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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Notes
- 1.
More details about this visual rating scale can be found at http://www.sbirc.ed.ac.uk/documents/epvs-rating-scale-user-guide.pdf.
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Acknowledgements
We would like to thank Dr. Stephen Makin (patient recuitment), study participants, radiographers and staff at the Brain Research Imaging Centre Edinburgh, a SINAPSE (Scottish Imaging Network A Platform for Scientific Excellence) collaboration centre, the Wellcome Trust for funding the primary study that provided the data (Ref. No. 088134/Z/09) and the Row Fogo Charitable Trust (Grants Nos. R35865 and R43412).
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González-Castro, V., Valdés Hernández, M.d.C., Armitage, P.A., Wardlaw, J.M. (2016). Automatic Rating of Perivascular Spaces in Brain MRI Using Bag of Visual Words. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_72
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