Semiautomatic Transfer Function Initialization for Abdominal Visualization Using Self-Generating Hierarchical Radial Basis Function Networks
Issue No. 03 - May/June (2009 vol. 15)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2008.198
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.
Volume visualization, Transfer function design, medical image, Hierarchical radial basis function networks, multiscale analysis, Volume histogram stack
C. Güzeliş and M. A. Selver, "Semiautomatic Transfer Function Initialization for Abdominal Visualization Using Self-Generating Hierarchical Radial Basis Function Networks," in IEEE Transactions on Visualization & Computer Graphics, vol. 15, no. , pp. 395-409, 2008.