A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision, Graphics, and Image Processing
Display of Surfaces from Volume Data
IEEE Computer Graphics and Applications
Semi-automatic generation of transfer functions for direct volume rendering
VVS '98 Proceedings of the 1998 IEEE symposium on Volume visualization
Multidimensional Transfer Functions for Interactive Volume Rendering
IEEE Transactions on Visualization and Computer Graphics
On the Location Error of Curved Edges in Low-Pass Filtered 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Retrospective Correction of MR Intensity Inhomogeneity by Information Minimization
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
DS-RT '09 Proceedings of the 2009 13th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications
Journal of Electrical and Computer Engineering
Hi-index | 0.00 |
The quality of volume visualization depends strongly on the quality of the underlying data. In virtual colonoscopy, CT data should be acquired at a low radiation dose that results in a low signal-to-noise ratio. Alternatively, MRI data is acquired without ionizing radiation, but suffers from noise and bias (global signal fluctuations). Current volume visualization techniques often do not produce good results with noisy or biased data. This paper describes methods for volume visualization that deal with these imperfections. The techniques are based on specially adapted edge detectors using first and second order derivative filters. The filtering is integrated into the the visualization process. The first order derivative method results in good quality images but suffers from localization bias. The second order method has better surface localization, especially in highly curved areas. It guarantees minimal detail smoothing resulting in a better visualization of polyps.