Geoffrey Young

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Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) allow in vivo investigation of molecular motion of tissue water at a microscopic level in cerebral gray matter (GM) and white matter (WM). DWI/DTI measure of water diffusion has been proven to be invaluable for the study of many neurodegenerative diseases (e.g., Alzheimer's disease and(More)
A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted(More)
We present a method for tissue classification based on diffusion-weighted imaging (DWI)/diffusion tensor imaging (DTI) data. Our motivation is that independent tissue segmentation based on DWI/DTI images provides complementary information to the tissue segmentation result using structural MRI data alone. The basis idea is to classify the brain into two(More)
PURPOSE To investigate whether regional brain volumes in adolescent idiopathic scoliosis (AIS) patients differ from matched control subjects as AIS subjects are reported to have poor performance on combined visual and proprioceptive testing and impaired postural balance in previous studies. MATERIALS AND METHODS Twenty AIS female patients with typical(More)
The human brain cortex is a highly convoluted sheet. Mapping of the cortical surface into a canonical coordinate space is an important tool for the study of the structure and function of the brain. Here, we present a technique based on least-square conformal mapping with spring energy for the mapping of the cortical surface. This method aims to reduce the(More)
This paper proposes a novel method to define deformation invariant attribute vector for each voxel in 3D image for the purpose of anatomic correspondence detection. This is the extension of the work for 2D deformation invariant attribute using geodesic intensity histogram (GIH) [1]. Our original contribution is to extend this 2D technique to 3D image, and(More)
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