DocumentCode :
2569390
Title :
Using local binary pattern to classify dementia in MRI
Author :
Oppedal, K. ; Engan, K. ; Aarsland, D. ; Beyer, M. ; Tysnes, O.B. ; Eftestøl, T.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Univ. of Stavanger, Stavanger, Norway
fYear :
2012
fDate :
2-5 May 2012
Firstpage :
594
Lastpage :
597
Abstract :
White matter lesions (WML) are hyperintense signals in T2-weighted MRI of the brain. Volume and regional distribution of WML have been extensively studied in dementia, but not much attention has been given to texture analysis in these regions. We wanted to explore if it is possible to distinguish patients with dementia from healthy elderly in a classification framework testing different texture features in the WML regions, paying special attention to a feature called Local Binary Pattern (LBP). The results presented here, indicates that the LBP features used in our experiment are powerful features in a maximum likelihood classifier, when classifying demented from normal controls.
Keywords :
biomedical MRI; brain; diseases; geriatrics; image segmentation; image texture; medical disorders; medical image processing; neurophysiology; T2-weighted MRI; classification framework testing; dementia; hyperintense signals; local binary pattern; regional distribution; texture analysis; texture features; volume distribution; white matter lesions; Biomedical imaging; Dementia; Magnetic resonance imaging; Senior citizens; Standards; Vectors; LBP; MRI; classification; dementia; texture;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on
Conference_Location :
Barcelona
ISSN :
1945-7928
Print_ISBN :
978-1-4577-1857-1
Type :
conf
DOI :
10.1109/ISBI.2012.6235618
Filename :
6235618
Link To Document :
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