Improvement of 3D visualization of the brain using anisotropic diffusion smoothing of MR data

International Journal of Healthcare Technology and Management

Volumn 1, Issue 3-4, 1999, Pages 390-400

  Bajla, I ^a   Sramek M ^a  

^a INSTITUTE OF MEASUREMENT SCIENCE   (Slovakia)

Author keywords

3D data segmentation and visualization in medicine; Magnetic Resonance Imaging (MRI); Nonlinear image smoothing

Indexed keywords

EID: 0001617439     PISSN: 13682156     EISSN: 17415144     Source Type: Journal    
DOI: 10.1504/ijhtm.1999.004536     Document Type: Article

References (7)

1. Perona, P. and Malik, J. (1990) ‘Scale-space and edge detection using anisotropic diffusion’, IEEE Trans. On Pattern Analysis and Machine Intelligence, PAMI12, No. 7, pp.629–639.
2. Gerig, G. et. al. (1992) ‘Nonlinear anisotropic filtering of MRI data’, IEEE Trans. on Medical Imaging, MI 11, No. 2, June, pp.221–232.
3. Nordstroem, N. (1990) ‘Biased anisotropic diffusion – a unified regularization and diffusion approach to edge detection’, Image Vision and Computing, Vol. 8, No. 4, pp.318–327.
4. Shrinivasan, V. et. al. (1993) ‘Image reconstruction by a Hopfield neural network’, Image and Vision Computing, Vol. 11, No. 5, pp.278–281.
5. Shepp, L.A. and Logan, B.F. (1974) ‘The Fourier reconstruction of a head section’, IEEE Trans. on Nucl. Sci., Vol. NS–21, No. 3, pp.21–24.
6. Srámek, M. (1993) ‘Interactive segmentation of tissues for medical imaging’, In Václav Hlavác and Tomás Pajdla (eds.), Czech Pattern Recognition Workshop, Temesvár u Písku, Czechoslovak Pattern Recognition Society, Prague, pp.164–171.
7. Höhne, K.H. and Hanson, W.A. (1992) ‘Interactive 3D segmentation of MRI and CT volumes using morphological operations’, Journal of Computer Assisted Tomography, Vol. 16, No. 2, pp.285–294.