Diploma and Master Theses (authored and supervised):
"Noise and Artifact Reduction in Interactive Volume Renderings of Electron-Microscopy Data-Sets";
Supervisor: E. Gröller, M. Hadwiger, J. Beyer;
Institut für Computergraphik und Algorithmen,
final examination: 2010-05-15.
Connectomics is an emerging area of neuroscience that is concerned with understanding the neural algorithms embeded in the neural circuits of the brain by tracking neurons and studying their connections. From all the available scanning technologies only electron microscopy (EM) can provide sufficient scanning resolutions in order to identify neural processes. EM data sets, however, suffer from bad signal-to-noise ratio and artifacts introduced to the data set during the sectioning and digital reconstruction process of the scanned specimen. In this thesis we present two different approaches that generally allow noise and artifact reduction on volumetric data sets and which can be used to increase the visual quality of direct volume renderings (DVRs) of EM data sets. The fist approach we developed was an interactive, on-the-fly filtering framework that allows a user to filter even very large volume data set with resizable 3D filter-kernels. For comparison, we implemented an average, a Gaussian, and a bilateral filter. The second approach we investigated is a semi-automatic one that allows a user to select regions within a data set. Similar regions are then retrieved by our algorithm using multiresolution histograms and the user can remove these regions from the rendering. By selecting and hiding regions containing noise or artifacts, the desired noise- and artifact-reduction can be achieved. We are going to show that both methods we investigated are suitable for removing noise and artifacts in EM data sets.
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.