HARDI based pattern classifiers for the identification of white matter pathologies.

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TitleHARDI based pattern classifiers for the identification of white matter pathologies.
Publication TypeJournal Article
Year of Publication2011
AuthorsBloy, L, Ingalhalikar, M, Eavani, H, Roberts, TPL, Schultz, RT, Verma, R
JournalMed Image Comput Comput Assist Interv
IssuePt 2
Date Published2011
KeywordsAlgorithms, Brain, Brain Mapping, Child, Child Development Disorders, Pervasive, Diffusion Tensor Imaging, Humans, Models, Statistical, Nerve Fibers, Myelinated, Pattern Recognition, Automated, Principal Component Analysis, Probability, Reproducibility of Results, Sensitivity and Specificity

The paper presents a method for creating abnormality classifiers from high angular resolution diffusion imaging (HARDI) data. We utilized the fiber orientation distribution (FOD) diffusion model to represent the local WM architecture of each subject. The FOD images are then spatially normalized to a common template using a non-linear registration technique. Regions of homogeneous white matter architecture (ROIs) are determined by applying a parcellation algorithm to the population average FOD image. Orientation invariant features of each ROI's mean FOD are determined and concatenated into a feature vector to represent each subject. Principal component analysis (PCA) was used for dimensionality reduction and a linear support vector machine (SVM) classifier is trained on the PCA coefficients. The classifier assigns each test subject a probabilistic score indicating the likelihood of belonging to the patient group. The method was validated using a 5 fold validation scheme on a population containing autism spectrum disorder (ASD) patients and typically developing (TD) controls. A clear distinction between ASD patients and controls was obtained with a 77% accuracy.

Alternate JournalMed Image Comput Comput Assist Interv
PubMed ID21995034
PubMed Central IDPMC3201760
Grant ListR01 MH092862 / MH / NIMH NIH HHS / United States
R01 MH092862-01 / MH / NIMH NIH HHS / United States
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