XML-Based Image Annotation: PAIS - a Personal Annotated Image Server

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Use of Remote Surface Based Tools for Visualizing Integrated Brain Imaging Data

We describe a surface-based approach to 3D visualization of integrated neuroimaging data. Our web-enabled software allows researchers to use these visualization tools over the Internet. We present examples of brain imaging studies where such remote surface-based visualization techniques have proven to be quite effective

Shape-Based Models for Interactive Segmentation of Medical Images

Accurate image segmentation is one of the key problems in computer vision. In domains such as radiation treatment planning, dosimetrists must manually trace the outlines of a few critical structures on large numbers of images. Considerable similarity can be seen in the shape of these regions, both between adjacent slices in a particular patient and across the spectrum of patients. Consequently we should be able to model this similarity and use it to assist in the process of segmentation. Previous work has demonstrated that a constraint-based 2D radial model can capture generic shape information for certain shape classes, and can reduce user interaction by a factor of three over purely manual segmentation. Additional simulation studies have shown that a probabilistic version of the model has the potential to further reduce user interaction. This paper describes an implementation of both models in a general-purpose imaging and graphics framework and compares the usefulness of the models on several shape classes

Brain Visualization in Java3D

BrainJ3D is a cross-platform Java/Java3D software toolkit for processing and visualizing brain imaging data, which 1) contains general purpose tools for reconstructing, mapping and visualizing integrated structural and functional images and 2) leverages Java's Remote Method Invocation to provide both a standalone and a client/server mode

Heuristic Refinement Method for the Derivation of Protein Solution Structures: Validation on Cytochrome B562

A method is described for determining the family of protein structures compatible with solution data obtained primarily from nuclear magnetic resonance (NMR) spectroscopy. Starting with all possible conformations, the method systematically excludes conformations until the remaining structures are only those compatible with the data. The apparent computational intractability of this approach is reduced by assembling the protein in pieces, by considering the protein at several levels of abstraction, by utilizing constraint satisfaction methods to consider only a few atoms at a time, and by utilizing artificial intelligence methods of heuristic control to decide which actions will exclude the most conformations. Example results are presented for simulated NMR data from the known crystal structure of cytochrome b562 (103 residues). For 10 sample backbones an average root-mean-square deviation from the crystal of 4.1 A was found for all alpha-carbon atoms and 2.8 A for helix alpha-carbons alone. The 10 backbones define the family of all structures compatible with the data and provide nearly correct starting structures for adjustment by any of the current structure determination methods