The Rotating Cube: Interactive Specification of Viewing for Volume Visualization

1994 ◽  
pp. 181-185
Author(s):  
Martin Frühauf ◽  
Kennet Karlsson
Keyword(s):  
Volume Visualization

Volume visualization

1994 ◽  
Author(s):  
Arie E. Kaufman ◽  
Roni Yagel ◽  
Karl H. Hoehne ◽  
Andreas Pommert
Keyword(s):  
Volume Visualization

2082 A pilot study on the use of volume visualization in image-based treatment planning for head and neck cancer

1995 ◽  
Vol 32 ◽  
pp. 302
Author(s):  
L. Scott Johnson ◽  
Charles A. Pelizzari ◽  
Robert Grzeszczuk ◽  
Martin Ryan ◽  
Daniel J. Haraf ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Pilot Study ◽  
Head And Neck ◽  
Treatment Planning ◽  
Neck Cancer ◽  
Volume Visualization

Automatic adjustment of bidimensional transfer functions for direct volume visualization of intracranial aneurysms

2004 ◽  
Author(s):  
Fernando Vega Higuera ◽  
Natascha Sauber ◽  
Bernd Tomandl ◽  
Christopher Nimsky ◽  
Guenther Greiner ◽  
...  
Keyword(s):  
Intracranial Aneurysms ◽  
Transfer Functions ◽  
Volume Visualization ◽  
Automatic Adjustment

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.

G2: the design and realization of an accelerator for volume visualization

1992 ◽  
Author(s):  
Louis T. S. Leung ◽  
Warren Synnott
Keyword(s):  
Volume Visualization
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