Hon Pong Ho

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We present a novel level set representation and front propagation scheme for active contours where the analysis/evolution domain is sampled by unstructured point cloud. These sampling points are adaptively distributed according to both local data and level set geometry, hence allow extremely convenient enhancement/reduction of local front precision by(More)
While geometric deformable models have brought tremendous impacts on shape representation and analysis in medical image analysis, some of the remaining problems include the handling of boundary leakage and the lack of global understanding of boundaries. We present a modification to the geodesic active contour framework such that influence from local(More)
In this paper, we describe methods for constructing shape priors using orientation information to model white matter tracts from magnetic resonance diffusion tensor images (DTI). Shape Normalization is needed for the construction of a shape prior using statistical methods. Moving beyond shape normalization using boundary-only or orientation-only(More)
Conventional level set based image segmentations are performed upon certain underlying grid/mesh structures for explicit spatial discretization of the problem and evolution domains. Such computational grids, however, lead to typically expensive and difficult grid refinement/remeshing problems whenever tradeoffs between time and precision are deemed(More)
A fast method is proposed for DTI neural tract extraction and visualization. Existing efficient tract extraction approaches are based on localizing fibers from a pre-computed whole brain tractography. However, the tracking parameters of precomputed fibers cannot be easily adjusted quickly. Moreover, high noise in the estimated orientation around crossing(More)
Fiber tracking in diffusion tensor magnetic resonance images (DTIs) reveals 3-D structural connectivity of the brain conveniently and thus is a viable tool for investigating neural differences. Unfortunately, local noise, image artifacts and numerical tracking errors during integration-based techniques are cumulative. Prematurely terminated fibers and(More)
Existing methods for fiber tracking, interactive bundling and editing from Diffusion Magnetic Resonance Images (DMRI) reconstruct white matter fascicles using groups of virtual pathways. Classical numerical fibers suffer from image noise and cumulative tracking errors. 3D visualization of bundles of fibers reveals structural connectivity of the brain;(More)
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