Automatic MRI segmentation of para-pharyngeal fat pads using interactive visual feature space analysis for classification
2017 | journal article. A publication with affiliation to the University of Göttingen.
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Automatic MRI segmentation of para-pharyngeal fat pads using interactive visual feature space analysis for classification
Shahid, M. L. U. R.; Chitiboi, T.; Ivanovska, T.; Molchanov, V.; Völzke, H. & Linsen, L. (2017)
BMC Medical Imaging, 17(1). DOI: https://doi.org/10.1186/s12880-017-0179-7
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Details
- Authors
- Shahid, Muhammad Laiq Ur Rahman; Chitiboi, Teodora; Ivanovska, Tetyana; Molchanov, Vladimir; Völzke, Henry; Linsen, Lars
- Abstract
- Background: Obstructive sleep apnea (OSA) is a public health problem. Detailed analysis of the para-pharyngeal fat pads can help us to understand the pathogenesis of OSA and may mediate the intervention of this sleeping disorder. A reliable and automatic para-pharyngeal fat pads segmentation technique plays a vital role in investigating larger data bases to identify the anatomic risk factors for the OSA. Methods: Our research aims to develop a context-based automatic segmentation algorithm to delineate the fat pads from magnetic resonance images in a population-based study. Our segmentation pipeline involves texture analysis, connected component analysis, object-based image analysis, and supervised classification using an interactive visual analysis tool to segregate fat pads from other structures automatically. Results: We developed a fully automatic segmentation technique that does not need any user interaction to extract fat pads. Our algorithm is fast enough that we can apply it to population-based epidemiological studies that provide a large amount of data. We evaluated our approach qualitatively on thirty datasets and quantitatively against the ground truths of ten datasets resulting in an average of approximately 78% detected volume fraction and a 79% Dice coefficient, which is within the range of the inter-observer variation of manual segmentation results. Conclusion: The suggested method produces sufficiently accurate results and has potential to be applied for the study of large data to understand the pathogenesis of the OSA syndrome.
- Issue Date
- 2017
- Journal
- BMC Medical Imaging
- Organization
- Fakultät für Physik
- ISSN
- 1471-2342
- Language
- English