Neuroradiology

, Volume 55, Issue 5, pp 567–584

Towards automated detection of depression from brain structural magnetic resonance images

  • Kuryati Kipli
  • Abbas Z. Kouzani
  • Lana J. Williams
Diagnostic Neuroradiology

DOI: 10.1007/s00234-013-1139-8

Cite this article as:
Kipli, K., Kouzani, A.Z. & Williams, L.J. Neuroradiology (2013) 55: 567. doi:10.1007/s00234-013-1139-8

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.

Keywords

DepressionMagnetic resonance imagingPattern recognitionImage processingClassification

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kuryati Kipli
    • 1
    • 2
  • Abbas Z. Kouzani
    • 1
  • Lana J. Williams
    • 3
  1. 1.School of EngineeringDeakin UniversityWaurn PondsAustralia
  2. 2.Department of Electronic, Faculty of EngineeringUniversiti Malaysia SarawakKota SamarahanMalaysia
  3. 3.School of MedicineDeakin UniversityWaurn PondsAustralia