Refine
Document Type
- ZIB-Report (2)
Language
- English (2)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Keywords
- DVR (2) (remove)
Institute
Radiologists from all application areas are trained to read slice-based visualizations of 3D medical image data. Despite the numerous
examples of sophisticated three-dimensional renderings, especially all variants of direct volume rendering, such methods are
often considered not very useful by radiologists who prefer slice-based visualization. Just recently there have been attempts to bridge this
gap between 2D and 3D renderings. These attempts include specialized techniques for volume picking that result in repositioning slices.
In this paper, we present a new volume picking technique that, in contrast to previous work, does not require pre-segmented data or metadata. The positions picked by our method are solely based on the data itself, the transfer function and,
most importantly, on the way the volumetric rendering is perceived by viewers. To demonstrate the usefulness of the proposed method
we apply it for automatically
repositioning slices in an abdominal MRI
scan, a data set from a flow simulation and a number of other volumetric scalar fields. Furthermore we discuss how the method can be implemented in combination with various different volumetric rendering techniques.
In this paper we describe VisiTrace, a novel technique to
draw 3D lines in 3D volume rendered images. It allows to
draw strokes in the 2D space of the screen to produce 3D
lines that run on top or in the center of structures actually
visible in the volume rendering. It can handle structures
that only shortly occlude the structure that has been visible at the starting point of the stroke and is able to ignore
such structures. For this purpose a shortest path algorithm
finding the optimal curve in a specially designed graph
data structure is employed. We demonstrate the usefulness of the technique by applying it to MRI data from
medicine and engineering, and show how the method can
be used to mark or analyze structures in the example data
sets, and to automatically obtain good views toward the
selected structures.