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

  • Kaiming Yin
  • Shiju Yan
  • Chengli Song
  • Bin Zheng
Original Article
  • 13 Downloads

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.

Keywords

Computer-aided diagnosis Pectoral muscle Automated segmentation Mediolateral oblique mammograms 

Notes

Acknowledgements

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

Compliance with ethical standards

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.

References

  1. 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):103CrossRefGoogle Scholar
  2. 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):6520–6528CrossRefGoogle Scholar
  3. 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):1828CrossRefGoogle Scholar
  4. 4.
    Bandyopadhyay SK, IndraKantaMaitra (2015) Fully automated computer aided diagnosis (CAD) system of human breast cancer using digital mammogramGoogle Scholar
  5. 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 DelhiGoogle Scholar
  6. 6.
    Karssemeijer N (1998) Automated classification of parenchymal patterns in mammograms. Phys Med Biol 43(2):365CrossRefGoogle Scholar
  7. 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):232CrossRefGoogle Scholar
  8. 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):479–489CrossRefGoogle Scholar
  9. 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):2592–2602CrossRefGoogle Scholar
  10. 10.
    Georgsson F (2001) Algorithms and techniques for computer aided mammo-graphic screening. Daily MailGoogle Scholar
  11. 11.
    Makandar A, Halalli B (2016) Threshold based segmentation technique for mass detection in mammography. J Comput 11(6):472–478CrossRefGoogle Scholar
  12. 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):681–691CrossRefGoogle Scholar
  13. 13.
    Hong BW, Sohn BS (2010) Segmentation of regions of interest in mammograms in a topographic approach. IEEE Trans Inf Technol Biomed 14(1):129CrossRefGoogle Scholar
  14. 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 diagnosisGoogle Scholar
  15. 15.
    Chen D, Chen YQ, Xue D, Pan F (2012) Adaptive image enhancement based on fractional differential mask. In: CCDC, vol 229, pp 1043–1047Google Scholar
  16. 16.
    Erçelebi E, Koç S (2006) Lifting-based wavelet domain adaptive wiener filter for image enhancement. IEE Proc Vis Image Signal Process 153(1):31–36CrossRefGoogle Scholar
  17. 17.
    Wuthrich M, Trimpe S, Kappler D, Schaal S (2015) A new perspective and extension of the Gaussian filter. Comput Sci 35(14)Google Scholar
  18. 18.
    Anh VV, Mcvinish R (2003) Fractional differential equations driven by Lévy noise. Int J Stoch Anal 16(2):97–119Google Scholar
  19. 19.
    Yi-Fei PU (2007) Application of fractional differential approach to digital image processing. J Sichuan Univ 39(3):124–132Google Scholar
  20. 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):491CrossRefGoogle Scholar
  21. 21.
    Eklund GW, Cardenosa G, Parsons W (1994) Assessing adequacy of mammographic image quality. Radiology 190(2):297–307CrossRefGoogle Scholar
  22. 22.
    Magid A, Rotman SR, Weiss AM (1990) Comments on picture thresholding using an iterative selection method. IEEE Trans Syst Man Cybern 20(5):1238–1239CrossRefGoogle Scholar
  23. 23.
    Boukamp BA (1986) A nonlinear least squares fit procedure for analysis of immittance data of electrochemical systems. Solid State Ionics 20(1):31–44CrossRefGoogle Scholar
  24. 24.
    Variable, R. (2013). Heaviside step function. BelowGoogle Scholar
  25. 25.
    Ruotsalainen K, Saranen J (1987) Some boundary element methods using Dirac’s distributions as trial functions. SIAM J Numer Anal 24(4):816–827CrossRefGoogle Scholar
  26. 26.
    Chan TF, Vese L (2001) Active contours without edges. IEEE Trans Image Process 10(2):266CrossRefGoogle Scholar
  27. 27.
    Yezzi A, Tsai A, Willsky A (2002) A fully global approach to image segmentation via coupled curve evolution equations. Academic Press Inc., New YorkGoogle Scholar
  28. 28.
    Mustra M, Grgic M (2013) Robust automatic breast and pectoral muscle segmentation from scanned mammograms. Signal Process 93(10):2817–2827CrossRefGoogle Scholar
  29. 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):232CrossRefGoogle Scholar
  30. 30.
    Huttenlocher DP, Klanderman GA, Rucklidge WJ (1993) Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell 15(9):850–863CrossRefGoogle Scholar
  31. 31.
    Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. MIT Press, CambridgeGoogle Scholar

Copyright information

© CARS 2018

Authors and Affiliations

  • Kaiming Yin
    • 1
  • Shiju Yan
    • 1
  • Chengli Song
    • 1
  • Bin Zheng
    • 2
  1. 1.School of Medical Instrument and Food EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina
  2. 2.School of Electrical and Computer EngineeringUniversity of OklahomaNormanUSA

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