Classification of pharynx from MRI using a visual analysis tool to study obstructive sleep apnea

Shahid, Muhammad Laiq Ur Rahman and Mir, Junaid and Shaukat, Furqan and Saleem, Muhammad Khurram and Tariq, Muhammad Atiq Ur Rehman and Nouman, Ahmed (2021) Classification of pharynx from MRI using a visual analysis tool to study obstructive sleep apnea. Current Medical Imaging, 17 (5). pp. 613-622. ISSN 1573-4056 (Print) 1875-6603 (Online)

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BACKGROUND: Obstructive sleep apnea (OSA) is a chronic sleeping disorder. The analysis of the pharynx and its surrounding tissues can play a vital role in understanding the pathogenesis of OSA. Classification of the pharynx is a crucial step in the analysis of OSA. 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.
Item Type: Article
Uncontrolled Keywords: classification; Machine learning algorithm; medical image analysis; MRI; multidimensional feature space; OSA; visual analysis
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Mechatronics
Faculty of Engineering and Natural Sciences
Depositing User: Ahmed Nouman
Date Deposited: 30 Aug 2022 18:32
Last Modified: 30 Aug 2022 18:32

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