEEE, pp 67–72Google Scholar
  27. 27.
    Kwok SM, Chandrasekhar R, Attikiouzel Y, Rickard MT (2004) Automatic pectoral muscle segmentation on mediolateral oblique view mammograms. IEEE Trans Med Imaging 23(9):1129–1140. doi:10.1109/TMI.2004.830529 CrossRefPubMedGoogle Scholar
  28. 28.
    Lewin JM, Hendrick RE, D’Orsi CJ, Isaacs PK, Moss LJ, Karellas A, Sisney GA, Kuni CC, Cutter GR (2001) Comparison of full-field digital mammography with screen-film mammography for cancer detection: results of 4,945 paired examinations. Radiology 218(3):873–880. doi:10.1148/radiology.218.3.r01mr29873 CrossRefPubMedGoogle Scholar
  29. 29.
    Li Y, Chen H, Yang Y, Yang N (2012) Pectoral muscle segmentation in mammograms based on homogenous texture and intensity deviation. Pattern Recogn 46:681–691CrossRefGoogle Scholar
  30. 30.
    Ma F, Bajger M, Slavotinek JP, Bottema MJ (2007) Two graph theory based methods for identifying the pectoral muscle in mammograms. Pattern Recogn 40(9):2592–2602. doi:10.1016/j.patcog.2006.12.011 CrossRefGoogle Scholar
  31. 31.
    Maitra I, Nag S, Bandyopadhyay S (2011) Automated digital mammogram segmentation for detection of abnormal masses using binary homogeneity enhancement algorithm. Indian J Comput Sci Eng 2(3):416–427Google Scholar
  32. 32.
    Maitra IK, Nag S, Bandyopadhyay SK (2011) Detection and isolation of pectoral muscle from digital mammogram: an automated approach. Int J Adv Res Comput Sci 2(3):375–380Google Scholar
  33. 33.
    Maitra IK, Nag S, Bandyopadhyay SK (2012) Technique for preprocessing of digital mammogram. Comput Methods Programs Biomed 107(2):175–188. doi:10.1016/j.cmpb.2011.05.007 CrossRefPubMedGoogle Scholar
  34. 34.
    Martí R, Oliver A, Raba D, Freixenet J (2007) Breast skin-line segmentation using contour growing. In: Martí J, Benedí JM, Mendonça AM, Serrat J (eds) Pattern recognition and image analysis. Springer, Berlin, Heidelberg, pp 564–571CrossRefGoogle Scholar
  35. 35.
    Mirzaalian H, Ahmadzadeh MR, Sadri S (2007) Pectoral muscle segmentation on digital mammograms by nonlinear diffusion filtering. In: IEEE Pacific Rim conference on communications, computers and signal processing, 2007. IEEE, pp 581–584Google Scholar
  36. 36.
    Mirzaalian H, Ahmadzadeh MR, Sadri S, Jafari M (2007) Pre-processing algorithms on digital mammograms. In: IAPR conference on machine vision applications, Tokyo. MVA, pp 118–121Google Scholar
  37. 37.
    Mustra M, Grgic M (2012) Robust automatic breast and pectoral muscle segmentation from scanned mammograms. Sig Process 93(10):2817–2827CrossRefGoogle Scholar
  38. 38.
    Mustra M, Bozek J, Grgic M (2009) Breast border extraction and pectoral muscle detection using wavelet decomposition. In: EUROCON 2009. IEEE, pp 1426–1433Google Scholar
  39. 39.
    Nagi J, Abdul Kareem S, Nagi F, Khaleel Ahmed S (2010) Automated breast profile segmentation for ROI detection using digital mammograms. In: IEEE EMBS conference on biomedical engineering and sciences (IECBES). IEEE, pp 87–92Google Scholar
  40. 40.
    NEMA (2011) Digital imaging and communications in medicine (DICOM), Dicom Standard. NEMA Publications, RosslynGoogle Scholar
  41. 41.
    Oliver A, Freixenet J, Marti R, Pont J, Perez E, Denton ER, Zwiggelaar R (2008) A novel breast tissue density classification methodology. IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society 12(1):55–65. doi:10.1109/TITB.2007.903514 CrossRefGoogle Scholar
  42. 42.
    Oliver A, Llado X, Perez E, Pont J, Denton ER, Freixenet J, Marti J (2010) A statistical approach for breast density segmentation. J Digit Imaging 23(5):527–537. doi:10.1007/s10278-009-9217-5 CrossRefPubMedGoogle Scholar
  43. 43.
    Qi H, Head JF (2001) Asymmetry analysis using automatic segmentation and classification for breast cancer detection in thermograms. In: Proceedings of the 23rd annual international conference of the IEEE. Engineering in Medicine and Biology Society, pp 2866–2869Google Scholar
  44. 44.
    Raba D, Oliver A, Marti J, Peracaula M, Espunya J (2005) Breast segmentation with pectoral muscle suppression on digital mammograms. Lect Notes Comput Sc 3523:471–478CrossRefGoogle Scholar
  45. 45.
    Rickard HE, Tourassi GD, Eltonsy N, Elmaghraby AS (2004) Breast segmentation in screening mammograms using multiscale analysis and self-organizing maps. In: 26th annual international conference of the IEEE Engineering in Medicine and Biology Society, 2004. IEEE, pp 1786–1789Google Scholar
  46. 46.
    Saidin N, Ngah UK, Sakim H, Siong DN, Hoe MK (2009) Density based breast segmentation for mammograms using graph cut techniques. In: IEEE region 10 conference TENCON 2009. IEEE, pp 1–5Google Scholar
  47. 47.
    Saltanat N, Hossain MA, Alam MS (2010) An efficient pixel value based mapping scheme to delineate pectoral muscle from mammograms. In: 2010 IEEE fifth international conference on bio-inspired computing: theories and applications. IEEE, pp 1510–1517Google Scholar
  48. 48.
    Shanmugavadivu P, Sivakumar V (2013) Segmentation of pectoral muscle in mammograms using fractal method. In: 2013 international conference on computer communication and informatics (ICCCI). IEEE, pp 1–6Google Scholar
  49. 49.
    Subashini TS, Ramalingam V, Palanivel S (2010) Pectoral muscle removal and detection of masses in digital mammogram using CCL. Int J Comput Appl 1(6):66–70Google Scholar
  50. 50.
    Suckling J, Parker J, Dance DR, Astley S, Hutt I, Boggis CRM, Ricketts I, Stamatakis E, Cernaez N, Kok SL, Taylor P, Betal D, Savage J (1994) The mammographic image analysis society digital mammogram database. In: Proceedings of the 2nd international workshop on digital mammography, pp 375–378Google Scholar
  51. 51.
    Sultana A, Ciuc M, Strungaru R (2010) Detection of pectoral muscle in mammograms using a mean-shift segmentation approach. In: 8th international conference on communications. IEEE, pp 165–168Google Scholar
  52. 52.
    Sun YJ, Suri JS, Desautels JEL, Rangayyan RM (2006) A new approach for breast skin-line estimation in mammograms. Pattern Anal Appl 9(1):34–47. doi:10.1007/s10044-006-0023-0 CrossRefGoogle Scholar
  53. 53.
    Tayel M, Mohsen A (2010) Breast boarder boundaries extraction using statistical properties of Mammogram. In: 10th international conference on signal processing (ICSP). IEEE, pp 2468–2471Google Scholar
  54. 54.
    Tzikopoulos S, Georgiou H, Mavroforakis M, Dimitropoulos N, Theodoridis S (2009) A fully automated complete segmentation scheme for mammograms. In: 16th international conference on digital signal processing. IEEE, pp 1–6Google Scholar
  55. 55.
    van Engeland S, Snoeren P, Hendriks J, Karssemeijer N (2003) A comparison of methods for mammogram registration. IEEE Trans Med Imaging 22(11):1436–1444. doi:10.1109/TMI.2003.819273 CrossRefPubMedGoogle Scholar
  56. 56.
    Wang L, Zhu ML, Deng LP, Yuan X (2010) Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model. J Zhejiang Univ Sci C 11(2):111–118. doi:10.1631/jzus.C0910025 CrossRefGoogle Scholar
  57. 57.
    Wei K, Guangzhi W, Hui D (2005) Segmentation of the breast region in mammograms using watershed transformation. In: 27th annual international conference of the Engineering in Medicine and Biology Society. IEEE-EMBS, pp 6500–6503Google Scholar
  58. 58.
    Weickert J (1997) A review of nonlinear diffusion filtering. Scale-Space Theory in Computer Vision 1252:3–28Google Scholar
  59. 59.
    Weidong X, Shunren X (2003) A model based algorithm to segment the pectoral muscle in mammograms. In: Proceedings of the 2003 international conference on neural networks and signal processing. IEEE, pp 1163–1169Google Scholar
  60. 60.
    Wirth MA, Stapinski A (2003) Segmentation of the breast region in mammograms using active contours. In: Ebrahimi T, Sikora T (eds) Visual communications and image processing. International Society for Optics and Photonics, Lugano, pp 1995–2006Google Scholar
  61. 61.
    Wirth MA, Lyon J, Nikitenko DA (2004) Fuzzy approach to segmenting the breast region in mammograms. In: IEEE annual meeting of the fuzzy information processing NAFIPS’04. IEEE, pp 474–479Google Scholar
  62. 62.
    Wirth M, Lyon J, Fraschini M, Nikitenko D (2004) The effect of mammogram databases on algorithm performance. In: 17th IEEE symposium on computer-based medical systems. IEEE, pp 15–20Google Scholar
  63. 63.
    Wongthanavasu S, Tanvoraphonkchai S (2008) Cellular automata-based identification of the pectoral muscle in mammograms. In: The proceedings of the 3rd international symposium on biomedical engineering, pp 294–298Google Scholar
  64. 64.
    Xu W, Li L, Liu W (2007) A novel pectoral muscle segmentation algorithm based on polyline fitting and elastic thread approaching. In: The 1st international conference on bioinformatics and biomedical engineering. IEEE, pp 837–840Google Scholar
  65. 65.
    Yapa RD, Harada K (2008) Breast skin-line estimation and breast segmentation in mammograms using fast-marching method. International Journal of Biological, Biomedical and Medical Sciences 3(1):54–62Google Scholar
  66. 66.
    Zhang Z, Lu J, Yip YJ (2010) Automatic segmentation for breast skin-line. In: IEEE 10th international conference on computer and information technology. IEEE, pp 1599–1604Google Scholar
  67. 67.
    Zuiderveld K (1994) Contrast limited adaptive histogram equalization. In: Heckbert PS (ed) Graphics gems IV. Academic Press Professional, Inc, San Diego, pp 474–485CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2015

Authors and Affiliations

  • Mario Mustra
    • 1
  • Mislav Grgic
    • 1
  • Rangaraj M. Rangayyan
    • 2
  1. 1.Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia
  2. 2.Schulich School of EngineeringUniversity of CalgaryCalgaryCanada

Personalised recommendations