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International Journal of Computer Assisted Radiology and Surgery

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01.07.2015 | Original Article

Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection

verfasst von: Kuryati Kipli, Abbas Z. Kouzani

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 7/2015

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Abstract

Purpose

Accurate detection of depression at an individual level using structural magnetic resonance imaging (sMRI) remains a challenge. Brain volumetric changes at a structural level appear to have importance in depression biomarkers studies. An automated algorithm is developed to select brain sMRI volumetric features for the detection of depression.

Methods

A feature selection (FS) algorithm called degree of contribution (DoC) is developed for selection of sMRI volumetric features. This algorithm uses an ensemble approach to determine the degree of contribution in detection of major depressive disorder. The DoC is the score of feature importance used for feature ranking. The algorithm involves four stages: feature ranking, subset generation, subset evaluation, and DoC analysis. The performance of DoC is evaluated on the Duke University Multi-site Imaging Research in the Analysis of Depression sMRI dataset. The dataset consists of 115 brain sMRI scans of 88 healthy controls and 27 depressed subjects. Forty-four sMRI volumetric features are used in the evaluation.

Results

The DoC score of forty-four features was determined as the accuracy threshold (Acc_Thresh) was varied. The DoC performance was compared with that of four existing FS algorithms. At all defined Acc_Threshs, DoC outperformed the four examined FS algorithms for the average classification score and the maximum classification score.

Conclusion

DoC has a good ability to generate reduced-size subsets of important features that could yield high classification accuracy. Based on the DoC score, the most discriminant volumetric features are those from the left-brain region.

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Titel: Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection
verfasst von: Kuryati Kipli
Abbas Z. Kouzani
Publikationsdatum: 01.07.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 7/2015
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-014-1130-9

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Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection

Abstract

Purpose

Methods

Results

Conclusion

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Abstract

Purpose

Methods

Results

Conclusion

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