Skip to main content

Advertisement

SpringerLink
Log in

A robust method for segmenting pectoral muscle in mediolateral oblique (MLO) mammograms

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Accurately detecting and removing pectoral muscle areas depicting on mediolateral oblique (MLO) view mammograms are an important step to develop a computer-aided detection scheme to assess global mammographic density or tissue patterns. This study aims to develop and test a new fully automated, accurate and robust method for segmenting pectoral muscle in MLO mammograms.

Methods

The new method includes the following steps. First, a small rectangular region in the top-left corner of the MLO mammogram which may contain pectoral muscle is captured and enhanced by the fractional differential method. Next, an improved iterative threshold method is applied to segment a rough binary boundary of the pectoral muscle in the small region. Then, a rough contour is fitted with the least squares method on the basis of points of the rough boundary. Last, the fitting contour is subjected to local active contour evolution to obtain the final pectoral muscle segmentation line. The method has been tested on 720 MLO mammograms.

Results

The segmentation results generated using the new scheme were evaluated by two expert mammographic radiologists using a 5-scale rating system. More than 65% were rated above scale 3. When assessing the segmentation results generated using Hough transform, morphologic thresholding methods and Unet-based model, less than 20%, 35% and 47% of segmentation results were rated above scale 3 by two radiologists, respectively. Quantitative data analysis results show that the Dice coefficient of 0.986 ± 0.005 is obtained. In addition, the mean rate of errors and Hausdorff distance between the contours detected by automated and manual segmentation are FP = 1.71 ± 3.82%, FN = 5.20 ± 3.94% and 2.75 ± 1.39 mm separately.

Conclusion

The proposed method can be used to segment the pectoral muscle in MLO mammograms with higher accuracy and robustness.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Yang Q, Li L, Zhang J, Shao G, Zheng B (2015) A new quantitative image analysis method for improving breast cancer diagnosis using DCE-MRI examinations. Med Phys 42(1):103

    Article  PubMed  Google Scholar 

  2. Aghaei F, Tan M, Hollingsworth AB, Qian W, Liu H, Zheng B (2015) Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy. Med Phys 42(11):6520–6528

    Article  PubMed  PubMed Central  Google Scholar 

  3. Lehman CD, Wellman RD, Buist DS, Kerlikowske K, Tosteson AN, Miglioretti DL (2015) Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern Med 175(11):1828

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bandyopadhyay SK, IndraKantaMaitra (2015) Fully automated computer aided diagnosis (CAD) system of human breast cancer using digital mammogram

  5. Vaidehi K, Subashini TS (2015) Automatic classification of CC view and MLO view in digital mammograms. In: Kamalakannan C, Suresh L, Dash S, Panigrahi B (eds) Power electronics and renewable energy systems, vol 326. Springer, New Delhi

    Google Scholar 

  6. Karssemeijer N (1998) Automated classification of parenchymal patterns in mammograms. Phys Med Biol 43(2):365

    Article  CAS  PubMed  Google Scholar 

  7. Ferrari RJ, Rangayyan RM, Desautels JEL, Borges RA, Frere AF (2004) Automatic identification of the pectoral muscle in mammograms. IEEE Trans Med Imaging 23(2):232

    Article  CAS  PubMed  Google Scholar 

  8. Yam M, Brady M, Highnam R, Behrenbruch C, English R, Kita Y (2001) Three-dimensional reconstruction of microcalcification clusters from two mammographic views. IEEE Trans Med Imaging 20(6):479–489

    Article  CAS  PubMed  Google Scholar 

  9. 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

    Article  Google Scholar 

  10. Georgsson F (2001) Algorithms and techniques for computer aided mammo-graphic screening. Daily Mail

  11. Makandar A, Halalli B (2016) Threshold based segmentation technique for mass detection in mammography. J Comput 11(6):472–478

    Article  Google Scholar 

  12. Li Y, Chen H, Yang Y, Yang N (2013) Pectoral muscle segmentation in mammograms based on homogenous texture and intensity deviation. Pattern Recogn 46(3):681–691

    Article  CAS  Google Scholar 

  13. Hong BW, Sohn BS (2010) Segmentation of regions of interest in mammograms in a topographic approach. IEEE Trans Inf Technol Biomed 14(1):129

    Article  PubMed  Google Scholar 

  14. Rodriguez-Ruiz A, Teuwen J, Chung K, Karssemeijer N, Chevalier M, Gubern-Mérida A (2018) Pectoral muscle segmentation in breast tomosynthesis with deep learning. In: Computer-aided diagnosis

  15. Chen D, Chen YQ, Xue D, Pan F (2012) Adaptive image enhancement based on fractional differential mask. In: CCDC, vol 229, pp 1043–1047

  16. Erçelebi E, Koç S (2006) Lifting-based wavelet domain adaptive wiener filter for image enhancement. IEE Proc Vis Image Signal Process 153(1):31–36

    Article  Google Scholar 

  17. Wuthrich M, Trimpe S, Kappler D, Schaal S (2015) A new perspective and extension of the Gaussian filter. Comput Sci 35(14)

  18. Anh VV, Mcvinish R (2003) Fractional differential equations driven by Lévy noise. Int J Stoch Anal 16(2):97–119

    Google Scholar 

  19. Yi-Fei PU (2007) Application of fractional differential approach to digital image processing. J Sichuan Univ 39(3):124–132

    Google Scholar 

  20. Pu YF, Zhou JL, Yuan X (2010) Fractional differential mask: a fractional differential-based approach for multiscale texture enhancement. IEEE Trans Image Process 19(2):491

    Article  PubMed  Google Scholar 

  21. Eklund GW, Cardenosa G, Parsons W (1994) Assessing adequacy of mammographic image quality. Radiology 190(2):297–307

    Article  CAS  PubMed  Google Scholar 

  22. Magid A, Rotman SR, Weiss AM (1990) Comments on picture thresholding using an iterative selection method. IEEE Trans Syst Man Cybern 20(5):1238–1239

    Article  Google Scholar 

  23. Boukamp BA (1986) A nonlinear least squares fit procedure for analysis of immittance data of electrochemical systems. Solid State Ionics 20(1):31–44

    Article  CAS  Google Scholar 

  24. Variable, R. (2013). Heaviside step function. Below

  25. Ruotsalainen K, Saranen J (1987) Some boundary element methods using Dirac’s distributions as trial functions. SIAM J Numer Anal 24(4):816–827

    Article  Google Scholar 

  26. Chan TF, Vese L (2001) Active contours without edges. IEEE Trans Image Process 10(2):266

    Article  CAS  PubMed  Google Scholar 

  27. Yezzi A, Tsai A, Willsky A (2002) A fully global approach to image segmentation via coupled curve evolution equations. Academic Press Inc., New York

    Book  Google Scholar 

  28. Mustra M, Grgic M (2013) Robust automatic breast and pectoral muscle segmentation from scanned mammograms. Signal Process 93(10):2817–2827

    Article  Google Scholar 

  29. Ferrari RJ, Rangayyan RM, Desautels JEL, Borges RA, Frere AF (2004) Automatic identification of the pectoral muscle in mammograms. IEEE Trans Med Imaging 23(2):232

    Article  CAS  PubMed  Google Scholar 

  30. Huttenlocher DP, Klanderman GA, Rucklidge WJ (1993) Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell 15(9):850–863

    Article  Google Scholar 

  31. Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. MIT Press, Cambridge

    Book  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Institutes of Health [Grant Numbers R01 CA160205, CA197150].

Author information

Authors and Affiliations

  1. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Kaiming Yin, Shiju Yan & Chengli Song

  2. School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA

    Bin Zheng

Authors
  1. Kaiming Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shiju Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chengli Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bin Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shiju Yan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animal performed by any of the authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yin, K., Yan, S., Song, C. et al. A robust method for segmenting pectoral muscle in mediolateral oblique (MLO) mammograms. Int J CARS 14, 237–248 (2019). https://doi.org/10.1007/s11548-018-1867-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-018-1867-7

Keywords

Search

Navigation