Abstract
| Language | English |
|---|---|
| Pages | 1864-1870 |
| Number of pages | 7 |
| Journal | IEEE Transactions on Biomedical Engineering |
| Volume | 56 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - Jul 2009 |
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Keywords
- biomechanics
- biomedical measurement
- feature extraction
- image motion analysis
- medical disorders
- medical image processing
- muscle
- neurophysiology
- patient treatment
- pattern classification
- support vector machines
- video signal processing
Cite this
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Quantitative analysis of facial paralysis using local binary patterns in biomedical videos. / He, Shu; Soraghan, J.J.; O'Reilly, Brian; Xing, D.
In: IEEE Transactions on Biomedical Engineering, Vol. 56, No. 7, 07.2009, p. 1864-1870.Research output: Contribution to journal › Article
TY - JOUR
T1 - Quantitative analysis of facial paralysis using local binary patterns in biomedical videos
AU - He, Shu
AU - Soraghan, J.J.
AU - O'Reilly, Brian
AU - Xing, D.
PY - 2009/7
Y1 - 2009/7
N2 - Facial paralysis is the loss of voluntary muscle movement of 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 a novel framework for objective measurement of facial paralysis. The motion information in the horizontal and vertical directions and the appearance features on the apex frames are extracted based on the local binary patterns (LBPs) on the temporal-spatial domain in each facial region. These features are temporally and spatially enhanced by the application of novel block processing schemes. A multiresolution extension of uniform LBP is proposed to efficiently combine the micropatterns and large-scale patterns into a feature vector. The symmetry of facial movements is measured by the resistor-average distance (RAD) between LBP features extracted from the two sides of the face. Support vector machine is applied to provide quantitative evaluation of facial paralysis based on the House-Brackmann (H-B) scale. The proposed method is validated by experiments with 197 subject videos, which demonstrates its accuracy and efficiency.
AB - Facial paralysis is the loss of voluntary muscle movement of 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 a novel framework for objective measurement of facial paralysis. The motion information in the horizontal and vertical directions and the appearance features on the apex frames are extracted based on the local binary patterns (LBPs) on the temporal-spatial domain in each facial region. These features are temporally and spatially enhanced by the application of novel block processing schemes. A multiresolution extension of uniform LBP is proposed to efficiently combine the micropatterns and large-scale patterns into a feature vector. The symmetry of facial movements is measured by the resistor-average distance (RAD) between LBP features extracted from the two sides of the face. Support vector machine is applied to provide quantitative evaluation of facial paralysis based on the House-Brackmann (H-B) scale. The proposed method is validated by experiments with 197 subject videos, which demonstrates its accuracy and efficiency.
KW - biomechanics
KW - biomedical measurement
KW - feature extraction
KW - image motion analysis
KW - medical disorders
KW - medical image processing
KW - muscle
KW - neurophysiology
KW - patient treatment
KW - pattern classification
KW - support vector machines
KW - video signal processing
U2 - 10.1109/TBME.2009.2017508
DO - 10.1109/TBME.2009.2017508
M3 - Article
VL - 56
SP - 1864
EP - 1870
JO - IEEE Transactions on Biomedical Engineering
T2 - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 7
ER -