EURASIP Journal on Image and Video Processing

Impact Factor 1.742

EURASIP Journal on Image and Video Processing Cover Image
Research Article
Open Access

Biomedical Image Sequence Analysis with Application to Automatic Quantitative Assessment of Facial Paralysis

EURASIP Journal on Image and Video Processing20072007:081282

https://doi.org/10.1155/2007/81282

©  He et al. 2007

Received: 26 February 2007

Accepted: 16 October 2007

Published: 2 December 2007

Abstract

Facial paralysis is a condition causing decreased movement on one side of the face. A quantitative, objective, and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents an approach based on the automatic analysis of patient video data. Facial feature localization and facial movement detection methods are discussed. An algorithm is presented to process the optical flow data to obtain the motion features in the relevant facial regions. Three classification methods are applied to provide quantitative evaluations of regional facial nerve function and the overall facial nerve function based on the House-Brackmann scale. Experiments show the radial basis function (RBF) neural network to have superior performance.

[12345678910111213141516171819202122232425]

Authors’ Affiliations

(1)
Department of Electronic and Electrical Engineering, University of Strathclyde
(2)
Institute of Neurological Sciences, Southern General Hospital

References

  1. Diamond C, Frew I: The Facial Nerve. Oxford University Press, Oxford, UK; 1979.Google Scholar
  2. House JW: Facial nerve grading systems. Laryngoscope 1983,93(8):1056-1069.View ArticleGoogle Scholar
  3. Beurskens CHG, Heymans PG: Positive effects of mime therapy on sequelae of facial paralysis: stiffness, lip mobility, and social and physical aspects of facial disability. Otology & Neurotology 2003,24(4):677-681. 10.1097/00129492-200307000-00024View ArticleGoogle Scholar
  4. Kahn JB, Gliklich RE: Validation of a patient-graded instrumnet for facial nerve paralysis: the FaCE scale. Laryngoscope 2001,111(3):387-398. 10.1097/00005537-200103000-00005View ArticleGoogle Scholar
  5. Linstrom J: Objective facial motion analysis in patients with facial nerve dysfunction. Laryngoscope 2002,112(7):1129-1147. 10.1097/00005537-200207000-00001View ArticleGoogle Scholar
  6. Scriba H, Stoeckli SJ, Veraguth D, Fisch U: Objective evaluation of normal facial function. Annals of Otology, Rhinology & Laryngology 1999,108(7, part 1):641-644.View ArticleGoogle Scholar
  7. Dulguerov P, Marchal F, Wang D: Review of objective topographic facial nerve evaluation methods. American Journal of Otology 1999,20(5):672-678.Google Scholar
  8. McGrenary S, O'Reilly BF, Soraghan JJ: Objective grading of facial paralysis using artificial intelligence analysis of video data. Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems(CBMS '05), June 2005, Dublin, Ireland 587-592.View ArticleGoogle Scholar
  9. Neely JG, Joaquin AH, Kohn LA, Cheung JY: Quantitative assessment of the variation within grades of facial paralysis. Laryngoscope 1996,106(4):438-442. 10.1097/00005537-199604000-00009View ArticleGoogle Scholar
  10. Helling TD, Neely JG: Validation of objective measures for facial paralysis. Laryngoscope 1997,107(10):1345-1349. 10.1097/00005537-199710000-00010View ArticleGoogle Scholar
  11. Neely JG: Advancement in the evaluation of facial function. In Advances in Otolaryngology—Head and Neck Surgery. Volume 15. Elsevier Science, New York, NY, USA; 2002:109-134.Google Scholar
  12. Wachtman GS, Liu Y, Zhao T, et al.: Measurement of asymmetry in persons with facial paralysis. Proceedings of Combined Annual Conference of the Robert H. Ivy and Ohio Valley Societies of Plastic and Reconstructive Surgeons, June 2002, Pittsburgh, Pa, USAGoogle Scholar
  13. Liu Y, Schmidt KL, Cohn JF, Mitra S: Facial asymmetry quantification for expression invariant human identification. Computer Vision and Image Understanding 2003,91(1-2):138-159. 10.1016/S1077-3142(03)00078-XView ArticleGoogle Scholar
  14. Yang M-H, Kriegman DJ, Ahuja N: Detecting faces in images: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002,24(1):34-58. 10.1109/34.982883View ArticleGoogle Scholar
  15. Yang MH: Recent advances in face detection. Proceedings of 17th International Conference on Pattern Recognition (ICPR '04), August 2004, Cambridge, UKGoogle Scholar
  16. Feng GC, Yuen PC: Multi-cues eye detection on gray intensity image. Pattern Recognition 2001,34(5):1033-1046. 10.1016/S0031-3203(00)00042-XView ArticleMATHGoogle Scholar
  17. Rurainsky J, Eisert P: Template-based eye and mouth detection for 3D video conferencing. In Visual Content Processing and Representation, Lecture Notes in Computer Science. Volume 2849. Springer, Berlin, Germany; 2003:23-31. 10.1007/978-3-540-39798-4_6View ArticleGoogle Scholar
  18. Farkas LG: Anthropometry of the Head and Face. Raven Press, New York, NY, USA; 1995.Google Scholar
  19. Smith SM, Brady JM: SUSAN—a new approach to low level image processing. International Journal of Computer Vision 1997,23(1):45-78. 10.1023/A:1007963824710View ArticleGoogle Scholar
  20. Hess M, Martinez G: Facial feature extraction based on the smallest univalue segment assimilating nucleus (SUSAN) algorithm. Proceedings of the Picture Coding Symposium (PCS '04), December 2004, San Francisco, Calif, USAGoogle Scholar
  21. Guestrin C, Cozman F: Image stabilisation for feature tracking and generation of stable video overlays. In Tech. Rep. CMU-RI-TR-97-42. Robotics Institute, Carnegie Mellon University, Pittsburgh, Pa, USA; 1997.Google Scholar
  22. Elad M, Feuer A: Recursive optical flow estimation—adaptive filtering approach. Proceedings of the 19th Convention of Electrical and Electronics Engineers (EEIS '96), November 1996, Jerusalem, Israel 387-390.View ArticleGoogle Scholar
  23. Barron JL, Fleet DJ: Performance of optical flow techniques. International Journal of Computer Vision 1994,12(1):43-77. 10.1007/BF01420984View ArticleGoogle Scholar
  24. Baker S, Matthews I: Lucas-Kanade 20 years on: a unifying framework. International Journal of Computer Vision 2004,56(3):221-255.View ArticleGoogle Scholar
  25. Duch W, Jankowski N: Transfer function : hidden possibilities for better neural networks. Proceedings of the 16th European Symposium on Artificcial Neural Networks Bruges (ESANN '01), April 2001, Bruges, Belgium 81-94.Google Scholar

Copyright

© He et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.