Brain Image Classification Based on Automated Morphometry and Penalised Linear Discriminant Analysis with Resampling
| Authors | |
|---|---|
| Year of publication | 2015 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 2015 Federated Conference on Computer Science and Information Systems |
| MU Faculty or unit | |
| Citation | |
| Web | https://fedcsis.org/proceedings/2015/pliks/147.pdf |
| Doi | http://dx.doi.org/10.15439/2015F147 |
| Field | Informatics |
| Keywords | pattern recognition; computational neuroanatomy; classification; penalized linear discriminant analysis with resampling; deformation-based morphometry; magnetic resonance imaging; schizophrenia |
| Description | This paper presents a new data-driven classification pipeline for discriminating two groups of individuals based on the medical images of their brain. The algorithm combines deformation-based morphometry and penalised linear discriminant analysis with resampling. The method is based on sparse representation of the original brain images using deformation logarithms reflecting the differences in the brain in comparison to the normal template anatomy. The sparse data enables efficient data reduction and classification via the penalised linear discriminant analysis with resampling. The classification accuracy obtained in an experiment with magnetic resonance brain images of first episode schizophrenia patients and healthy controls is comparable to the related state-of-the-art studies. |