An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection

An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection

Brain volume changes at structural level appear to have utmost importance in depression biomarkers studies. However, these brain volumetric findings have very minimal utilization in depression detection studies at individual level. Thus, this paper presents an evaluation of volumetric features to id...

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Main Authors: Kuryati, Kipli, Abbas, Z. Kouzani
Format: Conference or Workshop Item
Language:English
Published: 2013
Subjects:
QM Human anatomy
Online Access:http://ir.unimas.my/15869/
http://ir.unimas.my/15869/
http://ir.unimas.my/15869/1/An%20algorithm%20for%20determination%20of%20rank%20and%20degree%20of%20contribution%20of%20sMRI%20volumetric%20features%20in%20depression%20detection%20%28abstrak%29.pdf
id unimas-15869
recordtype eprints
spelling unimas-158692017-05-03T00:44:04Z http://ir.unimas.my/15869/ An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection Kuryati, Kipli Abbas, Z. Kouzani QM Human anatomy Brain volume changes at structural level appear to have utmost importance in depression biomarkers studies. However, these brain volumetric findings have very minimal utilization in depression detection studies at individual level. Thus, this paper presents an evaluation of volumetric features to identify the relevant/optimal features for the detection of depression. An algorithm is presented for determination of rank and degree of contribution (DoC) of structural magnetic resonance imaging (sMRI) volumetric features. The algorithm is based on the frequencies of each feature contribution toward the desired accuracy limit. Forty-four volumetric features from various brain regions were adopted for evaluation. From DoC analysis, the DoC of each volumetric feature for depression detection is calculated and the features that dominate the contribution are determined 2013 Conference or Workshop Item PeerReviewed text en http://ir.unimas.my/15869/1/An%20algorithm%20for%20determination%20of%20rank%20and%20degree%20of%20contribution%20of%20sMRI%20volumetric%20features%20in%20depression%20detection%20%28abstrak%29.pdf Kuryati, Kipli and Abbas, Z. Kouzani (2013) An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 3 July 2013 through 7 July 2013, Osaka; Japan. http://ieeexplore.ieee.org/document/6609767/
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Sarawak
building UNIMAS Institutional Repository
collection Online Access
language English
topic QM Human anatomy
spellingShingle QM Human anatomy
Kuryati, Kipli
Abbas, Z. Kouzani
An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection
description Brain volume changes at structural level appear to have utmost importance in depression biomarkers studies. However, these brain volumetric findings have very minimal utilization in depression detection studies at individual level. Thus, this paper presents an evaluation of volumetric features to identify the relevant/optimal features for the detection of depression. An algorithm is presented for determination of rank and degree of contribution (DoC) of structural magnetic resonance imaging (sMRI) volumetric features. The algorithm is based on the frequencies of each feature contribution toward the desired accuracy limit. Forty-four volumetric features from various brain regions were adopted for evaluation. From DoC analysis, the DoC of each volumetric feature for depression detection is calculated and the features that dominate the contribution are determined
format Conference or Workshop Item
author Kuryati, Kipli
Abbas, Z. Kouzani
author_facet Kuryati, Kipli
Abbas, Z. Kouzani
author_sort Kuryati, Kipli
title An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection
title_short An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection
title_full An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection
title_fullStr An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection
title_full_unstemmed An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection
title_sort algorithm for determination of rank and degree of contribution of smri volumetric features in depression detection
publishDate 2013
url http://ir.unimas.my/15869/
http://ir.unimas.my/15869/
http://ir.unimas.my/15869/1/An%20algorithm%20for%20determination%20of%20rank%20and%20degree%20of%20contribution%20of%20sMRI%20volumetric%20features%20in%20depression%20detection%20%28abstrak%29.pdf
first_indexed 2018-09-06T16:25:19Z
last_indexed 2018-09-06T16:25:19Z
_version_ 1610876053546336256

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