Autism Spectrum Disorder characterization in children by capturing local‐regional brain changes

What is it about?

We proposed an image descriptor based on an adapted multiscale representation, the Curvelet transform. This descriptor is interpretable in terms of texture (local) and shape (regional) to characterize brain regions, and a Generalized Gaussian Distribution (GGD) to reduce feature dimensionality. In this approach, each MRI is first parcelled into 3D anatomical regions. Each resultant region is represented by a single 2D image where slices are placed next to each other. Each 2D image is characterized by mapping it to the Curvelet space and each of the different Curvelet sub-bands is described by the set of GGD parameters. To assess the discriminant power of the proposed descriptor, a classification model per brain region was built to differentiate ASD patients from control subjects.

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Why is it important?

To detect autism spectrum disorders in early life using non-invasive procedures