Novel automated vessel pattern characterization of larynx contact endoscopic video images

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.

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

“…The CE-NBI dataset included 4 patients for this histopathology, presenting LVC and PVC vascular patterns. Due to this variation, the classifier’s learning process using the proposed features [ 17 , 18 ] can be complicated. SVM with RBF showed no misclassification per patient in this Category.…”

“…We used the algorithm presented in [ 17 , 18 ] to perform the automatic approach. The algorithm consists of a pre-processing step involving vessel enhancement and segmentation [ 20 ].…”

“…In this study, we made two main changes to the algorithm proposed in [ 17 , 18 ]. First, the Jerman filter [ 21 ] was used as pre-processing for the vessel enhancement step instead of the Frangi filter to overcome the problems related to the established enhancement function, not well adapted to natural variations of the vascular morphology.…”

“…In the manual approach, six experienced and less experienced otolaryngologists individually evaluated PVC and LVC in CE-NBI images of patients and classified them into benign and malignant groups. An updated version of the algorithm proposed in [ 17 , 18 ] with 24 features and four supervised classifiers has been used to classify CE-NBI images into benign and malignant groups. The results of the two approaches were compared in terms of classification sensitivity and specificity.…”

Evaluation of Vascular Patterns Using Contact Endoscopy and Narrow-Band Imaging (CE-NBI) for the Diagnosis of Vocal Fold Malignancy

“…Furthermore, as in other medical fields like gastroenterology, such endoscopic data can be used for automated evaluation approaches [28]. Training machine-learning algorithms to differentiate between benign and malignant lesions, as proposed by Esmaeili et al, could play an important role in computer-aided diagnoses in the future [29]. Automated vascular pattern recognition and assessments could be used alone or in combination with texture assessment strategies to optimize endoscopic diagnoses in the future [30,31].…”