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

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• 关键词：Brain MRI ; Perivascular spaces ; Bag of visual words ; SIFT ; SVM
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9730
• 期：1
• 页码：642-649
• 全文大小：784 KB
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  12.Valdés Hernández, M.D.C., Armitage, P.A., Thrippleton, M.J., Chappell, F., Sandeman, E., Muñoz Maniega, S., Shuler, K., Wardlaw, J.M.: Rationale, design and methodology of the image analysis protocol forstudies of patients with cerebral small vessel disease and mild stroke. Brain Behav. 5(12), e00415 (2015)CrossRef
  13.Valdés Hernández, M.D.C., Piper, R.J., Wang, X., Deary, I.J., Wardlaw, J.M.: Towards the automatic computational assessment of enlargedperivascular spaces on brain magnetic resonance images: a systematic review. J. Magn. Reson. Imaging 38(4), 774–785 (2013)CrossRef
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• 作者单位：Víctor González-Castro (15)
  María del C. Valdés Hernández (15)
  Paul A. Armitage (16)
  Joanna M. Wardlaw (15)
  
  15. Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
  16. Department of Cardiovascular Sciences, University of Sheffield, Sheffield, UK
  
• 丛书名：Image Analysis and Recognition
• ISBN：978-3-319-41501-7
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9730

文摘

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 %.