Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A signal processing approach to fair surface design
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
UFLOW: visualizing uncertainty in fluid flow
Proceedings of the 7th conference on Visualization '96
International Journal of Computer Vision
Simplifying surfaces with color and texture using quadric error metrics
Proceedings of the conference on Visualization '98
Harnessing Natural Textures for Multivariate Visualization
IEEE Computer Graphics and Applications
An image analysis algorithm for dendritic spines
Neural Computation
The asymptotic decider: resolving the ambiguity in marching cubes
VIS '91 Proceedings of the 2nd conference on Visualization '91
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Top Scientific Visualization Research Problems
IEEE Computer Graphics and Applications
Defining and computing curve-skeletons with medial geodesic function
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Visualizing Large-Scale Uncertainty in Astrophysical Data
IEEE Transactions on Visualization and Computer Graphics
Uncertainty Visualization in Medical Volume Rendering Using Probabilistic Animation
IEEE Transactions on Visualization and Computer Graphics
Visualization of Uncertainty and Reasoning
SG '07 Proceedings of the 8th international symposium on Smart Graphics
Rapid automated three-dimensional tracing of neurons from confocal image stacks
IEEE Transactions on Information Technology in Biomedicine
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Neuronal dendrites and their spines affect the connectivity of neural networks, and play a significant role in many neurological conditions. Neuronal function is observed to be closely correlated with the appearance, disappearance and morphology of the spines. Automatic 3-D reconstruction of neurons from light microscopy images, followed by the identification, classification and visualization of dendritic spines is therefore essential for studying neuronal physiology and biophysical properties. In this paper, we present a method to reconstruct dendrites using a surface representation of the dendrite. The 1-D skeleton of the dendritic surface is then extracted by a medial geodesic function that is robust and topologically correct. This is followed by a Bayesian identification and classification of the spines. The dendrite and spines are visualized in a manner that displays the spines' types and the inherent uncertainty in identification and classification. We also describe a user study conducted to validate the accuracy of the classification and the efficacy of the visualization.