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Adaptation Approaches in Unsupervised Learning: A Survey of the State-of-the-Art and Future Directions Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Automatic Rating of Perivascular Spaces in Brain MRI Using Bag of Visual Words

Authors : Víctor González-Castro, María del C. Valdés Hernández, Paul A. Armitage, Joanna M. Wardlaw

Published in: Image Analysis and Recognition

Publisher: Springer International Publishing

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Abstract

Perivascular spaces (PVS), if enlarged and visible in magnetic resonance imaging (MRI), relate to poor cognition, depression in older age, Parkinson’s disease, inflammation, hypertension and cerebral small vessel disease. In this paper we present a fully automatic method to rate the burden of PVS in the basal ganglia (BG) region using structural brain MRI. We used a Support Vector Machine classifier and described the BG following the bag of visual words (BoW) model. The latter was evaluated using a) Scale Invariant Feature Transform (SIFT) descriptors of points extracted from a dense sampling and b) textons, as local descriptors. BoW using SIFT yielded a global accuracy of 82.34 %, whereas using textons it yielded 79.61 %.

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Footnotes
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Metadata
Title
Automatic Rating of Perivascular Spaces in Brain MRI Using Bag of Visual Words
Authors
Víctor González-Castro
María del C. Valdés Hernández
Paul A. Armitage
Joanna M. Wardlaw
Copyright Year
2016
Book
Image Analysis and Recognition

Print ISBN: 978-3-319-41500-0

Electronic ISBN: 978-3-319-41501-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-41501-7_72

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