Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/36128
Title: Laryngeal lesion classification based on vascular patterns in contact endoscopy and narrow band imaging : manual versus automatic approach
Author(s): Esmaeili, Nazila
Illanes, Alfredo
Boese, AxelLook up in the Integrated Authority File of the German National Library
Davaris, NikolaosLook up in the Integrated Authority File of the German National Library
Arens, ChristophLook up in the Integrated Authority File of the German National Library
Navab, NassirLook up in the Integrated Authority File of the German National Library
Friebe, MichaelLook up in the Integrated Authority File of the German National Library
Issue Date: 2020
Type: Article
Language: English
URN: urn:nbn:de:gbv:ma9:1-1981185920-363617
Subjects: Laryngeal cancer
Contact endoscopy
Narrow band imaging
Abstract: Longitudinal and perpendicular changes in the vocal fold’s blood vessels are associated with the development of benign and malignant laryngeal lesions. The combination of Contact Endoscopy (CE) and Narrow Band Imaging (NBI) can provide intraoperative real-time visualization of the vascular changes in the laryngeal mucosa. However, the visual evaluation of vascular patterns in CE-NBI images is challenging and highly depends on the clinicians’ experience. The current study aims to evaluate and compare the performance of a manual and an automatic approach for laryngeal lesion’s classification based on vascular patterns in CE-NBI images. In the manual approach, six observers visually evaluated a series of CE+NBI images that belong to a patient and then classified the patient as benign or malignant. For the automatic classification, an algorithm based on characterizing the level of the vessel’s disorder in combination with four supervised classifiers was used to classify CE-NBI images. The results showed that the manual approach’s subjective evaluation could be reduced by using a computer-based approach. Moreover, the automatic approach showed the potential to work as an assistant system in case of disagreements among clinicians and to reduce the manual approach’s misclassification issue.
URI: https://opendata.uni-halle.de//handle/1981185920/36361
http://dx.doi.org/10.25673/36128
Open Access: Open access publication
License: (CC BY 4.0) Creative Commons Attribution 4.0(CC BY 4.0) Creative Commons Attribution 4.0
Sponsor/Funder: DFG-Publikationsfonds 2020
Journal Title: Sensors
Publisher: MDPI
Publisher Place: Basel
Volume: 20
Issue: 14
Original Publication: 10.3390/s20144018
Page Start: 1
Page End: 12
Appears in Collections:Medizinische Fakultät (OA)

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