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Authors: Muhammad Laiq Ur Rahman Shahid 1 ; Teodora Chitiboi 2 ; Tatyana Ivanovska 3 ; Vladimir Molchanov 1 ; Henry Völzke 3 ; Horst K. Hahn 2 and Lars Linsen 1

Affiliations: 1 Jacobs University, Germany ; 2 Jacobs University and Fraunhofer MEVIS, Germany ; 3 Ernst-Moritz-Arndt-Universität Greifswald, Germany

ISBN: 978-989-758-089-5

Keyword(s): Obstructive Sleep Apnea (OSA), Pharynx Segmentation, Magnetic Resonance Imaging (MRI).

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis ; Medical Image Applications ; Segmentation and Grouping

Abstract: Obstructive sleep apnea (OSA) is a public health problem. Volumetric analysis of the upper airways can help us to understand the pathogenesis of OSA. A reliable pharynx segmentation is the first step in identifying the anatomic risk factors for this sleeping disorder. As manual segmentation is a time-consuming and subjective process, a fully automatic segmentation of pharyngeal structures is required when investigating larger data bases such as in cohort studies. We develop a context-based automatic algorithm for segmenting pharynx from magnetic resonance images (MRI). It consists of a pipeline of steps including pre-processing (thresholding, connected component analysis) to extract coarse 3D objects, classification of the objects (involving object-based image analysis (OBIA), visual feature space analysis, and silhouette coefficient computation) to segregate pharynx from other structures automatically, and post-processing to refine the shape of the identified pharynx (including extr action of the oropharynx and propagating results from neighboring slices to slices that are difficult to delineate). Our technique is fast such that we can apply our algorithm to population-based epidemiological studies that provide a high amount of data. Our method needs no user interaction to extract the pharyngeal structure. The approach is quantitatively evaluated on ten datasets resulting in an average of approximately 90% detected volume fraction and a 90% Dice coefficient, which is in the range of the interobserver variation within manual segmentation results. (More)

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Paper citation in several formats:
Shahid, M.; Chitiboi, T.; Ivanovska, T.; Molchanov, V.; Völzke, H.; Hahn, H. and Linsen, L. (2015). Automatic Pharynx Segmentation from MRI Data for Obstructive Sleep Apnea Analysis.In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 599-608. DOI: 10.5220/0005315905990608

@conference{visapp15,
author={Muhammad Laiq Ur Rahman Shahid. and Teodora Chitiboi. and Tatyana Ivanovska. and Vladimir Molchanov. and Henry Völzke. and Horst K. Hahn. and Lars Linsen.},
title={Automatic Pharynx Segmentation from MRI Data for Obstructive Sleep Apnea Analysis},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={599-608},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005315905990608},
isbn={978-989-758-089-5},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Automatic Pharynx Segmentation from MRI Data for Obstructive Sleep Apnea Analysis
SN - 978-989-758-089-5
AU - Shahid, M.
AU - Chitiboi, T.
AU - Ivanovska, T.
AU - Molchanov, V.
AU - Völzke, H.
AU - Hahn, H.
AU - Linsen, L.
PY - 2015
SP - 599
EP - 608
DO - 10.5220/0005315905990608

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