Advertisement

Annals of Biomedical Engineering

, Volume 38, Issue 5, pp 1703–1718 | Cite as

Computational Tools for Quantitative Breast Morphometry Based on 3D Scans

  • D. Chen
  • D. R. Chittajallu
  • G. Passalis
  • I. A. Kakadiaris
Article

Abstract

Quantitative analysis of breast morphometry is critical to breast plastic surgery. Recently, three-dimensional (3D) photography has emerged as a strong new alternative for breast morphometry analysis in comparison to other existing techniques. 3D photography enables the capture of the entire breast surface topology virtually in a single snapshot and without any direct contact with the patient, thus causing minimal discomfort. In this paper, we present a set of computational tools for the quantitative analysis of two key morphological properties of the breast that are of interest to breast plastic surgery based on 3D scans, namely breast shape and volume. The breast shape is modeled using a compact geometric model capable of capturing the global shape of the breast with very few parameters. Specifically, the shape model is deduced by applying a set of five global deformations to a geometric primitive. These deformations, defined using very intuitive parameters, closely model the key shape variables that surgeons inherently use to describe the overall shape of the breast. Patient-specific parameters of the breast shape model are automatically recovered by fitting a generic breast shape model to the 3D scan of the patient’s breast using a physics-based deformable model framework. The mean error of fit between the automatically fitted shape model and the actual breast surface for 12 subjects varied between 0.9 and 2.6 mm. These results are very encouraging considering the fact that only 17 parameters are used to determine the shape of the breast. The breast volume is estimated automatically by first localizing the breast on a 3D scan of the patient’s torso and then computing the volume enclosed between an interpolated breast-less torso surface and the actual breast. The volume estimated by the proposed method was found to be within the intra-operator variability among five segmentation trials performed manually by an expert on 3D torso scans of three subjects.

Keywords

Breast shape model Breast plastic surgery Computer-aided Surgery Breast morphometry Deformable model Breast reconstruction Surgical planning Breast volume estimation 

Notes

Acknowledgments

This study was supported in part by NSF BES-0402591. Any opinions, findings, conclusions, or recommendations expressed in this material are of the authors and may not reflect the views of the sponsors. We would like to thank Dr. M. Miller and Dr. C. Patrick from the Department of Plastic Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, USA, for providing us with the data and their invaluable advice.

References

  1. 1.
    3dMD, 2009. Available from http://www.3dmd.com/.
  2. 2.
    Barr, A. Superquadrics and angle-preserving transformations. IEEE Comput. Graph. Appl. 1(1):11–23, 1981.CrossRefGoogle Scholar
  3. 3.
    Beattie, S., J. Siebert, J. Stevenson, and L. Yap. Assessment tools for breast surgery based on 3D surface anatomy imaging. Proceeding of the 6th International Workshop on Digital Mammography, Bremen, Germany, June 22–25, 2002.Google Scholar
  4. 4.
    Bostwick, J. Plastic and Reconstructive Breast Surgery, 2nd ed. St. Louis, MO: Quality Medical Publishing, Inc., 2000.Google Scholar
  5. 5.
    Brown, T., C. Ringrose, R. Hyland, A. Cole, and T. Brotherston. A method of assessing female breast morphometry and its clinical application. Br. J. Plast. Surg. 52(5):355–359, 1999.CrossRefPubMedGoogle Scholar
  6. 6.
    Catanuto, G., A. Spano, A. Pennati, E. Riggio, G. Farinella, G. Impoco, S. Spoto, G. Gallo, and M. Nava. Experimental methodology for digital breast shape analysis and objective surgical outcome evaluation. J. Plast. Reconstruct. Aesthet. Surg. 61(3):314–318, 2008.CrossRefGoogle Scholar
  7. 7.
    Chen, D., I. Kakadiaris, M. Miller, R. Loftin, and C. Patrick. Modeling for plastic and reconstructive breast surgery. Proceeding of the 3rd Medical Image Computing and Computer Assisted Intervention, Pittsburgh, PA, October 11–14, pp. 1040–1050, 2000.Google Scholar
  8. 8.
    Coons, S. Surfaces for computer-aided design of space forms. Massachusetts Institute of Technology, Cambridge, MA, Technical Report, 1967.Google Scholar
  9. 9.
    Cormen, T. H. Introduction to Algorithms, 2nd ed. Cambridge, MA: MIT Press, 2001.Google Scholar
  10. 10.
    Galdino, G., M. Nahabedian, M. Chiaramonte, J. Geng, S. Klatsky, and P. Manson. Clinical applications of three-dimensional photography in breast surgery. Plast. Reconstruct. Surg. 110(1):58–70, 2002.CrossRefGoogle Scholar
  11. 11.
    Kakadiaris, I. Motion-Based Part Segmentation, Shape and Motion Estimation of Multi-Part Objects: Application to Human Body Tracking. PhD dissertation, University of Pennsylvania, 1996.Google Scholar
  12. 12.
    Kim, M., G. Reece, E. Beahm, M. Miller, E. Atkinson, and M. Markey. Objective assessment of aesthetic outcomes of breast cancer treatment: measuring ptosis from clinical photographs. Comput. Biol. Med. 37(1):49–59, 2007.CrossRefPubMedGoogle Scholar
  13. 13.
    Kovacs, L., M. Eder, R. Hollweck, A. Zimmermann, M. Settles, A. Schneider, K. Udosic, K. Schwenzer-Zimmerer, N. Papadopulos, and E. Biemer. New aspects of breast volume measurement using 3-dimensional surface imaging. Ann. Plast. Surg. 57(6):602–610, 2006.CrossRefPubMedGoogle Scholar
  14. 14.
    Losken, A., I. Fishman, D. Denson, H. Moyer, and G. Carlson. An objective evaluation of breast symmetry and shape differences using 3-dimensional images. Ann. Plast. Surg. 55(6):571–575, 2005.CrossRefPubMedGoogle Scholar
  15. 15.
    Losken, A., H. Seify, D. Denson, A. Paredes, and G. Carlson. Validating three-dimensional imaging of the breast. Ann. Plast. Surg. 54(5):471–476, 2005.CrossRefPubMedGoogle Scholar
  16. 16.
    Metaxas, D. Physics-Based Modeling of Non-Rigid Objects for Vision and Graphics. PhD dissertation, Graduate Department of Computer Science, University of Toronto, 1992.Google Scholar
  17. 17.
    Metaxas, D., and I. Kakadiaris. Elastically adaptive deformable models. IEEE Trans. Pattern Anal. Mach. Intell. 24(10):1310–1321, 2002.CrossRefGoogle Scholar
  18. 18.
    Metaxas, D., and D. Terzopoulos. Shape and nonrigid motion estimation through physics-based synthesis. IEEE Trans. Pattern Anal. Mach. Intell. 15(6):580–591, 1993.CrossRefGoogle Scholar
  19. 19.
    Nahabedian, M., and G. Galdino. Symmetrical breast reconstruction: is there a role for three-dimensional digital photography? Plast. Reconstruct. Surg. 112(6):1582–1590, 2003.CrossRefGoogle Scholar
  20. 20.
    Park, J. Model-Based Shape and Motion Analysis: Left Ventricle of a Heart. Ph.D. dissertation, University of Pennsylvania, 1996.Google Scholar
  21. 21.
    Passalis, G., T. Theoharis, M. Miller, and I. Kakadiaris. Noninvasive automatic breast volume estimation for post-mastectomy breast reconstructive surgery. Proceeding of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2, Cancun, Mexico, September 17–20, pp. 1319–1322, 2003.Google Scholar
  22. 22.
    Regnault, P. Breast ptosis: definition and treatment. Clin. Plast. Surg. 3(2):193–203, 1976.PubMedGoogle Scholar
  23. 23.
    Rigotti, C., G. Ferrigno, A. Aliverti, and A. Pedotti. Surface scanning: an application to mammary surgery. J. Biomed. Opt. 3(2):161–170, 1998.CrossRefGoogle Scholar
  24. 24.
    Tanabe, Y., T. Honda, Y. Nakajima, H. Sakurai, and M. Nozaki. Intraoperative application of three-dimensional imaging for breast surgery. Scand. J. Plast. Reconstruct. Surg. Hand Surg. 39(6):349–352, 2005.CrossRefGoogle Scholar
  25. 25.
    Tepper, O., K. Small, L. Rudolph, M. Choi, and N. Karp. Virtual 3-dimensional modeling as a valuable adjunct to aesthetic and reconstructive breast surgery. Am. J. Surg. 192(4):548–551, 2006.CrossRefPubMedGoogle Scholar

Copyright information

© Biomedical Engineering Society 2010

Authors and Affiliations

  • D. Chen
    • 1
  • D. R. Chittajallu
    • 2
  • G. Passalis
    • 2
  • I. A. Kakadiaris
    • 2
  1. 1.Office of High Performance Computing and CommunicationsNational Library of MedicineBethesdaUSA
  2. 2.Computational Biomedicine LabUniversity of HoustonHoustonUSA

Personalised recommendations