Authors:
Ilaria Gori
1
;
Alessia Giuliano
2
;
Piernicola Oliva
3
;
Michela Tosetti
4
;
Filippo Muratori
5
;
Sara Calderoni
6
and
Alessandra Retico
7
Affiliations:
1
National Institute of Nuclear Physics, Pisa, and University of Sassari and Sassari, Italy
;
2
National Institute of Nuclear Physics and and University of Pisa, Italy
;
3
National Institute of Nuclear Physics, Cagliari and and University of Sassari, Italy
;
4
IRCCS Fondazione Stella Maris, and Fondazione Imago7 and Pisa, Italy
;
5
IRCCS Stella Maris Foundation, Pisa and University of Pisa, Italy
;
6
IRCCS Stella Maris Foundation and Pisa, Italy
;
7
Istituto Nazionale di Fisica Nucleare, Italy
Keyword(s):
Image Processing, Feature Classification, One-class Support Vector Machine, Brain Magnetic Resonance Imaging (MRI), Autism Spectrum Disorders.
Related
Ontology
Subjects/Areas/Topics:
Bioimaging
;
Biomedical Engineering
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Feature Recognition and Extraction Methods
;
Health Engineering and Technology Applications
;
Image Processing Methods
;
Magnetic Resonance Imaging
;
Medical Imaging and Diagnosis
;
NeuroSensing and Diagnosis
;
Neurotechnology, Electronics and Informatics
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. Th
e 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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