Semi-automatic generation of transfer functions for direct volume rendering

1998 ◽  
Author(s):  
Gordon Kindlmann ◽  
James W. Durkin
Keyword(s):  
Volume Rendering ◽  
Transfer Functions ◽  
Automatic Generation ◽  
Direct Volume Rendering

scholarly journals State of the Art in Transfer Functions for Direct Volume Rendering

2016 ◽  
Vol 35 (3) ◽  
pp. 669-691 ◽  
Author(s):  
Patric Ljung ◽  
Jens Krüger ◽  
Eduard Groller ◽  
Markus Hadwiger ◽  
Charles D. Hansen ◽  
...  
Keyword(s):  
Volume Rendering ◽  
Transfer Functions ◽  
State Of The Art ◽  
Direct Volume Rendering

scholarly journals A Clustering-Based Automatic Transfer Function Design for Volume Visualization

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Tianjin Zhang ◽  
Zongrui Yi ◽  
Jinta Zheng ◽  
Dong C. Liu ◽  
Wai-Mai Pang ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Spatial Information ◽  
Transfer Functions ◽  
Clustering Algorithms ◽  
Volume Visualization ◽  
Automatic Generation ◽  
Volume Data ◽  
3D Volume ◽  
Ap Clustering ◽  
Different Tissues

The two-dimensional transfer functions (TFs) designed based on intensity-gradient magnitude (IGM) histogram are effective tools for the visualization and exploration of 3D volume data. However, traditional design methods usually depend on multiple times of trial-and-error. We propose a novel method for the automatic generation of transfer functions by performing the affinity propagation (AP) clustering algorithm on the IGM histogram. Compared with previous clustering algorithms that were employed in volume visualization, the AP clustering algorithm has much faster convergence speed and can achieve more accurate clustering results. In order to obtain meaningful clustering results, we introduce two similarity measurements: IGM similarity and spatial similarity. These two similarity measurements can effectively bring the voxels of the same tissue together and differentiate the voxels of different tissues so that the generated TFs can assign different optical properties to different tissues. Before performing the clustering algorithm on the IGM histogram, we propose to remove noisy voxels based on the spatial information of voxels. Our method does not require users to input the number of clusters, and the classification and visualization process is automatic and efficient. Experiments on various datasets demonstrate the effectiveness of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document