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Open AccessArticle

Laryngeal Lesion Classification Based on Vascular Patterns in Contact Endoscopy and Narrow Band Imaging: Manual Versus Automatic Approach

1
INKA-Application Driven Research, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany
2
Department of Otorhinolaryngology, Head and Neck Surgery, Magdeburg University Hospital, 39120 Magdeburg, Germany
3
Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University Munich, 85748 Munich, Germany
4
IDTM GmbH, 45657 Recklinghausen, Germany
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(14), 4018; https://doi.org/10.3390/s20144018
Received: 29 May 2020 / Revised: 17 July 2020 / Accepted: 18 July 2020 / Published: 19 July 2020
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. View Full-Text
Keywords: laryngeal cancer; contact endoscopy; narrow band imaging; automatic classification; feature extraction; machine learning laryngeal cancer; contact endoscopy; narrow band imaging; automatic classification; feature extraction; machine learning
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MDPI and ACS Style

Esmaeili, N.; Illanes, A.; Boese, A.; Davaris, N.; Arens, C.; Navab, N.; Friebe, M. Laryngeal Lesion Classification Based on Vascular Patterns in Contact Endoscopy and Narrow Band Imaging: Manual Versus Automatic Approach. Sensors 2020, 20, 4018. https://doi.org/10.3390/s20144018

AMA Style

Esmaeili N, Illanes A, Boese A, Davaris N, Arens C, Navab N, Friebe M. Laryngeal Lesion Classification Based on Vascular Patterns in Contact Endoscopy and Narrow Band Imaging: Manual Versus Automatic Approach. Sensors. 2020; 20(14):4018. https://doi.org/10.3390/s20144018

Chicago/Turabian Style

Esmaeili, Nazila; Illanes, Alfredo; Boese, Axel; Davaris, Nikolaos; Arens, Christoph; Navab, Nassir; Friebe, Michael. 2020. "Laryngeal Lesion Classification Based on Vascular Patterns in Contact Endoscopy and Narrow Band Imaging: Manual Versus Automatic Approach" Sensors 20, no. 14: 4018. https://doi.org/10.3390/s20144018

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