Brain Image Classification Based on Automated Morphometry and Penalised Linear Discriminant Analysis with Resampling

• Autorzy: Eva Janousova , Daniel Schwarz , Giovanni Montana , Tomas Kasparek
• Języki publikacji: EN

Abstrakty

EN

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.(original abstract)

Słowa kluczowe

EN

Algorithms; Medicine

PL

Algorytmy; Medycyna

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2015
• Tom: 5
• Strony: 263--268

Twórcy

autor

Eva Janousova

• Masaryk University, Institute of Biostatistics and Analyses, Brno

autor

Daniel Schwarz

• Masaryk University, Institute of Biostatistics and Analyses, Brno

autor

Giovanni Montana

• Imperial College London

autor

Tomas Kasparek

• Masaryk University, Department of Psychiatry, Brno

Bibliografia

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Identyfikatory

DOI

10.15439/2015F147