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Towards automated detection of depression from brain structural magnetic resonance images

Introduction: Depression is a major issue worldwide and is seen as a significant health problem. Stigma and patient denial, clinical experience, time limitations, and reliability of psychometrics are barriers to the clinical diagnoses of depression. Thus, the establishment of an automated system tha...

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Main Authors: Kuryati, Kipli, Abbas, Z. Kouzani, Williams, Lana J.
Format: Article
Language:English
Published: Springer Verlag 2013
Subjects:
QM Human anatomy
Online Access:http://ir.unimas.my/id/eprint/16348/1/Kuryati%20Kipli.pdf
http://ir.unimas.my/id/eprint/16348/
https://link.springer.com/article/10.1007/s00234-013-1139-8
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spelling my.unimas.ir.163482023-08-11T06:59:17Z http://ir.unimas.my/id/eprint/16348/ Towards automated detection of depression from brain structural magnetic resonance images Kuryati, Kipli Abbas, Z. Kouzani Williams, Lana J. QM Human anatomy Introduction: Depression is a major issue worldwide and is seen as a significant health problem. Stigma and patient denial, clinical experience, time limitations, and reliability of psychometrics are barriers to the clinical diagnoses of depression. Thus, the establishment of an automated system that could detect such abnormalities would assist medical experts in their decision-making process. This paper reviews existing methods for the automated detection of depression from brain structural magnetic resonance images (sMRI). Methods: Relevant sources were identified from various databases and online sites using a combination of keywords and terms including depression, major depressive disorder, detection, classification, and MRI databases. Reference lists of chosen articles were further reviewed for associated publications. Results: The paper introduces a generic structure for representing and describing the methods developed for the detection of depression from sMRI of the brain. It consists of a number of components including acquisition and preprocessing, feature extraction, feature selection, and classification. Conclusion: Automated sMRI-based detection methods have the potential to provide an objective measure of depression, hence improving the confidence level in the diagnosis and prognosis of depression. Springer Verlag 2013-05 Article PeerReviewed text en http://ir.unimas.my/id/eprint/16348/1/Kuryati%20Kipli.pdf Kuryati, Kipli and Abbas, Z. Kouzani and Williams, Lana J. (2013) Towards automated detection of depression from brain structural magnetic resonance images. Neuroradiology, 55 (5). pp. 567-584. ISSN 0028-3940 https://link.springer.com/article/10.1007/s00234-013-1139-8 DOI: 10.1007/s00234-013-1139-8
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 QM Human anatomy
spellingShingle QM Human anatomy
Kuryati, Kipli
Abbas, Z. Kouzani
Williams, Lana J.
Towards automated detection of depression from brain structural magnetic resonance images
description Introduction: Depression is a major issue worldwide and is seen as a significant health problem. Stigma and patient denial, clinical experience, time limitations, and reliability of psychometrics are barriers to the clinical diagnoses of depression. Thus, the establishment of an automated system that could detect such abnormalities would assist medical experts in their decision-making process. This paper reviews existing methods for the automated detection of depression from brain structural magnetic resonance images (sMRI). Methods: Relevant sources were identified from various databases and online sites using a combination of keywords and terms including depression, major depressive disorder, detection, classification, and MRI databases. Reference lists of chosen articles were further reviewed for associated publications. Results: The paper introduces a generic structure for representing and describing the methods developed for the detection of depression from sMRI of the brain. It consists of a number of components including acquisition and preprocessing, feature extraction, feature selection, and classification. Conclusion: Automated sMRI-based detection methods have the potential to provide an objective measure of depression, hence improving the confidence level in the diagnosis and prognosis of depression.
format Article
author Kuryati, Kipli
Abbas, Z. Kouzani
Williams, Lana J.
author_facet Kuryati, Kipli
Abbas, Z. Kouzani
Williams, Lana J.
author_sort Kuryati, Kipli
title Towards automated detection of depression from brain structural magnetic resonance images
title_short Towards automated detection of depression from brain structural magnetic resonance images
title_full Towards automated detection of depression from brain structural magnetic resonance images
title_fullStr Towards automated detection of depression from brain structural magnetic resonance images
title_full_unstemmed Towards automated detection of depression from brain structural magnetic resonance images
title_sort towards automated detection of depression from brain structural magnetic resonance images
publisher Springer Verlag
publishDate 2013
url http://ir.unimas.my/id/eprint/16348/1/Kuryati%20Kipli.pdf
http://ir.unimas.my/id/eprint/16348/
https://link.springer.com/article/10.1007/s00234-013-1139-8
_version_ 1775627201681555456
score 13.211869

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