TY - JOUR
T1 - Classification of Pharynx from MRI Using a Visual Analysis Tool to Study Obstructive Sleep Apnea
AU - Shahid, Muhammad Laiq Ur Rahman
AU - Mir, Junaid
AU - Shaukat, Furqan
AU - Saleem, Muhammad Khurram
AU - Tariq, Muhammad Atiq Ur Rehman
AU - Nouman, Ahmed
N1 - Publisher Copyright:
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Copyright:
This record is sourced from MEDLINE/PubMed, a database of the U.S. National Library of Medicine
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - classification
KW - Machine learning algorithm
KW - medical image analysis
KW - MRI
KW - multidimensional feature space
KW - OSA
KW - visual analysis
UR - http://www.scopus.com/inward/record.url?scp=85108304134&partnerID=8YFLogxK
U2 - 10.2174/1573405616666201118143935
DO - 10.2174/1573405616666201118143935
M3 - Article
C2 - 33213336
AN - SCOPUS:85108304134
VL - 17
SP - 613
EP - 622
JO - Current medical imaging
JF - Current medical imaging
SN - 1573-4056
IS - 5
ER -