Towards automated detection of depression from brain structural magnetic resonance images

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

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Official URL: https://link.springer.com/article/10.1007/s00234-0...

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.

Item Type: Article
Uncontrolled Keywords: Classification; Depression; Image processing; Magnetic resonance imaging; Pattern recognition, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak
Subjects: Q Science > QM Human anatomy
Divisions: Academic Faculties, Institutes and Centres > Faculty of Engineering
Depositing User: Saman
Date Deposited: 22 May 2017 01:16
Last Modified: 22 May 2017 01:16
URI: http://ir.unimas.my/id/eprint/16348

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