NA-MIC Internal Collaborations:StructuralImageAnalysis

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Structural Image Analysis

Image Segmentation

Tubular Surface Segmentation Framework We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. More... New: V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for identifying anatomical structures from medical imagery. September 2008.

Image Registration

	Cortical Correspondence using Particle System In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. More... New: Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.
	Population Analysis from Deformable Registration Analysis of populations of diffusion images typically requires time-consuming manual segmentation of structures of interest to obtain correspondance for statistics. This project uses non-rigid registration of DTI images to produce a common coordinate system for hypothesis testing of diffusion properties. More... New: Casey B. Goodlett, P. Thomas Fletcher, John H. Gilmore, Guido Gerig. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. NeuroImage 45 (1) Supp. 1, 2009. p. S133-S142.
	Multimodal Atlas In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called iCluster, is used to compute multiple atlases for a given population. More... NEW: Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

Morphometric Measures and Shape Analysis

Shape Analysis Framework using SPHARM-PDM The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. More... New: Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.