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Assessment for facial nerve paralysis based on facial asymmetry

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Abstract

Facial nerve paralysis (FNP) is a loss of facial movement due to facial nerve damage, which will lead to significant physical pain and abnormal function in patients. Traditional FNP grading methods are solely based on clinician’s judgment and are time-consuming and subjective. Hence, an accurate, quantitative and objective method of evaluating FNP is proposed for constructing a standard system, which will be an invaluable tool for clinicians who treat the patient with FNP. In this paper, we introduce a novel method for quantitative assessment of FNP which combines an effective facial landmark estimation (FLE) algorithm and facial asymmetrical feature (FAF) by processing facial movement image. The facial landmarks can be detected automatically and accurately using FLE. The FAF is based on the angle of key facial landmark connection and mirror degree of multiple regions on human face. Our method provides significant contribution as it describes the displacement of facial organ and the changes of facial organ exposure during performing facial movements. Experiments show that our method is effective, accurate and convenient in practice, which is beneficial to FNP diagnosis and personalized rehabilitation therapy for each patient.

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References

  1. Neely JG et al (2010) Computerized objective measurement of facial motion: normal variation and test–retest reliability. Otol Neurotol 31(9):1488–1492

    PubMed  Google Scholar 

  2. House JW, Brackmann DE (1985) Facial nerve grading system. Otolaryngology 93(2):146–147

    CAS  Google Scholar 

  3. Fields MJ, Peckitt NS (1990) Facial nerve function index: a clinical measurement of facial nerve activity in patients with facial nerve palsies. Oral Surg Oral Med Oral Pathol 69(6):681–682

    Article  CAS  PubMed  Google Scholar 

  4. Ross BG, Fradet G, Nedzelski JM (1996) Development of a sensitive clinical facial grading system. Otolaryngology 114(3):380–386

    CAS  Google Scholar 

  5. Neely JG et al (2010) Sunnybrook facial grading system: reliability and criteria for grading. Laryngoscope 120(5):1038–1045

    PubMed  Google Scholar 

  6. Johnson PC et al (1994) Simultaneous quantitation of facial movements: the maximal static response assay of facial nerve function. Ann Plast Surg 32(2):171–179

    Article  CAS  PubMed  Google Scholar 

  7. Samsudin WSW, Sundaraj K (2012) Image processing on facial paralysis for facial rehabilitation system: a review. Control System, Computing and Engineering (ICCSCE), 2012 IEEE International Conference on IEEE

  8. Hadlock TA, Urban LS (2012) Toward a universal, automated facial measurement tool in facial re-animation. Arch Facial Plast Surg 14(4):277–282

    Article  PubMed  Google Scholar 

  9. Liu L, Cheng G, Dong J et al (2010) Evaluation of facial paralysis degree based on regions. In Knowledge Discovery and Data Mining, 2010. WKDD’10. Third International Conference on IEEE, pp 514–517

  10. Anguraj K, Padma S (2012) Analysis of facial paralysis disease using image processing technique. Int J Comput Appl 54(11):1–4

    Google Scholar 

  11. Dong J, Ma L, Li Q et al (2008) An approach for quantitative evaluation of the degree of facial paralysis based on salient point detection. In Intelligent Information Technology Application Workshops, 2008. IITAW’08. International Symposium on IEEE, pp 483–486

  12. Neely JG, Joaquin AH, Kohn LA et al (1996) Quantitative assessment of the variation within grades of facial paral-ysis. Laryngoscope 106(4):438–442

    Article  CAS  PubMed  Google Scholar 

  13. Helling TD, Gail Neely J (1997) Validation of objective measures for facial paralysis. Laryngoscope 107(10):1345–1349

    Article  CAS  PubMed  Google Scholar 

  14. Neely JG (2002) Advancement in the evaluation of facial function. Adv Otolaryngol 15:109–134

    Google Scholar 

  15. Mc Grenary S, O’Reilly BF, Soraghan JJ (2005) Objective grading of facial paralysis using artificial intelligence analy-sis of video data. In 18th IEEE Symposium on Computer-Based Medical Systems (CBMS’05), IEEE, pp 587–592

  16. He S, Soraghan J, O’Reilly B (2007) Biomedical image sequence analysis with application to automatic quantitative assessment of facial paralysis. Hindawi Publishing Corp, Cairo

    Google Scholar 

  17. Wachtman GS et al (2002) Measurement of asymmetry in persons with facial paralysis. Proceedings of Com-bined Annual Conference of the Robert H. Ivy and Ohio Valley Societies of Plastic and Reconstructive Sur-geons

  18. Liu Y et al (2003) “Facial asymmetry quantification for expression invariant human identification. Comput Vis Image Underst 91(1):138–159

    Article  Google Scholar 

  19. He S et al (2009) Quantitative analysis of facial paralysis using local binary patterns in biomedical videos. IEEE Trans Biomed Eng 56(7):1864–1870

    Article  PubMed  Google Scholar 

  20. Wang T, Zhang S, Dong J et al (2016) Automatic evaluation of the degree of facial nerve paralysis. Multimed Tools Appl 75(19):11893–11908

    Article  Google Scholar 

  21. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on IEEE, vol 1, pp 886–893

  22. Wang T, Dong J, Sun X et al (2014) Automatic recognition of facial movement for paralyzed face. Biomed Mater Eng 24(6):2751–2760

    PubMed  Google Scholar 

  23. Cootes TF, Taylor CJ, Cooper DH et al (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59

    Article  Google Scholar 

  24. Kazemi V, Sullivan J (2014) One millisecond face alignment with an ensemble of regression trees. In CVPR, IEEE Computer Society

  25. King DE (2009) Dlib-ml: a machine learning toolkit. J Mach Learn Res 10:1755–1758

    Google Scholar 

  26. Belhumeur PN, Jacobs DW, Kriegman DJ et al (2011) Localizing parts of faces using a consensus of exemplars. In Computer Vision and Pattern Recognition, IEEE, pp 545–552

  27. Le V, Brandt J, Bourdev L et al (2012) Interactive facial feature localization. In European Conference on Computer Vision. Springer, Berlin, pp 679–692

  28. Davis TA, Ramanujacharyulu C (1971) Statistical analysis of bilateral symmetry in plant organs. Sankhyā Ind J Stat Ser B 1:259–290

    Google Scholar 

  29. Endress PK (1999) Symmetry in flowers: diversity and evolution. Int J Plant Sci 160(S6):S3–S23

    Article  CAS  PubMed  Google Scholar 

  30. Hönekopp J, Bartholomé T, Jansen G (2004) Facial attractiveness, symmetry, and physical fitness in young women. Hum Nat 15(2):147–167

    Article  PubMed  Google Scholar 

  31. Rhodes G, Proffitt F, Grady JM et al (1998) Facial symmetry and the perception of beauty. Psychon Bull Rev 5(4):659–669

    Article  Google Scholar 

  32. Thornhill R, Gangestad SW (1999) Facial attractiveness. Trends Cogn Sci 3(12):452–460

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This study is financially supported by National Natural Science Foundation of China (Grant No. 91630206). Their support is greatly appreciated.

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Song Anping, Xu Guoliang, Ding Xuehai, Song Jiaxin & Zhang Wu

  2. Department of Rehabilitation, Shanghai Tenth People’s Hospital, Shanghai, 200072, China

    Xu Gang

Authors
  1. Song Anping
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  2. Xu Guoliang
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  3. Ding Xuehai
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  4. Song Jiaxin
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  5. Xu Gang
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  6. Zhang Wu
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Correspondence to Song Anping.

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All authors declare they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Informed consent was obtained from all individual participants included in the study.

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Anping, S., Guoliang, X., Xuehai, D. et al. Assessment for facial nerve paralysis based on facial asymmetry. Australas Phys Eng Sci Med 40, 851–860 (2017). https://doi.org/10.1007/s13246-017-0597-4

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