Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

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

  • Review Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  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 633–640

  2. ACR (2003) American College of Radiology Breast Imaging Reporting and Data System (BI-RADS). American College of Radiology

  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. 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

    Article  PubMed  Google Scholar 

  5. Bandyopadhyay SK (2010) Pre-processing of mammogram images. Int J Eng Sci Technol 2(11):6753–6758

    Google Scholar 

  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

    Article  PubMed  Google Scholar 

  7. Camilus KS, Govindan VK, Sathidevi PS (2011) Pectoral muscle identification in mammograms. J Appl Clin Med Phys Am Coll Med Phys 12(3):3285

    Google Scholar 

  8. Canny J (1986) A computational approach to edge detection. Pattern. IEEE Trans Pattern Anal Mach Intell 6:679–698

    Article  Google Scholar 

  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 4781–4784

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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 121–125

  13. Czaplicka K, Włodarczyk J (2011) Automatic breast-line and pectoral muscle segmentation. Schedae Inform 20:195–209

    Google Scholar 

  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):95–103

    PubMed  Google Scholar 

  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 3158–3161

  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

    Article  CAS  PubMed  Google Scholar 

  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):232–245

    Article  CAS  PubMed  Google Scholar 

  18. Gonzalez RC, Woods RE (2008) Digital image processing. Pearsons Education Inc, Upper Saddle River

    Google Scholar 

  19. Gonzalez RC, Woods RE, Eddins SL (2009) Digital image processing using MATLAB, vol 2. Gatesmark, Knoxville

    Google Scholar 

  20. Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer WP (2001) The digital database for screening mammography. IWDM 2000:212–218

    Google Scholar 

  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 730–737

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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 67–72

  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

    Article  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  29. Li Y, Chen H, Yang Y, Yang N (2012) Pectoral muscle segmentation in mammograms based on homogenous texture and intensity deviation. Pattern Recogn 46:681–691

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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):416–427

    Google Scholar 

  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):375–380

    Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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 564–571

    Chapter  Google Scholar 

  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 581–584

  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 118–121

  37. Mustra M, Grgic M (2012) Robust automatic breast and pectoral muscle segmentation from scanned mammograms. Sig Process 93(10):2817–2827

    Article  Google Scholar 

  38. Mustra M, Bozek J, Grgic M (2009) Breast border extraction and pectoral muscle detection using wavelet decomposition. In: EUROCON 2009. IEEE, pp 1426–1433

  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 87–92

  40. NEMA (2011) Digital imaging and communications in medicine (DICOM), Dicom Standard. NEMA Publications, Rosslyn

    Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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 2866–2869

  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 3523:471–478

    Article  Google Scholar 

  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 1786–1789

  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 1–5

  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 1510–1517

  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 1–6

  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):66–70

    Google Scholar 

  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 375–378

  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 165–168

  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

    Article  Google Scholar 

  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 2468–2471

  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 1–6

  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

    Article  PubMed  Google Scholar 

  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

    Article  Google Scholar 

  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 6500–6503

  58. Weickert J (1997) A review of nonlinear diffusion filtering. Scale-Space Theory in Computer Vision 1252:3–28

    Google Scholar 

  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 1163–1169

  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 1995–2006

    Google Scholar 

  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 474–479

  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 15–20

  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 294–298

  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 837–840

  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):54–62

    Google Scholar 

  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 1599–1604

  67. Zuiderveld K (1994) Contrast limited adaptive histogram equalization. In: Heckbert PS (ed) Graphics gems IV. Academic Press Professional, Inc, San Diego, pp 474–485

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia

    Mario Mustra & Mislav Grgic

  2. Schulich School of Engineering, University of Calgary, Calgary, AB, Canada

    Rangaraj M. Rangayyan

Authors
  1. Mario Mustra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mislav Grgic
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rangaraj M. Rangayyan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mario Mustra.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mustra, M., Grgic, M. & Rangayyan, R.M. Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms. Med Biol Eng Comput 54, 1003–1024 (2016). https://doi.org/10.1007/s11517-015-1411-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-015-1411-7

Keywords

Search

Navigation