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Semiautomatic Transfer Function Initialization for Abdominal Visualization Using Self-Generating Hierarchical Radial Basis Function Networks
May/June 2009 (vol. 15 no. 3)
pp. 395-409
ASCII Text
x 
 
M. Alper Selver, Cüneyt Güzeliş, "Semiautomatic Transfer Function Initialization for Abdominal Visualization Using Self-Generating Hierarchical Radial Basis Function Networks," IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 3, pp. 395-409, May/June, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TVCG.2008.198,
author = {M. Alper Selver and Cüneyt Güzeliş},
title = {Semiautomatic Transfer Function Initialization for Abdominal Visualization Using Self-Generating Hierarchical Radial Basis Function Networks},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {15},
number = {3},
issn = {1077-2626},
year = {2009},
pages = {395-409},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2008.198},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - Semiautomatic Transfer Function Initialization for Abdominal Visualization Using Self-Generating Hierarchical Radial Basis Function Networks
IS - 3
SN - 1077-2626
SP395
EP409
EPD - 395-409
A1 - M. Alper Selver,
A1 - Cüneyt Güzeliş,
PY - 2009
KW - Volume visualization
KW - Transfer function design
KW - medical image
KW - Hierarchical radial basis function networks
KW - multiscale analysis
KW - Volume histogram stack
VL - 15
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
M. Alper Selver, Dokuz Eylül University, Izmir
Cüneyt Güzeliş, Dokuz Eylül University, Izmir
Being a tool that assigns optical parameters used in interactive visualization, Transfer Functions (TF) have important effects on the quality of volume rendered medical images. Unfortunately, finding accurate TFs is a tedious and time consuming task because of the trade off between using extensive search spaces and fulfilling the physician's expectations with interactive data exploration tools and interfaces. By addressing this problem, we introduce a semi-automatic method for initial generation of TFs. The proposed method uses a Self Generating Hierarchical Radial Basis Function Network to determine the lobes of a Volume Histogram Stack (VHS) which is introduced as a new domain by aligning the histograms of slices of a image series. The new self generating hierarchical design strategy allows the recognition of suppressed lobes corresponding to suppressed tissues and representation of the overlapping regions which are parts of the lobes but can not be represented by the Gaussian bases in VHS. Moreover, approximation with a minimum set of basis functions provides the possibility of selecting and adjusting suitable units to optimize the TF. Applications on different CT/MR data sets show enhanced rendering quality and reduced optimization time in abdominal studies.

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Index Terms:
Volume visualization, Transfer function design, medical image, Hierarchical radial basis function networks, multiscale analysis, Volume histogram stack
Citation:
M. Alper Selver, Cüneyt Güzeliş, "Semiautomatic Transfer Function Initialization for Abdominal Visualization Using Self-Generating Hierarchical Radial Basis Function Networks," IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 3, pp. 395-409, May-June 2009, doi:10.1109/TVCG.2008.198
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