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2016 | OriginalPaper | Buchkapitel

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

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

Erschienen in: Image Analysis and Recognition

Verlag: 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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Fußnoten
1
 
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Metadaten
Titel
Automatic Rating of Perivascular Spaces in Brain MRI Using Bag of Visual Words
verfasst von
Víctor González-Castro
María del C. Valdés Hernández
Paul A. Armitage
Joanna M. Wardlaw
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-41501-7_72