Abstract
Display Omitted A new tool is presented for objective measurements in facial reconstructive surgery.Tracking of user-defined anatomical points is performed in 3D using RGB-D cameras.Facegram, a new data report standard for facial reconstructive surgery, is proposed.Complete system is low cost, simple to use and adapted to clinical environment.Data acquisition/processing algorithms are encapsulated in user-friendly GUIs. Evaluation of effectiveness in reconstructive plastic surgery has become an increasingly important asset in comparing and choosing the most suitable medical procedure to handle facial disfigurement. Unfortunately, traditional methods to assess the results of surgical interventions are mostly qualitative and lack information about movement dynamics. Along with this, the few existing methodologies tailored to objectively quantify surgery results are not practical in the medical field due to constraints in terms of cost, complexity and poor suitability to clinical environment. These limitations enforce an urgent need for the creation of a new system to quantify facial movement and allow for an easy interpretation by medical experts. With this in mind, we present here a novel method capable of quantitatively and objectively assess complex facial movements, using a set of morphological, static and dynamic measurements. For this purpose, RGB-D cameras are used to acquire both color and depth images, and a modified block matching algorithm, combining depth and color information, was developed to track the position of anatomical landmarks of interest. The algorithms are integrated into a user-friendly graphical interface and the analysis outcomes are organized into an innovative medical tool, named facegram. This system was developed in close collaboration with plastic surgeons and the methods were validated using control subjects and patients with facial paralysis. The system was shown to provide useful and detailed quantitative information (static and dynamic) making it an appropriate solution for objective quantitative characterization of facial movement in a clinical environment.
- b0005 W. Hoffman, Reanimation of the paralyzed face, Otolaryngol. Clin. North Am., 25 (1992) 649-667.Google Scholar
- b0010 A.M. Kosins, K.A. Hurvitz, G.R. Evans, G.A. Wirth, Facial paralysis for the plastic surgeon, Can. J. Plast. Surg., 15 (2007) 77.Google ScholarCross Ref
- b0015 American Academy of Facial Plastic and Reconstructive Surgery, Facial Reconstructive Procedures, 2015. Available: <http://www.aafprs.org/patient/procedures/facial_reconst.html>.Google Scholar
- b0020 S.E. Coulson, G.R. Croxson, R.D. Adams, N.J. O'Dwyer, Reliability of the "Sydney", "Sunnybrook", and "House Brackmann" facial grading systems to assess voluntary movement and synkinesis after facial nerve paralysis, Otolaryngol.-Head Neck Surg., 132 (2005) 543-549.Google ScholarCross Ref
- b0025 R. Alsarraf, Outcomes research in facial plastic surgery: a review and new directions, Aesthetic Plast. Surg., 24 (2000) 192-197.Google ScholarCross Ref
- b0030 V. Meier-Gallati, H. Scriba, U. Fisch, Objective scaling of facial nerve function based on area analysis (OSCAR), Otolaryngol.-Head Neck Surg., 118 (1998) 545-550.Google Scholar
- b0035 M.J. Fields, N.S. Peckitt, Facial nerve function index: a clinical measurement of facial nerve activity in patients with facial nerve palsies, Oral Surg., Oral Med., Oral Pathol., 69 (1990) 681-682.Google ScholarCross Ref
- b0040 P.C. Johnson, H. Brown, W.M. Kuzon, R. Balliet, J.L. Garrison, J. Campbell, Simultaneous quantitation of facial movements: the maximal static response assay of facial nerve function, Ann. Plast. Surg., 32 (1994) 171-179.Google ScholarCross Ref
- b0045 M. Isono, K. Murata, H. Tanaka, M. Kawamoto, H. Azuma, An objective evaluation method for facial mimic motion, Otolaryngol.-Head Neck Surg., 114 (1996) 27-31.Google ScholarCross Ref
- b0050 D. Bray, D.K. Henstrom, M.L. Cheney, T.A. Hadlock, Assessing outcomes in facial reanimation: evaluation and validation of the SMILE system for measuring lip excursion during smiling, Arch. Facial Plast. Surg., 12 (2010) 352-354.Google ScholarCross Ref
- b0055 J.G. Neely, J.Y. Cheung, M. Wood, J. Byers, A. Rogerson, Computerized quantitative dynamic analysis of facial motion in the paralyzed and synkinetic face, Otol. Neurotol., 13 (1992) 97-107.Google Scholar
- b0060 E.W. Sargent, O.A. Fadhli, R.S. Cohen, Measurement of facial movement with computer software, Arch. Otolaryngol.-Head Neck Surg., 124 (1998) 313-318.Google ScholarCross Ref
- b0065 G.S. Wachtman, J.F. Cohn, J.M. VanSwearingen, E.K. Manders, Automated tracking of facial features in patients with facial neuromuscular dysfunction, Plast. Reconstr. Surg., 107 (2001) 1124-1133.Google ScholarCross Ref
- b0070 D.A. Wood, G.B. Hughes, M. Secic, T.L. Good, Objective measurement of normal facial movement with video microscaling, Otol. Neurotol., 15 (1994) 61-65.Google Scholar
- b0075 C.J. Linstrom, Objective facial motion analysis in patients with facial nerve dysfunction, The Laryngoscope, 112 (2002) 1129-1147.Google ScholarCross Ref
- b0080 T.A. Hadlock, L.S. Urban, Toward a universal, automated facial measurement tool in facial reanimation, Arch. Facial Plast. Surg., 14 (2012) 277-282.Google ScholarCross Ref
- b0085 R. Horta, P. Aguiar, D. Monteiro, A. Silva, J.M. Amarante, A facegram for spatial-temporal analysis of facial excursion: applicability in the microsurgical reanimation of long-standing paralysis and pretransplantation, J. Cranio-Maxillofacial Surg., 42 (2014) 1250-1259.Google ScholarCross Ref
- b0090 M. Frey, P. Giovanoli, H. Gerber, M. Slameczka, E. Stüssi, Three-dimensional video analysis of facial movements: a new method to assess the quantity and quality of the smile, Plast. Reconstr. Surg., 104 (1999) 2032-2039.Google ScholarCross Ref
- b0095 C.-H.J. Tzou, I. Pona, E. Placheta, A. Hold, M. Michaelidou, N. Artner, Evolution of the 3-dimensional video system for facial motion analysis: ten years' experiences and recent developments, Ann. Plast. Surg., 69 (2012) 173-185.Google ScholarCross Ref
- b0100 B. Hontanilla, C. Aubá, Automatic three-dimensional quantitative analysis for evaluation of facial movement, J. Plast., Reconstr. Aesthetic Surg., 61 (2008) 18-30.Google ScholarCross Ref
- b0105 S. Zhang, S.-T. Yau, High-resolution, real-time 3D absolute coordinate measurement based on a phase-shifting method, Opt. Express, 14 (2006) 2644-2649.Google ScholarCross Ref
- b0110 Z. Zhang, Microsoft kinect sensor and its effect, MultiMedia, IEEE, 19 (2012) 4-10. Google ScholarDigital Library
- b0115 G. Vineetha, C. Sreeji, J. Lentin, Face expression detection using Microsoft Kinect with the help of artificial neural network, Trends Innovative Comput. (2012).Google Scholar
- b0120 B. Seddik, H. Maâmatou, S. Gazzah, T. Chateau, N.E. Ben Amara, Unsupervised facial expressions recognition and avatar reconstruction from kinect, in: Systems, Signals & Devices (SSD), 2013 10th International Multi-Conference on, 2013, pp. 1-6.Google Scholar
- b0125 A. Zell, Real time face tracking and pose estimation using an adaptive correlation filter for human-robot interaction, in: Mobile Robots (ECMR), 2013 European Conference on, 2013, pp. 119-124.Google Scholar
- b0130 K.S. Arun, T.S. Huang, S.D. Blostein, Least-squares fitting of two 3-D point sets, Pattern Anal. Mach. Intell., IEEE Trans. (1987) 698-700. Google ScholarDigital Library
- b0135 J.W. House, D.E. Brackmann, Facial nerve grading system, Otolaryngol. Head Neck Surg., 93 (1985) 146-147.Google ScholarCross Ref
- b0140 B.G. Ross, G. Fradet, J.M. Nedzelski, Development of a sensitive clinical facial grading system, Otolaryngol.-Head Neck Surg., 114 (1996) 380-386.Google ScholarCross Ref
- b0145 Changing Faces, Statistics, 2014. Available: <https://www.changingfaces.org.uk/Health-Care-Professionals/Introduction-to-patient-needs/Statistics>.Google Scholar
- b0150 L.R. Tomat, R.T. Manktelow, Evaluation of a new measurement tool for facial paralysis reconstruction, Plast. Reconstr. Surg., 115 (2005) 696-704.Google ScholarCross Ref
- b0155 R.P. Mehta, S. Zhang, T.A. Hadlock, Novel 3-D video for quantification of facial movement, Otolaryngol.-Head Neck Surg., 138 (2008) 468-472.Google ScholarCross Ref
Index Terms
- Facegram - Objective quantitative analysis in facial reconstructive surgery
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