Skip to main content

Advertisement

SpringerLink
Log in

Automatic Detection of the Nipple in Screen-Film and Full-field Digital Mammograms Using a Novel Hessian-Based Method

  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

Automatic detection of the nipple in mammograms is an important step in computerized systems that combine multiview information for accurate detection and diagnosis of breast cancer. Locating the nipple is a difficult task owing to variations in image quality, presence of noise, and distortion and displacement of the breast tissue due to compression. In this work, we propose a novel Hessian-based method to locate automatically the nipple in screen-film and full-field digital mammograms (FFDMs). The method includes detection of a plausible nipple/retroareolar area in a mammogram using geometrical constraints, analysis of the gradient vector field by mean and Gaussian curvature measurements, and local shape-based conditions. The proposed procedure was tested on 566 mammographic images consisting of 372 randomly selected scanned films from two public databases (mini-MIAS and DDSM), and 194 digital mammograms acquired with a GE Senographe 2000D FFDM system. A radiologist independently marked the centers of the nipples for evaluation of the results. The average error obtained was 6.7 mm (22 pixels) with reference to the center of the nipple as identified by the radiologist. Only two out of the 566 detected nipples (0.35 %) had an error larger than 50 mm. The method was also directly compared with two other techniques for the detection of the nipple. The results indicate that the proposed method outperforms other algorithms presented in the literature and can be used to identify accurately the nipple on various types of mammographic images.

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

Similar content being viewed by others

References

  1. Van Engeland S, Timp S, Karssemeijer N: Finding corresponding regions of interest in mediolateral oblique and craniocaudal mammographic views. Med Phys 33(9):3203–3212, 2006

    Article  PubMed  Google Scholar 

  2. Yuan Y, Giger ML, Li H, Sennett C: Correlative feature analysis on FFDM. Med Phys 35(12):5490–5500, 2008

    Article  PubMed  Google Scholar 

  3. Zheng B, Tan J, Ganott MA, Chough DM, Gur D: Matching breast masses depicted on different views: a comparison of three methods. Acad Radiol 16(11):1338–1347, 2009

    Article  PubMed  Google Scholar 

  4. Paquerault S, Petrick N, Chan HP, Sahiner B, Helvie MA: Improvement of computerized mass detection on mammograms: fusion of two-view information. Med Phys 29(2):238–247, 2002

    Article  PubMed  Google Scholar 

  5. Rangayyan RM, Banik S, Desautels JEL: Computer-aided detection of architectural distortion in prior mammograms of interval cancer. J Digit Imag 23(5):611–631, 2010

    Article  Google Scholar 

  6. Rangayyan RM, Ferrari RJ, Frère AF: Analysis of bilateral asymmetry in mammograms using directional, morphological, and density features. J Electron Imaging 16(01):013003:1–013003:12, 2007

    Article  Google Scholar 

  7. Tzikopoulos SD, Mavroforakis ME, Georgiou HV, Dimitropoulos N, Theodoridis S: A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry. Comput Methods Programs Biomed 102(1):47–63, 2011

    Article  PubMed  Google Scholar 

  8. Yuan Y, Giger ML, Li H, Bhooshan N, Sennett CA: Correlative analysis of FFDM and DCE-MRI for improved breast CADx. J Med Biol Eng 32(1):42–50, 2012

    Article  Google Scholar 

  9. Kinoshita SK, de Azevedo-Marques PM, Pereira Jr, RR, Rodrigues JA, Rangayyan RM: Content-based retrieval of mammograms using visual features related to breast density patterns. J Digital Imaging 20(2):172–190, 2007

    Article  Google Scholar 

  10. Thomson JZ, Evans AJ, Pinder SE, Burrel HC, Wilson AR, Ellis IO: Growth pattern of ductal carcinoma in situ (DCIS): a retrospective analysis based on mammographic findings. Br J Cancer 85(2):225–227, 2001

    Article  PubMed  CAS  Google Scholar 

  11. Tabàr L, Tot T, Dean P: Breast cancer, the art and science of early detection with mammography: perception, interpretation, histopathologic correlation. Thieme, Stuttgart, Germany, 2005

    Google Scholar 

  12. Casti P, Mencattini A, Salmeri, M, Pepe ML, Mangieri F, Ancona A: Characterization of the breast region for computer assisted Tabar masking of paired mammographic images. CBMS 2012 25th International Symposium on

  13. Yin FF, Giger ML, Doi K, Vyborny CJ, Schmidt RA: Computerized detection of masses in digital mammograms: analysis of bilateral subtraction images. Med Phys 18(5):955–963, 1991

    Article  PubMed  CAS  Google Scholar 

  14. Méndez AJ, Tahoces PG, Lado MJ, Souto M, Correa JL, Vidal JJ: Automatic detection of breast border and nipple in digital mammograms. Comput Methods Programs Biomed 49(3):253–262, 1996

    Article  PubMed  Google Scholar 

  15. Chandrasekhar R, Attikiouzel Y: A simple method for automatically locating the nipple on mammograms. IEEE Trans Med Imag 16(5):483–494, 1997

    Article  CAS  Google Scholar 

  16. Zhou C, Chan HP, Paramagul C, Roubidoux MA, Sahiner B, Hadjiiski LM, Petrick N: Computerized nipple identification for multiple image analysis in computer-aided diagnosis. Med Phys 31(10):2871–2882, 2004

    Article  PubMed  Google Scholar 

  17. 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

    Article  PubMed  CAS  Google Scholar 

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

    Article  Google Scholar 

  19. Suckling J, Parker J, Dance DR, Astley S, Hutt I, Boggis CRM, Ricketts I, Stamakis E, Cerneaz N, Kok SL, Taylor P, Betal D, Savage J: The Mammographic Image Analysis Society digital mammogram database. Exerpta Med Int Congr Ser 1069:375–378, 1994

    Google Scholar 

  20. Heath M, Bowyer KW, Kopans D, Moore R, Kegelmeyer P Jr: The Digital Database for Screening Mammography. In: Proceedings of the 5th International Workshop on Digital Mammography (Toronto, Canada, 2000) Medical Physics Publishing, 2001, 212–218

  21. Ferrari RJ, Rangayyan RM, Desautels JE, Borges RA, Frère AF: Automatic identification of the pectoral muscle in mammograms. IEEE Trans Med Imag 23(2):232–245, 2004

    Article  CAS  Google Scholar 

  22. Chan TF, Vese LA: Active contours without edges. IEEE Trans Image Process 10(2):266–277, 2001

    Article  PubMed  CAS  Google Scholar 

  23. Mencattini A, Salmeri M, Casti P, Pepe ML, Mangieri F, Ancona A: Local active contour models and Gabor wavelets for an optimal breast region segmentation. Int J Comput Assist Radiol Surg 7(1):256–257, 2012

    Google Scholar 

  24. Wei J, Chan HP, Sahiner B, Zhou C, Hadjiiski LM, Roubidoux MA, Helvie MA: Computer-aided detection of breast masses on mammograms: dual system approach with two-view analysis. Med Phys 36(10):4451–4460, 2009

    Article  PubMed  Google Scholar 

  25. Mencattini A, Salmeri M: Breast masses detection using phase portrait analysis and fuzzy inference systems. Int J Comput Assist Radiol Surg 7(4):573–583, 2012

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

We thank the Diagnostic Radiology Unit, San Paolo Hospital of Bari, Italy, for providing the digital mammograms used in this work and Dr. Paulo Mazzoncini de Azevedo Marques of the University of São Paulo, Ribeirão Preto, Brasil, for providing the code of the method of Kinoshita et al. [17] used for comparison.

Author information

Authors and Affiliations

  1. Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy

    Paola Casti, Arianna Mencattini & Marcello Salmeri

  2. Radiology Unit Bari, San Paolo Hospital of Bari, Bari, Italy

    Antonietta Ancona, Fabio Felice Mangieri & Maria Luisa Pepe

  3. Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada

    Rangaraj Mandayam Rangayyan

Authors
  1. Paola Casti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arianna Mencattini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marcello Salmeri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Antonietta Ancona
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fabio Felice Mangieri
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Maria Luisa Pepe
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Rangaraj Mandayam Rangayyan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paola Casti.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Casti, P., Mencattini, A., Salmeri, M. et al. Automatic Detection of the Nipple in Screen-Film and Full-field Digital Mammograms Using a Novel Hessian-Based Method. J Digit Imaging 26, 948–957 (2013). https://doi.org/10.1007/s10278-013-9587-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-013-9587-6

Keywords

Search

Navigation