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

EURASIP Journal on Image and Video Processing volume 2007, Article number: 081282 (2007) Cite this article

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.

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Authors and Affiliations

  1. Department of Electronic and Electrical Engineering, University of Strathclyde, Royal College Building, Glasgow, G1 1XW, UK

    Shu He & JohnJ Soraghan

  2. Institute of Neurological Sciences, Southern General Hospital, 1345 Govan Road, Glasgow, G51 4TF, UK

    BrianF O'Reilly

Authors
  1. Shu He
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  2. JohnJ Soraghan
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  3. BrianF O'Reilly
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Correspondence to Shu He.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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He, S., Soraghan, J. & O'Reilly, B. Biomedical Image Sequence Analysis with Application to Automatic Quantitative Assessment of Facial Paralysis. J Image Video Proc 2007, 081282 (2007). https://doi.org/10.1155/2007/81282

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

Keywords

  • Radial Basis Function
  • Optical Flow
  • Facial Feature
  • Video Data
  • Motion Feature