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Image Analysis

SCI’s imaging work addresses fundamental questions in 2D and 3D image processing, including filtering, segmentation, surface reconstruction, and shape analysis. In low-level image processing, this effort has produce new nonparametric methods for modeling image statistics, which have resulted in better algorithms for denoising and reconstruction. Work with particle systems has led to new methods for visualizing and analyzing 3D surfaces. Our work in image processing also includes applications of advanced computing to 3D images, which has resulted in new parallel algorithms and real-time implementations on graphics processing units (GPUs). Application areas include medical image analysis, biological image processing, defense, environmental monitoring, and oil and gas.

 

Diffusion Tensor MRI Population Analysis

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Diffusion tensor magnetic resonance imaging (DT-MRI) is relatively new imaging technique which provides new insight into the structure of brain white matter by measuring the local diffusion of water in the brain.  In this project associated with the national alliance for medical image computing (NAMIC) images are combined from a population into a template atlas which reflects the average properties of the population.  White matter bundles are extracted from in the template atlas to serve as a coordinate system for measuring diffusion properties and how they differ between populations.  In a study of neurodevelopment in association with the CONTE center at University of North Carolina - Chapel Hill, an atlas was developed based on subjects at one and two years of age.  Statistical comparison of the diffusion properties between one and two year olds indicates significant changes that may reflect underlying changes in myelination and axon development.  The above image shows differences in the fractional anisotropy (FA), a measure of diffusion tensors which is thought to reflect axon development, from one to two years.  The red regions indicate the largest increase of the FA value.