Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
RGVis: Region Growing Based Techniques for Volume Visualization
PG '03 Proceedings of the 11th Pacific Conference on Computer Graphics and Applications
Mean Shift Is a Bound Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Intelligent System Approach to Higher-Dimensional Classification of Volume Data
IEEE Transactions on Visualization and Computer Graphics
On Histograms and Isosurface Statistics
IEEE Transactions on Visualization and Computer Graphics
Mean Shift Parallel Tracking on GPU
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
The Occlusion Spectrum for Volume Classification and Visualization
IEEE Transactions on Visualization and Computer Graphics
Accelerated convergence using dynamic mean shift
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Spatialized transfer functions
EUROVIS'05 Proceedings of the Seventh Joint Eurographics / IEEE VGTC conference on Visualization
A cluster-space visual interface for arbitrary dimensional classification of volume data
VISSYM'04 Proceedings of the Sixth Joint Eurographics - IEEE TCVG conference on Visualization
EUROVIS'06 Proceedings of the Eighth Joint Eurographics / IEEE VGTC conference on Visualization
Automating Transfer Function Design with Valley Cell-Based Clustering of 2D Density Plots
Computer Graphics Forum
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In this paper we present a parallel dynamic mean shift algorithm based on path transmission for medical volume data segmentation. The algorithm first translates the volume data into a joint position-color feature space subdivided uniformly by bandwidths, and then clusters points in feature space in parallel by iteratively finding its peak point. Over iterations it improves the convergent rate by dynamically updating data points via path transmission and reduces the amount of data points by collapsing overlapping points into one point. The GPU implementation of the algorithm can segment 256×256×256 volume in 6 seconds using an NVIDIA GeForce 8800 GTX card for interactive processing, which is hundreds times faster than its CPU implementation. We also introduce an interactive interface to segment volume data based on this GPU implementation. This interface not only provides the user with the capability to specify segmentation resolution, but also allows the user to operate on the segmented tissues and create desired visualization results.