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http://dx.doi.org/10.9717/kmms.2022.25.5.661

Segmentation of the Glottis and Quantitative Measurement of the Vocal Cord Mucosal Morphology in the Laryngoscopic Image  

Lee, Seon Min (Dept. of Nursing, College of Nursing, Gachon University)
Oh, Seok (Dept. of Biomedical Engineering, Gil Medical Center, College of Medicine, Gachon University)
Kim, Young Jae (Dept. of Biomedical Engineering, Gil Medical Center, College of Medicine, Gachon University)
Woo, Joo Hyun (Dept. of Otorhinolaryngology-Head and Neck Surgery, Gil Medical Center, College of Medicine, Gachon University)
Kim, Kwang Gi (Dept. of Health Sciences & Technology, Gachon Advanced Institute for Health Sciences & Tecnology (GAIHST), Gachon University)
Publication Information
Journal of Korea Multimedia Society / v.25, no.5, 2022 , pp. 661-669 More about this Journal
Abstract
The purpose of this study is to compare and analyze Deep Learning (DL) and Digital Image Processing (DIP) techniques using the results of the glottis segmentation of the two methods followed by the quantification of the asymmetric degree of the vocal cord mucosa. The data consists of 40 normal and abnormal images. The DL model is based on Deeplab V3 architecture, and the Canny edge detector algorithm and morphological operations are used for the DIP technique. According to the segmentation results, the average accuracy of the DL model and the DIP was 97.5% and 94.7% respectively. The quantification results showed high correlation coefficients for both the DL experiment (r=0.8512, p<0.0001) and the DIP experiment (r=0.7784, p<0.0001). In the conclusion, the DL model showed relatively higher segmentation accuracy than the DIP. In this paper, we propose the clinical applicability of this technique applying the segmentation and asymmetric quantification algorithm to the glottal area in the laryngoscopic images.
Keywords
Laryngoscopy; Vocal Cord; Segmentation; Quantitative measurement; Deep Learning; Digital Image Processing;
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