Image Processing Laboratory

Image Processing Laboratory
- Research Scope -

Back

Digital processing of medical (especially CT and MR) images

Image preparation and segmentation

3D image smoothing and visualization

Computer modelling of MR-imaging based on diffusion weighted measuremnt

Research objectives include:

Increasing signal-to-noise-ratio in multivariate MR image data via the anisotropic diffusion smoothing and image segmentation based on geometry-driven diffusion preprocessing of input MRI data

Automated segmentation of 2D and 3D MR images using interactive (X--Windows), functional, and statistical methods

Development of quantitative evaluation procedures (design of figures of merit) of the performance of image processing algorithms

Subvoxel precision surface detection

Fast voxel traversal algorithms

Analysis of the contribution of nonstandard measurement on polar or spherical(3D) raster in MR microscopy using computer models

Other activities:

Lecturing of the graduate and postgraduate courses on image processing at the Faculty of Electrical Engineering, Slovak Technical University, Bratislava

Supervising of students in graduate (MSc thesis) and postgraduate (PhD thesis) studies

International cooperation:

Institute of Information Processing, Austrian Academy of Sciences, Vienna, Austria

Basic equipment:

HP Apollo 9000/720 (RISC workstation),

PCs

Publications in CC journals during the period 1989-1994:
1. Bajla I., Holländer I. (1998): Nonlinear filtering of MR tomograms by Geometry-driven diffusion. Machine Vision and Applications 10, 243-255.
2. Bajla I., Šrámek M. (1998): Nonlinear filtering and fast ray tracing of 3D image data. IEEE Engineering in Medicine and Biology Magazine 17, 73-80.
3. Holländer I., Bajla I. (1998): Adaptive smoothing of MR brain images by 3D Geometry-driven diffusion. Computer Methods and Programs in Biomedicine 55, 157-176.
4. Bajla I., Šrámek M. (1998): Improvement of 3D visualization of the brain using anisotropic diffusion smoothing of MR data. International Journal of Technology Management. Special edition in Health Care Telematics. (v tlači).
5. Matej S., Lewitt R.M. (1996): Practical considerations for 3-D image reconstruction using spherically symmetric volume elements. IEEE Transactions on Medical Imaging 15, 68-78.
6. Matej S., Furuie S.S., Herman G.T. (1996): The relevance of statistically significant differences between reconstruction algorithms. IEEE Transactions on Image Processing 5, 554-556.
7. Matej S., Herman G.T., Vardi A. (1998): Binary tomography on the hexagonal grid using Gibbs priors. Journal of Imaging Systems and Technology 9, 126-131.
8. Matej S., Karp J.S., Lewitt R.M., Becher A.J. (1998): Performance of the Fourier rebinning algorithm for PET with large acceptance angles. Phys. Med. Biol. 43, 787-795.
9. Matej S., Herman G.T., Vardi A. (1998): Binary tomography on the hexagonal grid using Gibbs priors. Journal of Imaging Systems and Technology 9, 126-131.
10. Matej S., Karp J.S., Lewitt R.M., Becher A.J. (1998): Performance of the Fourier rebinning algorithm for PET with large acceptance angles. Phys. Med. Biol. 43, 787-795.