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.

Processing Magnetic resonance image features with one-class support vector machines investigation of the autism spectrum disorder heterogeneity / Gori, Ilaria; Giuliano, Alessia; Oliva, Piernicola; Tosetti, Michela; Muratori, Filippo; Calderoni, Sara; Retico, Alessandra. - (2016), pp. 111-117. ((Intervento presentato al convegno 3rd International Conference on Bioimaging, BIOIMAGING 2016 - Part of 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016 tenutosi a ita nel 2016.

Processing Magnetic resonance image features with one-class support vector machines investigation of the autism spectrum disorder heterogeneity

GORI, Ilaria;OLIVA, Piernicola;
2016

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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9789897581700
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11388/174286
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