Processing Magnetic Resonance Image Features with One-class Support Vector Machines - Investigation of the Autism Spectrum Disorder Heterogeneity

Ilaria Gori, Alessia Giuliano, Piernicola Oliva, Michela Tosetti, Filippo Muratori, Sara Calderoni, Alessandra Retico

Abstract

Support Vector Machine (SVM) classifiers are widely used to analyse features extracted from brain MRI data to identify useful biomarkers of pathology in several disease conditions. They are trained to distinguish patients from healthy control subjects by making a binary classification of image features extracted by image processing algorithms. This task is particularly challenging when dealing with psychiatric disorders, as the reported neuroanatomical alterations are often very small and quite un-replicated within different studies. Subtle signs of pathology are difficult to catch especially in extremely heterogeneous conditions such as Autism Spectrum Disorders (ASD). We propose the use of the One-Class Classification (OCC) or Data Description method that, in contrast with two-class classification, is based on a description of one class of objects only. Then, new examples are tested for their similarity to the examples of this target class, end eventually considered as outliers. The application of the OCC to features extracted from brain MRI of children affected by ASD and control subjects demonstrated that a common pattern of features characterize the ASD population.

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Paper Citation


in Harvard Style

Gori I., Giuliano A., Oliva P., Tosetti M., Muratori F., Calderoni S. and Retico A. (2016). Processing Magnetic Resonance Image Features with One-class Support Vector Machines - Investigation of the Autism Spectrum Disorder Heterogeneity . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 111-117. DOI: 10.5220/0005776001110117


in Bibtex Style

@conference{bioimaging16,
author={Ilaria Gori and Alessia Giuliano and Piernicola Oliva and Michela Tosetti and Filippo Muratori and Sara Calderoni and Alessandra Retico},
title={Processing Magnetic Resonance Image Features with One-class Support Vector Machines - Investigation of the Autism Spectrum Disorder Heterogeneity},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2016)},
year={2016},
pages={111-117},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005776001110117},
isbn={978-989-758-170-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2016)
TI - Processing Magnetic Resonance Image Features with One-class Support Vector Machines - Investigation of the Autism Spectrum Disorder Heterogeneity
SN - 978-989-758-170-0
AU - Gori I.
AU - Giuliano A.
AU - Oliva P.
AU - Tosetti M.
AU - Muratori F.
AU - Calderoni S.
AU - Retico A.
PY - 2016
SP - 111
EP - 117
DO - 10.5220/0005776001110117