후두 내시경 영상에서의 성문 분할 및 성대 점막 형태의 정량적 평가
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이선민 | - |
dc.contributor.author | 오석 | - |
dc.contributor.author | 김영재 | - |
dc.contributor.author | 우주현 | - |
dc.contributor.author | 김광기 | - |
dc.date.accessioned | 2022-06-11T15:40:05Z | - |
dc.date.available | 2022-06-11T15:40:05Z | - |
dc.date.created | 2022-06-11 | - |
dc.date.issued | 2022-05 | - |
dc.identifier.issn | 1229-7771 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/84605 | - |
dc.description.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. | - |
dc.language | 한국어 | - |
dc.language.iso | ko | - |
dc.publisher | 한국멀티미디어학회 | - |
dc.relation.isPartOf | 멀티미디어학회논문지 | - |
dc.title | 후두 내시경 영상에서의 성문 분할 및 성대 점막 형태의 정량적 평가 | - |
dc.title.alternative | Segmentation of the Glottis and Quantitative Measurement of the Vocal Cord Mucosal Morphology in the Laryngoscopic Image | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 2 | - |
dc.identifier.bibliographicCitation | 멀티미디어학회논문지, v.25, no.5, pp.661 - 669 | - |
dc.identifier.kciid | ART002845374 | - |
dc.description.isOpenAccess | N | - |
dc.citation.endPage | 669 | - |
dc.citation.startPage | 661 | - |
dc.citation.title | 멀티미디어학회논문지 | - |
dc.citation.volume | 25 | - |
dc.citation.number | 5 | - |
dc.contributor.affiliatedAuthor | 이선민 | - |
dc.contributor.affiliatedAuthor | 오석 | - |
dc.contributor.affiliatedAuthor | 김영재 | - |
dc.contributor.affiliatedAuthor | 우주현 | - |
dc.contributor.affiliatedAuthor | 김광기 | - |
dc.subject.keywordAuthor | Laryngoscopy | - |
dc.subject.keywordAuthor | Vocal Cord | - |
dc.subject.keywordAuthor | Segmentation | - |
dc.subject.keywordAuthor | Quantitative measurement | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | Digital Image Processing | - |
dc.description.journalRegisteredClass | kci | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.