Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65627
Title: A clustering-based automatic transfer function design for volume visualization
Authors: Zhang, T
Yi, Z
Zheng, J
Liu, DC
Pang, WM
Wang, Q
Qin, J 
Issue Date: 2016
Publisher: Hindawi Publishing Corporation
Source: Mathematical problems in engineering, 2016, v. 2016, 4547138 How to cite?
Journal: Mathematical problems in engineering 
Abstract: 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.
URI: http://hdl.handle.net/10397/65627
ISSN: 1024-123X
EISSN: 1563-5147
DOI: 10.1155/2016/4547138
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