Towards automated detection of depression from brain structural magnetic resonance images

Kipli, Kuryati, Kouzani, Abbas Z. and Williams, Lana J. 2013, Towards automated detection of depression from brain structural magnetic resonance images, Neuroradiology, vol. 55, no. 5, pp. 567-584.

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Title Towards automated detection of depression from brain structural magnetic resonance images
Author(s) Kipli, Kuryati
Kouzani, Abbas Z.
Williams, Lana J.
Journal name Neuroradiology
Volume number 55
Issue number 5
Start page 567
End page 584
Total pages 18
Publisher Springer
Place of publication Berlin, Germany
Publication date 2013-01
ISSN 0028-3940
1432-1920
Keyword(s) depression
magnetic resonance imaging
pattern recognition
image processing
classification
Summary 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.
Language eng
Field of Research 090609 Signal Processing
Socio Economic Objective 920203 Diagnostic Methods
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2013, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30051077

Document type: Journal Article
Collections: School of Medicine
School of Engineering
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