Background: Obstructive sleep apnea (OSA) is a chronic sleeping disorder. The analysis of pharynx and its
surrounding tissues can play a vital role in understanding the pathogenesis of OSA. Classification of pharynx is a crucial
step in the analysis of OSA.
Objective: An automatic pharyngeal classification from magnetic resonance images (MRI) and the influence of different
features can help in analyzing the pharynx anatomy. However, the state-of-the-art classifiers do not provide any insight
regarding the features’ selection and their influence.
Methods: A visual analysis-based classifier is developed to classify the pharynx from MRI datasets. The classification
pipeline consists of different stages including pre-processing to select the initial candidates, extraction of categorical and
numerical features to form a multidimensional features space, and a supervised classifier trained by using visual analytics
and silhouette coefficient to classify the pharynx.
Results: The pharynx is classified automatically and gives an approximately 86% Jaccard coefficient by evaluating the
classifier on different MRI datasets. The expert’s knowledge can be utilized to select the optimal features and their
corresponding weights during the training phase of the classifier.
Conclusion: The proposed classifier is accurate and more efficient in terms of computational cost. It provides additional
insight to better understand the influence of different features individually and collectively. It finds its applications in
epidemiological studies where large datasets need to be analyzed.