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.
展开▼
