Summary
High-speed digital imaging can provide valuable information on disordered voice production
in voice science. However, the large amounts of high-speed image data with limited
image resolutions produce significant challenges for computer analysis, and thus effective
and efficient image edge extraction methods allowing for the batch analysis of high-speed
images of vocal folds is clinically important. In this paper, a novel algorithm for
automatic image edge detection is proposed to effectively and efficiently process
high-speed images of the vocal folds. The method integrates Lagrange interpolation,
differentiation, and Canny edge detection, which allow objective extraction of aperiodic
vocal fold vibratory patterns from large numbers of high-speed digital images. This
method and two other popular algorithms, histogram and active contour, are performed
on 10 sets of high-speed video data from excised larynx experiments to compare their
performances in analyzing high-speed images. The accuracy in computing glottal area
and the computation time of these methods are investigated. The results show that
our proposed method provides the most accurate and efficient detection, and is applicable
when processing low-resolution images. In this study, we focus on developing a method
to effectively and efficiently process high-speed image data from excised larynges.
However, in addition we show the clinical potential of this method by use of example
high-speed image data obtained from a patient with vocal nodules.The proposed automatic
image-processing algorithm may provide a valuable biomedical application for the clinical
assessment of vocal disorders by use of high-speed digital imaging.
Key Words
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Article info
Publication history
Published online: May 27, 2008
Accepted:
March 14,
2008
Identification
Copyright
© 2010 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
