Abstract
Videokymographic (VKG) images of the human larynx are often used for automatic vibratory feature extraction for diagnostic purposes. One of the most challenging parameters to evaluate is the mucosal wave's presence and its lateral peaks' sharpness. Although these features can be clinically helpful and give an insight into the health and pliability of vocal fold mucosa, the identification and visual estimation of the sharpness can be challenging for human examiners and even more so for an automatic process. This work aims to create and validate a method that can automatically quantify the lateral peak sharpness from the VKG images using a convolutional neural network.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
