Multidimensional transfer functions (MDTF) allow studying a volumetric data in a space built from features of interest. Thus, a transfer function (TF) can be defined as a region in a feature space that assigns optical properties to each voxel supporting volume rendering. Since voxels belonging to different objects can share feature similarities, segmentation of individual volume structures is not a straightforward task. We present a TF building approach from a 2D low-dimensional space using dimensionality reduction (DR). Namely, we carried out a Stochastic Neighbor Embedding (SNE)-based DR from MDTF domains. The outcomes show how our proposal, termed SNETF, outperform state-of-the-art approaches that use DR techniques in TF domains. The experiments were performed in a synthetical volume and in a standard volumetric tomography. Our method achieved a higher separability among objects on the new 2D space preserving the original distances between voxel samples. Thus, it was possible to get 3D representation of an object of interest into a given volume, which is an important fact for the next step in automating the generation of TF for volume rendering.
CITATION STYLE
Serna-Serna, W., Álvarez-Meza, A. M., & Orozco-Gutierrez, Á. Á. (2018). Volume rendering by stochastic neighbor embedding-based 2D transfer function building. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10657 LNCS, pp. 618–626). Springer Verlag. https://doi.org/10.1007/978-3-319-75193-1_74
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