DTI based diagnostic prediction of a disease via pattern classification

Authors:
Madhura Ingalhalikar;Stathis Kanterakis;Ruben Gur;Timothy P. L. Roberts;Ragini Verma
Affiliations:
Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA;Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA;Brain Behavior Laboratory, University of Pennsylvania, Philadelphia, PA;Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA;Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Year:
2010

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Abstract

The paper presents a method of creating abnormality classifiers learned from Diffusion Tensor Imaging (DTI) data of a population of patients and controls. The score produced by the classifier can be used to aid in diagnosis as it quantifies the degree of pathology. Using anatomically meaningful features computed from the DTI data we train a non-linear support vector machine (SVM) pattern classifier. The method begins with high dimensional elastic registration of DT images followed by a feature extraction step that involves creating a feature by concatenating average anisotropy and diffusivity values in anatomically meaningful regions. Feature selection is performed via a mutual information based technique followed by sequential elimination of the features. A non-linear SVM classifier is then constructed by training on the selected features. The classifier assigns each test subject with a probabilistic abnormality score that indicates the extent of pathology. In this study, abnormality classifiers were created for two populations; one consisting of schizophrenia patients (SCZ) and the other with individuals with autism spectrum disorder (ASD). A clear distinction between the SCZ patients and controls was achieved with 90.62% accuracy while for individuals with ASD, 89.58% classification accuracy was obtained. The abnormality scores clearly separate the groups and the high classification accuracy indicates the prospect of using the scores as a diagnostic and prognostic marker.