Automatic recognizing of vocal fold disorders from glottis images. (September 2014)

• Record Type: Journal Article
• Authors: Huang, Chang-Chiun; Leu, Yi-Shing; Kuo, Chung-Feng Jeffrey; Chu, Wen-Lin; Chu, Yueng-Hsiang; Wu, Han-Cheng
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 228:Number 9(2014:Sep.)
• Page Start: 952
• Page End: 961
• Publication Date: 2014-09
• Subjects: Laryngeal video stroboscope -- glottis physiological parameters -- digital image processing -- laser projection marking module -- glottal area -- dynamic image search; Biomedical engineering -- Periodicals; Medical instruments and apparatus -- Periodicals; 610.28
• DOI: 10.1177/0954411914551851
• Languages: English
• ISSNs: 0954-4119

Abstract

The laryngeal video stroboscope is an important instrument to test glottal diseases and read vocal fold images and voice quality for physician clinical diagnosis. This study is aimed to develop a medical system with functionality of automatic intelligent recognition of dynamic images. The static images of glottis opening to the largest extent and closing to the smallest extent were screened automatically using color space transformation and image preprocessing. The glottal area was also quantized. As the tongue base movements affected the position of laryngoscope and saliva would result in unclear images, this study used the gray scale adaptive entropy value to set the threshold in order to establish an elimination system. The proposed system can improve the effect of automatically captured images of glottis and achieve an accuracy rate of 96%. In addition, the glottal area and area segmentation threshold were calculated effectively. The glottis area segmentation was corrected, and the glottal area waveform pattern was drawn automatically to assist in vocal fold diagnosis. When developing the intelligent recognition system for vocal fold disorders, this study analyzed the characteristic values of four vocal fold patterns, namely, normal vocal fold, vocal fold paralysis, vocal fold polyp, and vocal fold cyst. It also used the support vector machine classifier to identify vocal fold disorders and achieved an identification accuracy rate of 98.75%. The results can serve as a very valuable reference for diagnosis.