Aesthetic Plastic Surgery

, Volume 35, Issue 3, pp 289–302 | Cite as

A Morphological Methodology for Three-dimensional Human Face Soft-tissue Landmarks Extraction: A Preliminary Study

  • F. Calignano
  • E. Vezzetti
Original Article


Assessment of facial soft tissues could be implemented using only anatomical landmarks. These points are so significant in the medical context because are able to provide significant information about the human face morphology and dimensions. At present their detection and location is made by expert physicians using palpation. Even if this procedure normally provides reliable information, the reliability of the results is proportional to the expertise of the physician. Considering that at present many physicians are beginning to use 3D scanners that provide three-dimensional data of the human face, it is possible to implement a robust and repeatable methodology that supports the physician’s diagnosis. To reach this goal it is necessary to implement a methodology based on geometrical codification of landmarks and which mathematically formalizes the physician’s visual and palpation analyses of the real patient.


Human face Soft tissues landmarks 3D scanners Automatic detection 



The authors want to thank Prof. G. Ramieri and Prof. L. Verzè of the “Università di Torino” whose collaboration with the authors in the LAFAV Laboratory, financed by Compagnia di San Paolo, provided precious suggestions and data for this study.


The authors have no conflicts of interest or financial ties to disclose.


  1. 1.
    Agarwal MG, Ancha C, Shah M, Puri A, Pai S (2007) Limb salvage surgery for osteosarcoma: effective low-cost treatment. Clin Orthop Relat Res 459:82–91PubMedCrossRefGoogle Scholar
  2. 2.
    Worz S, Rohr K (2006) Localization of anatomical point landmarks in 3-D medical images by fitting 3-D parametric intensity models. Med Image Anal 10(1):41–58PubMedCrossRefGoogle Scholar
  3. 3.
    Liu X, Kim W, Drerup B (2004) Foot 3D characterization and localization of anatomical landmarks of the foot by FASTscan. Real-Time Imaging 10(4):217–228CrossRefGoogle Scholar
  4. 4.
    Yahara H, Higuma N, Fukui Y, Nishihara S, Mochimaru M, Kouchi M (2005) Estimation of anatomical landmark positions from model of 3-dimensional foot by the FFD method. Syst Comput Jpn 36(6):26–38CrossRefGoogle Scholar
  5. 5.
    Griffin FM, Math K, Scuderi GR, Insall JN, Poilvache PL (2000) Anatomy of the epicondyles of the distal femur: MRI analysis of normal knees. J Arthroplasty 15(3):354–359PubMedCrossRefGoogle Scholar
  6. 6.
    van Sint JS (2007) Colour atlas of skeletal landmark definitions: guidelines for reproducible manual and virtual palpations. Churchill Livingstone/Elsevier, LondonGoogle Scholar
  7. 7.
    della Croce U, Cappozzo A, Kerrigan DC (1999) Pelvis and lower limb anatomical landmark calibration precision and its propagation to bone geometry and joint angles. Med Biol Eng Comput 37(1):155–161PubMedCrossRefGoogle Scholar
  8. 8.
    Yang J, Ling X, Lu Y, Wei M, Ding G (2001) Cephalometric image analysis and measurement for orthognathic surgery. Med Biol Eng Comput 39(3):279–284PubMedCrossRefGoogle Scholar
  9. 9.
    Maudgil DD, Free SL, Sisodiya SM, Lemieux L, Woermann FG, Fish DR, Shorvon SD (1998) Identifying homologous anatomical landmarks on reconstructed magnetic resonance images of the human cerebral cortical surface. J Anat 193(4):559–571PubMedCrossRefGoogle Scholar
  10. 10.
    Coombes AM, Moss JP, Linney AD, Richards R, James DR (1991) A mathematical method for comparison of three dimensional changes in the facial surface. Eur J Orthod 13:95–110PubMedGoogle Scholar
  11. 11.
    Yang X, Dong Y, Long X, Zhang G, Kao C (2005) The evaluation of jaw function subsequent to bilateral sagittal split osteotomy. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 100(1):10–16PubMedCrossRefGoogle Scholar
  12. 12.
    Hutton T (2004) Dense surface models of the human face, PhD thesis, University College LondonGoogle Scholar
  13. 13.
    Colbry D, Stockman G, Jain A (2006) Detection of anchor points for 3D face verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, New York, 17–22 June, 2006, pp 118–125Google Scholar
  14. 14.
    Moreno AB, Sanchez A, Velez JF, Diaz FJ (2003) Face recognition using 3D surface-extracted descriptors. In: Proceedings of the Irish machine vision and image processing conference, Coleraine, Northern Ireland, September 2003Google Scholar
  15. 15.
    Nair P, Zou L, Cavallaro A (2005) Facial scan change detection. In: Proceedings of the European workshop on the integration of knowledge, semantic and digital media technologies, London, December 2005, pp 77–82Google Scholar
  16. 16.
    Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models: their training and application. Comput Vis Image Underst 61(1):38–59CrossRefGoogle Scholar
  17. 17.
    Matthews I, Baker S (2004) Active appearance models revisited. Int J Comput Vis 60(2):135–164CrossRefGoogle Scholar
  18. 18.
    Blanz V, Vetter T (1999) A morphable model for the synthesis of 3D faces. In: Proceedings of the 26th annual conference on computer graphics and interactive techniques, Los Angeles, CA, August 1999, pp 187–194Google Scholar
  19. 19.
    Dickens MM, Gleason SS, Sari-Sarraf H (2002) Volumetric segmentation via 3D active shape models. In: Proceedings of the 5th IEEE southwest symposium on image analysis and interpretation, Sante Fe, NM, 7–9 April 2002, pp 248–252Google Scholar
  20. 20.
    Buchaillard S, Ong SH, Payan Y, Foong KWC (2004) Reconstruction of 3D tooth images. In: Proceedings of the IEEE international conference on image processing, Singapore, October 2004, pp 1077–1080Google Scholar
  21. 21.
    Thirion JP (1996) New feature points based on geometric invariants for 3D image registration. Int J Comput Vis 18(2):121–137CrossRefGoogle Scholar
  22. 22.
    Rohr K (1997) On 3D differential operators for detecting point landmarks. Image Vis Comput 15(3):219–233CrossRefGoogle Scholar
  23. 23.
    Frantz S, Rohr K, Stiehl HS (1998) Multi-step differential approaches for the localization of 3D point landmarks in medical images. J Comput Inform Technol 6(4):435–447Google Scholar
  24. 24.
    Hartkens T, Rohr K, Stiehl HS (2002) Evaluation of 3D operators for the detection of anatomical point landmarks in MR and CT images. Comput Vis Image Underst 85:1–19CrossRefGoogle Scholar
  25. 25.
    Beil W, Rohr K, Stiehl HS (1997) Investigation of approaches for the localization of anatomical landmarks in 3D medical images. In: Lemke HU, Vannier MW, Inamura K (eds) Proceedings of the computer assisted radiology and surgery (CAR’97). Elsevier Science, Berlin, pp 265–270Google Scholar
  26. 26.
    Nair P, Cavallaro A (2007) Region segmentation and feature point extraction on 3d faces using a point distribution model. In: IEEE International Conference on Image Processing, San Antonio, TX, 16–19 September 2007, pp 85–88Google Scholar
  27. 27.
    Moreno A, Sanchez A, Velez J, Diaz F (2004) Face recognition using 3d surface extracted descriptors. In: Proceedings of the Irish machine vision and image processing 2003, Coleraine, Northern IrelandGoogle Scholar
  28. 28.
    Flynn PJ, Jain AK (1989) On reliable curvature estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, San Diego, CA, 4–8 June 1989, pp 110–116Google Scholar
  29. 29.
    Besl PJ, Jain RC (1986) Invariant surface characteristics for 3D object recognition in range images. Comput Vision Graph 33:33–80CrossRefGoogle Scholar
  30. 30.
    Vemuri BC, Mitiche A, Aggarwal JK (1986) Curvature-based representation of objects from range data. Image Vision Comput 4:107–114CrossRefGoogle Scholar
  31. 31.
    Fan TJ, Medioni G, Nevatia R (1986) Description of surfaces from range data using curvature properties. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 86–91Google Scholar
  32. 32.
    Ittner DJ, Jain AK (1985) 3-D surface discrimination from local curvature measures. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 119–123Google Scholar
  33. 33.
    Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905CrossRefGoogle Scholar
  34. 34.
    Tang CK, Medioni G (1999) Robust estimation of curvature information from noisy 3D data for shape description. In: Proceedings of the 7th IEEE international conference on computer vision, pp 426–433Google Scholar
  35. 35.
    Trucco E, Fisher RB (1995) Experiments in curvature-based segmentation of range data. IEEE Trans Pattern Anal Mach Intell 17(2):177–182CrossRefGoogle Scholar
  36. 36.
    Angelopoulou E, Wolff L (1998) Sign of gaussian curvature from curve orientation in photometric space. IEEE Trans Pattern Anal Mach Intell 20:1056–1066CrossRefGoogle Scholar
  37. 37.
    Koenderink JJ, van Doorn AJ (1992) Surface shape and curvature scales. Image Vision Comput 10:557–565CrossRefGoogle Scholar
  38. 38.
    Hoffman D, Richards W (1984) Parts of recognition. Cognition 18:65–96PubMedCrossRefGoogle Scholar
  39. 39.
    Katz S, Tal A (2003) Hierarchical mesh decomposition using fuzzy clustering and cuts. ACM Trans Graph 22(3):954–961CrossRefGoogle Scholar
  40. 40.
    Katz S, Leifman G, Tal A (2005) Mesh segmentation using feature point and core extraction. Visual Comput 21(8–10):649–658CrossRefGoogle Scholar
  41. 41.
    Lee Y, Lee S, Shamir A, Cohen-Or D, Seidel HP (2005) Mesh scissoring with minima rule and part salience. Comput Aided Geom Des 22(5):444–465CrossRefGoogle Scholar
  42. 42.
    Lee Y, Lee S (2002) Geometric snakes for triangular meshes. Comput Graph Forum (Eurographics 2002) 21(3):229–238CrossRefGoogle Scholar
  43. 43.
    Liu R, Zhang H (2004) Segmentation of 3D meshes through spectral clustering. In: 12th Pacific conference on computer graphics and applications, 6–8 October 2004, pp 298–305Google Scholar
  44. 44.
    Page DL, Koschan A, Abidi M (2003) Perception-based 3D triangle mesh segmentation using fast marching watersheds. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, 18–20 June 2003, pp 27–32Google Scholar
  45. 45.
    Page DL, Koschan A, Abidi M, Zhang Y (2003) Object representation using the minima rule and superquadrics for under vehicle inspection. In: Proceedings of 1st IEEE Latin American conference on robotics and automation, pp 91–97Google Scholar
  46. 46.
    Zhang Y, Paik J, Koschan A, Abidi MA (2002) A simple and efficient algorithm for part decomposition of 3D triangulated models based on curvature analysis. In: Proceedings of the international conference on image processing, 24–28 June 2002, pp 273–276Google Scholar
  47. 47.
    Zhang H, Liu R (2005) Mesh segmentation via recursive and visually salient spectral cuts. In: Proceedings of vision, modeling, and visualization (VMV), Erlangen, Germany, pp 429–436Google Scholar
  48. 48.
    ISO/IEC Guide 43-1, Proficiency testing by interlaboratory comparisons—Part 1: development and operation of proficiency testing schemes. International Organization for Standardization, Geneva, 1997Google Scholar
  49. 49.
    Vezzetti E, Calignano F (2010) Soft tissue diagnosis in maxillofacial surgery: a preliminary study on three-dimensional face geometrical features based analysis. Aesthetic Plast Surg 34(2):200–211PubMedCrossRefGoogle Scholar
  50. 50.
    Docarmo M (1976) Differential geometry of curves and surfaces. Prentice-Hall, Englewood Cliffs, NJGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC and International Society of Aesthetic Plastic Surgery 2010

Authors and Affiliations

  1. 1.Dipartimento di Sistemi di Produzione Ed Economia dell’AziendaPolitecnico di TorinoTurinItaly

Personalised recommendations