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Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection

level using structural magnetic resonance imaging (sMRI) remains a challenge. Brain volumetric changes at a structural level appear to have importance in depression biomarkers studies. An automated algorithm is developed to select brain sMRI volumetric features for the detection of depression

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Bibliographic Details
Main Authors: Kuryati, Kipli, Abbas, Z. Kouzani
Format: Article
Language:English
Published: International Society for Computer Aided Surgery (ISCAS) 2015
Subjects:
T Technology (General)
Online Access:http://ir.unimas.my/id/eprint/11960/1/No%208%20%28abstrak%29.pdf
http://ir.unimas.my/id/eprint/11960/
http://www.cars-int.org/cars_journal/journal_of_cars.html
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id my.unimas.ir.11960
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spelling my.unimas.ir.119602023-04-04T01:38:59Z http://ir.unimas.my/id/eprint/11960/ Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection Kuryati, Kipli Abbas, Z. Kouzani T Technology (General) level using structural magnetic resonance imaging (sMRI) remains a challenge. Brain volumetric changes at a structural level appear to have importance in depression biomarkers studies. An automated algorithm is developed to select brain sMRI volumetric features for the detection of depression International Society for Computer Aided Surgery (ISCAS) 2015-11-25 Article PeerReviewed text en http://ir.unimas.my/id/eprint/11960/1/No%208%20%28abstrak%29.pdf Kuryati, Kipli and Abbas, Z. Kouzani (2015) Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection. International Journal of Computer Assisted Radiology and Surgery, 10 (7). pp. 1003-1016. ISSN 1861-6410 http://www.cars-int.org/cars_journal/journal_of_cars.html DOI 10.1007/s11548-014-1130-9
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Kuryati, Kipli
Abbas, Z. Kouzani
Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection
description level using structural magnetic resonance imaging (sMRI) remains a challenge. Brain volumetric changes at a structural level appear to have importance in depression biomarkers studies. An automated algorithm is developed to select brain sMRI volumetric features for the detection of depression
format Article
author Kuryati, Kipli
Abbas, Z. Kouzani
author_facet Kuryati, Kipli
Abbas, Z. Kouzani
author_sort Kuryati, Kipli
title Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection
title_short Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection
title_full Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection
title_fullStr Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection
title_full_unstemmed Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection
title_sort degree of contribution (doc) feature selection algorithm for structural brain mri volumetric features in depression detection
publisher International Society for Computer Aided Surgery (ISCAS)
publishDate 2015
url http://ir.unimas.my/id/eprint/11960/1/No%208%20%28abstrak%29.pdf
http://ir.unimas.my/id/eprint/11960/
http://www.cars-int.org/cars_journal/journal_of_cars.html
_version_ 1762396640858079232
score 13.211869

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