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Neuroradiology
Published in: Neuroradiology 5/2013

01-05-2013 | Diagnostic Neuroradiology

Towards automated detection of depression from brain structural magnetic resonance images

Authors: Kuryati Kipli, Abbas Z. Kouzani, Lana J. Williams

Published in: Neuroradiology | Issue 5/2013

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Abstract

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.
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Metadata
Title
Towards automated detection of depression from brain structural magnetic resonance images
Authors
Kuryati Kipli
Abbas Z. Kouzani
Lana J. Williams
Publication date
01-05-2013
Publisher
Springer-Verlag
Published in
Neuroradiology / Issue 5/2013
Print ISSN: 0028-3940
Electronic ISSN: 1432-1920
DOI
https://doi.org/10.1007/s00234-013-1139-8

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