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27.07.2019 | Original Article | Ausgabe 10/2019 Open Access

International Journal of Computer Assisted Radiology and Surgery 10/2019

Novel automated vessel pattern characterization of larynx contact endoscopic video images

Zeitschrift:
International Journal of Computer Assisted Radiology and Surgery > Ausgabe 10/2019
Autoren:
Nazila Esmaeili, Alfredo Illanes, Axel Boese, Nikolaos Davaris, Christoph Arens, Michael Friebe
Wichtige Hinweise
This work was financially supported by the Federal Ministry of Education and Research (BMBF) in context of the ‘INKA’ project (Grand Number 03IPT7100X and by EFRE funding in context of the ego.-INKUBATOR program (ZS/2016/09//81061/IK 01/2015)).

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

Purpose

Contact endoscopy (CE) is a minimally invasive procedure providing real-time information about the cellular and vascular structure of the superficial layer of laryngeal mucosa. This method can be combined with optical enhancement methods such as narrow band imaging (NBI). However, these techniques have some problems like subjective interpretation of vascular patterns and difficulty in differentiation between benign and malignant lesions. We propose a novel automated approach for vessel pattern characterization of larynx CE + NBI images in order to solve these problems.

Methods

In this approach, five indicators were computed to characterize the level of vessel’s disorder based on evaluation of consistency of gradient and two-dimensional curvature analysis and then 24 features were extracted from these indicators. The method evaluated the ability of the extracted features to classify CE + NBI images based on the vascular pattern and based on the laryngeal lesions. Four datasets were generated from 32 patients involving 1485 images. The classification scenarios were implemented using four supervised classifiers.

Results

For classification of CE + NBI images based on the vascular pattern, polykernel support vector machine (SVM), SVM with radial basis function (RBF), k-nearest neighbor (kNN), and random forest (RF) show an accuracy of 97%, 96%, 96%, and 96%, respectively. For the classification based on the histopathology, Polykernel SVM showed an accuracy of 84%, 86% and 84%, RBF SVM showed an accuracy of 81%, 87% and 83%, kNN showed an accuracy of 89%, 87%, 91%, RF showed an accuracy of 90%, 88% and 91% for classification between benign histopathologies, between malignant histopathologies and between benign and malignant lesions, respectively.

Conclusion

These promising results show that the proposed method could solve the problem of subjectivity in interpretation of vascular patterns and also support the clinicians in the early detection of benign, pre-malignant and malignant lesions.

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