Detection of Temporomandibular Disorder from Facial Pattern

Author

Ghodsi, M. ; Sanei, S. ; Hicks, Y. ; Lee, T. ; Dunne, S.

Author_Institution

Cardiff Univ., Cardiff

fYear

2007

fDate

1-4 July 2007

Firstpage

151

Lastpage

154

Abstract

The analysis of temporomandibular disorder (TMD) is a challenging problem. This paper describes a new approach for detecting TMD. A measure of nonlinear dynamics of the variations in the movement of colour markers positioned on the subjects´ faces was obtained via estimating the maximum Lya- punov exponent. Static features such as the number of outliers and kurtosis have also been evaluated. Support vector machine (SVM) classifier was used to distinguish patients with TMD and healthy subjects on the basis of the values of the above features. Accuracy of 100% has been achieved on a limited data set.

Keywords

Lyapunov methods; feature extraction; image classification; medical image processing; support vector machines; video signal processing; SVM; colour markers; facial movement; facial pattern; kurtosis; maximum Lyapunov exponent; outliers; static features; support vector machine classifier; temporomandibular disorder detection; video sequences; Chaos; Dentistry; Digital signal processing; Face detection; Masticatory muscles; Nearest neighbor searches; Pain; Support vector machine classification; Support vector machines; Video sequences; Bootstrap; Maximum Lyapunov exponent; SVM; TMD;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Signal Processing, 2007 15th International Conference on

Conference_Location

Cardiff

Print_ISBN

1-4244-0882-2

Electronic_ISBN

1-4244-0882-2

Type

conf

DOI

10.1109/ICDSP.2007.4288541

Filename

4288541