Anne-Laure Fouque, C. Fischer, V. Frouin, P. Ciuciu, E. Duchesnay
{"title":"Comparison of Features for Voxel-Based Analysis and Classification of Anatomical Neuroimaging Data","authors":"Anne-Laure Fouque, C. Fischer, V. Frouin, P. Ciuciu, E. Duchesnay","doi":"10.1109/PRNI.2013.55","DOIUrl":null,"url":null,"abstract":"The aim of this paper is to identify the relevant features that improve the identification of associations between structural (T1-weighted) MRI and a group (clinical status) of each subject. We compare 5 features derived from grey matter and deformation, on both simulated and experimental data. With voxel-based analysis we compare sensitivity of detection of anatomical differences, with pattern recognition approaches, we compare the accuracies of group prediction. The best results on our data are achieved by a multivariate representation of the deformation, the strain tensor, that can be associated with grey matter.","PeriodicalId":144007,"journal":{"name":"2013 International Workshop on Pattern Recognition in Neuroimaging","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Workshop on Pattern Recognition in Neuroimaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PRNI.2013.55","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The aim of this paper is to identify the relevant features that improve the identification of associations between structural (T1-weighted) MRI and a group (clinical status) of each subject. We compare 5 features derived from grey matter and deformation, on both simulated and experimental data. With voxel-based analysis we compare sensitivity of detection of anatomical differences, with pattern recognition approaches, we compare the accuracies of group prediction. The best results on our data are achieved by a multivariate representation of the deformation, the strain tensor, that can be associated with grey matter.