Volume Rendering by Stochastic Neighbor Embedding-Based 2D Transfer Function Building

  • Walter Serna-Serna
  • Andres M. Álvarez-Meza
  • Álvaro-Ángel Orozco-Gutierrez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)


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.


Volume rendering Transfer functions Sthocastic neighbor embedding 



Under grants provided by the project (111074455778) “Desarrollo de un sistema de apoyo al diagnstico no invasivo de pacientes con epilepsia fármaco-resistente asociada a displasias corticales cerebrales: Método costo-efectivo basado en procesamiento de imágenes de resonancia magnética”, funded by Colciencias.


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Automatics Research GroupUniversidad Tecnológica de PereiraPereiraColombia

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