Research and projectsData analysis tools FEATbox

Feature extraction & classification toolbox

Author: Radomír Kůs
Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, Czech Republic

Kůs, R.: FEATbox (Feature Extraction & clAssification Toolbox), version 1.0 [on-line]. 2016. Available from:

FEATbox (Feature Extraction & clAssification Toolbox) is an outcome of attempts to compare feature extraction and selection methods for schizophrenia classification based on magnetic resonance images (MRI) of brains. Thus, the primary focus of the toolbox are various feature extraction techniques, extracting features from 3-D images given in NIfTI format. Namely, Mann-Whitney testing is implemented as a representative of univariate approaches with contrast to multivariate methods such as intersubject PCA (isPCA), the K-SVD algorithm, and pattern-based morphometry (PBM). The extracted features can be either examined more thoroughly or passed to a subsequent leave-one-out cross-validated (LOOCV) linear support vector machine (SVM) classification. Also, several classification measures are implemented in the toolbox for assessing and comparing classification performance of different classification schemes.

documentation (PDF file, 1.1 MB)
source code (ZIP archive, 1.4 MB)