title: Towards automated detection of depression from brain structural magnetic resonance images.
abstract: BACKGROUND
  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.
  
  
  CONCLUSIONS
  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.
authors: School of Engineering, Deakin University, Waurn Ponds, Victoria 3216, Australia. kkipli@deakin.edu.au
authors: Kuryati
authors: K
authors: Kipli
authors: Abbas Z
authors: AZ
authors: Kouzani
authors: Lana J
authors: LJ
authors: Williams
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D003863:Depression / Q000175:diagnosis*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity