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Automatic Pectoral Muscle Region Segmentation in Mammograms Using Genetic Algorithm and Morphological Selection

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Abstract

In computer-aided diagnosis systems for breast mammography, the pectoral muscle region can easily cause a high false positive rate and misdiagnosis due to its similar texture and low contrast with breast parenchyma. Pectoral muscle region segmentation is a crucial pre-processing step to identify lesions, and accurate segmentation in poor-contrast mammograms is still a challenging task. In order to tackle this problem, a novel method is proposed to automatically segment pectoral muscle region in this paper. The proposed method combines genetic algorithm and morphological selection algorithm, incorporating four steps: pre-processing, genetic algorithm, morphological selection, and polynomial curve fitting. For the evaluation results on different databases, the proposed method achieves average FP rate and FN rate of 2.03 and 6.90% (mini MIAS), 1.60 and 4.03% (DDSM), and 2.42 and 13.61% (INBreast), respectively. The results can be comparable performance in various metrics over the state-of-the-art methods.

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References

  1. Stewart B, Wild CP, et al (2017) World cancer report 2014, Health

  2. World Health Organization Breast cancer (2017). [Online]. Available: http://www.who.int/cancer/prevention/diagnosis-screening/breast-cancer/en/

  3. World Health Organization, et al. (2014) WHO position paper on mammography screening. World Health Organization

  4. Kwok S, Chandrasekhar R, Attikiouzel Y Automatic pectoral muscle segmentation on mammograms by straight line estimation and cliff detection. In: Intelligent Information Systems Conference, The Seventh Australian and New Zealand 2001. IEEE, 2001, pp 67–72

  5. Kwok SM, Chandrasekhar R, Attikiouzel Y, Rickard MT: Automatic pectoral muscle segmentation on mediolateral oblique view mammograms. IEEE Trans Med Imaging 23 (9): 1129–1140, 2004

  6. Ferrari RJ, Rangayyan RM, Desautels JL, Borges R, Frere AF: Automatic identification of the pectoral muscle in mammograms. IEEE Trans Med Imaging 23 (2): 232–245, 2004

  7. Kinoshita SK, Azevedo-Marques PM, Pereira RR, Rodrigues JAH, Rangayyan RM: Radon-domain detection of the nipple and the pectoral muscle in mammograms. J Digit Imaging 21 (1): 37–49, 2008

  8. Chakraborty J, Mukhopadhyay S, Singla V, Khandelwal N, Bhattacharyya P: Automatic detection of pectoral muscle using average gradient and shape based feature. J Digit Imaging 25 (3): 387–399, 2012

  9. Raba D, Oliver A, Martí J, Peracaula M, Espunya J (2005) Breast segmentation with pectoral muscle suppression on digital mammograms. Pattern Recognition and Image Analysis, pp 153–158

  10. Nagi J, Kareem SA, Nagi F, Ahmed SK Automated breast profile segmentation for ROI detection using digital mammograms. In: 2010 IEEE EMBS conference on biomedical engineering and sciences (IECBES). IEEE, 2010, pp 87–92

  11. Chen Z, Zwiggelaar R A combined method for automatic identification of the breast boundary in mammograms. In: 2012 5th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2012, pp 121– 125

  12. Maitra IK, Nag S, Bandyopadhyay SK: Technique for preprocessing of digital mammogram. Comput Methods Prog Biomed 107 (2): 175–188, 2012

  13. Rampun A, Morrow PJ, Scotney BW, Winder J (2017) Fully automated breast boundary and pectoral muscle segmentation in mammograms. Artificial Intelligence in Medicine

  14. Czaplicka K, Włodarczyk H., et al: Automatic breast-line and pectoral muscle segmentation. Schedae Informaticae 2011 (20): 195–209, 2012

  15. Camilus KS, Govindan V, Sathidevi P: Computer-aided identification of the pectoral muscle in digitized mammograms. J Digit Imaging 23 (5): 562–580, 2010

  16. Camilus KS, Govindan V, Sathidevi P: Pectoral muscle identification in mammograms. J Appl Clin Med Phys 12 (3): 215–230, 2011

  17. Liu L, Liu Q, Lu W: Pectoral muscle detection in mammograms using local statistical features. J Digit Imaging 27 (5): 633–641, 2014

  18. Vikhe P, Thool V: Intensity based automatic boundary identification of pectoral muscle in mammograms. Proc. Comput. Sci. 79: 262–269, 2016

  19. Sreedevi S, Sherly E: A novel approach for removal of pectoral muscles in digital mammogram. Proc. Comput. Sci. 46: 1724–1731, 2015

  20. Yoon WB, Oh JE, Chae EY, Kim HH, Lee SY, Kim KG Automatic detection of pectoral muscle region for computer-aided diagnosis using MIAS mammograms. BioMed research international, 2016

  21. Xu W, Li L, Liu W A novel pectoral muscle segmentation algorithm based on polyline fitting and elastic thread approaching. In: 2007 The 1st international conference on bioinformatics and biomedical engineering, 2007. ICBBE. IEEE, 2007, pp 837– 840

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

  23. Chen C, Liu G, Wang J, Sudlow G: Shape-based automatic detection of pectoral muscle boundary in mammograms. J Med Biol Eng 35 (3): 315–322, 2015

  24. Mustra M, Grgic M, Rangayyan RM: Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms. Med Biol Eng Comput 54 (7): 1003–1024, 2016

  25. Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, Ricketts I, Stamatakis E, Cerneaz N, Kok S, et al. The mammographic image analysis society digital mammogram database. In: Exerpta Medica. International Congress Series, vol 1069, 1994, pp 375–378

  26. Ma F, Bajger M, Slavotinek JP, Bottema MJ: Two graph theory based methods for identifying the pectoral muscle in mammograms. Pattern Recogn 40 (9): 2592–2602, 2007

  27. Iglesias JE, Karssemeijer N: Robust initial detection of landmarks in film-screen mammograms using multiple FFDM atlases. IEEE Trans Med Imaging 28 (11): 1815–1824, 2009

  28. Oliver A, Lladó X, Torrent A, Martí J One-shot segmentation of breast, pectoral muscle, and background in digitised mammograms. In: 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014, pp 912–916

  29. Zhou C, Wei J, Chan H.-P., Paramagul C, Hadjiiski LM, Sahiner B, Douglas JA: Computerized image analysis: Texture-field orientation method for pectoral muscle identification on MLO-view mammograms. Med Phys 37 (5): 2289–2299, 2010

  30. Masters BR, Gonzalez RC, Woods R: Digital image processing. J sBiomed Opt 14 (2): 029901, 2009

  31. Otsu N: A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9 (1): 62–66, 1979

  32. Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer WP The digital database for screening mammography, in Proceedings of the 5th international workshop on digital mammography, Medical Physics Publishing, 2000, pp 212–218

  33. Hammouche K, Diaf M, Siarry P: A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation. Comput Vis Image Underst 109 (2): 163–175, 2008

  34. Ergen B (2012) Signal and image denoising using wavelet transform. In: Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology, InTech

  35. Yen J-C, Chang F-J, Chang S: A new criterion for automatic multilevel thresholding. IEEE Trans Image Process 4 (3): 370–378, 1995

  36. Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS: Inbreast: toward a full-field digital mammographic database. Acad Radiol 19 (2): 236–248, 2012

  37. Gower JC: Measures of similarity, dissimilarity and distance. Encyclopedia of Statistical Sciences, Johnson and CB Read 5: 397–405, 1985

  38. Gardner A, Kanno J, Duncan CA, Selmic R Measuring distance between unordered sets of different sizes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp 137–143

  39. Jaccard P: Étude comparative de la distribution florale dans une portion des alpes et des jura. Bull Soc Vaudoise Sci Nat 37: 547–579, 1901

  40. Kosub S (2016) A note on the triangle inequality for the Jaccard distance. arXiv:1612.02696

  41. Henrikson J: Completeness and total boundedness of the Hausdorff metric. MIT Undergraduate J Math 1: 69–80, 1999

  42. PyWavelets development team, Pywavelets, 2017. [Online]. Available: https://github.com/PyWavelets/pywt

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Authors and Affiliations

  1. Key Laboratory of Information Storage System, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, People’s Republic of China

    Rongbo Shen & Ke Zhou

  2. Tencent Inc., Shenzhen, China

    Kezhou Yan, Fen Xiao, Jia Chang & Cheng Jiang

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  1. Rongbo Shen
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  2. Kezhou Yan
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  3. Fen Xiao
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  4. Jia Chang
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  5. Cheng Jiang
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  6. Ke Zhou
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Correspondence to Rongbo Shen.

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Shen, R., Yan, K., Xiao, F. et al. Automatic Pectoral Muscle Region Segmentation in Mammograms Using Genetic Algorithm and Morphological Selection. J Digit Imaging 31, 680–691 (2018). https://doi.org/10.1007/s10278-018-0068-9

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  • DOI: https://doi.org/10.1007/s10278-018-0068-9

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