Nonlinear filtering and fast ray tracing of 3-D image data: Improving visualization in medical imaging

IEEE Engineering in Medicine and Biology Magazine

Volumn 17, Issue 2, 1998, Pages 73-80

  Bajla, Ivan ^a   Srámek, Milos ^a  

^a INSTITUTE OF CHEMISTRY   (Slovakia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BRAIN; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; NONLINEAR SYSTEMS; SIGNAL FILTERING AND PREDICTION; SIGNAL TO NOISE RATIO;

FAST RAY TRACING; NONLINEAR FILTERING;

MEDICAL IMAGING;

ARTICLE; DATA ANALYSIS; DIAGNOSTIC IMAGING; NONLINEAR SYSTEM; NUCLEAR MAGNETIC RESONANCE; THREE DIMENSIONAL IMAGING;

ALGORITHMS; BRAIN; HUMANS; MAGNETIC RESONANCE IMAGING; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED;

EID: 2642687665     PISSN: 07395175     EISSN: None     Source Type: Journal    
DOI: 10.1109/51.664034     Document Type: Article

References (34)

1. McCormick BH, DeFanti TA, Brown MD: Special issue on visualization in scientific computing. Comp Graph, 21(6), 1987.
2. Seebode C, Schubert R, Pommert A, et al.: An interactive 3D-atlas of acetabular fractures. In: Lemke HU, Rhodes ML, Jaffe CC, Felix R, (Eds): Comp Assisted Radiol, Proc. CAR'93, pp. 716-721. Springer-Verlag, Berlin, 1993.
3. Höhne KH, et al: A 3D anatomical atlas based on a volume model. IEEE Comp Graph and Appl, 12(4), pp. 72-78, 1992.
4. Acharya R, Wasserman R, Stevens J, Hinojosa C: Biomedical imaging modalities: A tutorial. Computerized Med Imaging and Graphics, 19(1), pp. 3-25, 1995.
5. Kaufman A, (Ed): Volume Visualization. IEEE Comp Soc Press, Los Alamitos, CA, 1991.
6. Frahm J, et al: Rapid scan techniques. In: Stark DD, Bradley WG, (Eds): Magnetic Resonance Imaging, 2nd ed., pp. 165-204, St. Louis: CV Mosby, 1992.
7. Katz WT, et al: Segmentation of the brain from 3D magnetic resonance images of the head. Proc. of the SPIE, 1898, pp. 809-819, 1993.
8. Joliot M, Mazoyer BM: Three-dimensional segmentation and interpolation of magnetic resonance brain images. IEEE Trans. on MI, 12(2), pp. 269-277, 1993.
9. Clarke LP, et al: MRI segmentation: Methods and applications. Mag Res Imaging, 13(3), pp. 343-368, 1995.
10. Perona P, Malik J: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. on PAMI, 12(7), pp. 629-639, 1990.
11. ter Haar Romeny B (Ed): Geometry-driven diffusion in computer vision. Kluwer Academic Publishers, Dordrecht/Boston/London, 1994.
12. Gerig G, et al: Nonlinear anisotropic filtering of MRI data. IEEE Trans. MI 11(2), pp. 221-232, 1992.
13. Witkovský V, Bajla I: Statistical criteria for 3D image solid segmentation within linear regression model. Machine Graphics and Vision, 2(4), pp. 339-355, 1993.
14. Bajla I, Marusiak M, Srámek M: Anisotropic Filtering of MRI Data Based upon Image Gradient Histogram. In: Chetverikov D, Kropatsch WD, (Eds): Comp Analysis of Images and Patterns, pp. 90-97, Springer-Verlag, Budapest, 1993.
15. Bajla I, Srámek M: Improvement of 3D visualization of the brain using anisotropic diffusion smoothing of MR data. In: Greenes RA, et al., (Eds): Proceedings of MEDINFO'95, 8th World Congress on Medical Informatics, pp. 683-686, Medical Information Association, Vancouver, 1995.
16. Bajla I, Srámek M: A modified approach and novel measure for evaluation of the image smoothing algorithm performance. J of Elect Eng, 47(3-4), pp. 93-101, 1996.
17. Bajla I, Srámek M: Improvement of 3D visualization of the brain using anisotropic diffusion smoothing of MR data. Int J of Technology Management, Special Edition in Health Informatics, 1997. In press.
18. Bajla I, Holländer I: Geometry-driven diffusion smoothing of the MR-brain images using a novel variable conductance. In Proc. of 18th Annual lnt Conf of the IEEE Engineering in Medicine and Biology Society, CD-ROM, No. 21, 1997.
19. Srámek M: Cubic macro-regions for fast voxel traversal. Machine Graphics and Vision, 3(1/2), pp. 171-179, 1994.
20. Srámek M: Fast surface rendering from raster data by voxel traversal using chessboard distance. In: Bergeron RD, Kaufman AE, (Eds): Visualization'94, pp. 188-195, IEEE Comp Soc Press, Washington, D.C., 1994.
21. Srámek M: Fast ray-tracing of rectilinear volume data. In: Göbel M, David J, Slavik P, van Wijk JJ, (Eds): Virtual Environments and Scientific Visualization'96, pp. 201-210. Springer-Verlag, New York, 1996.
22. Srámek M: Visualisation of Volumetric Data by Ray Tracing. PhD thesis, Technical University Vienna, ftp://umhp.savba.sk/pub/papers, 1996.
23. Bigün J, Granlund H: Optimal orientation detection of linear symmetry. In: Proc. of the First International Conference on Computer Vision, pp. 433-438, London, 1987.
24. Shrinivasan V, et al: Image reconstruction by a hopfield neural network. Image and Vision Computing, 11(5), pp. 278-281, 1993.
25. Yang GZ, et al: Structure adaptive anisotropic filtering. Image and Vision Compiling, 14(2), pp. 135-145, 1996.
26. Yagel R, Shi Z: Accelerating volume animation by space-leaping. In: Visualization '93, pp. 62-84, IEEE Comp Soc Press, San Jose, CA, 1993.
27. Levoy M: Display of surfaces from volume data. IEEE Comp Graph and Appl, 8(3), pp. 29-37, 1988.
28. Drebin RA, Carpenter L, Hanrahan P: Volume rendering. Comp Graph, 22(4), pp. 65-74, 1988.
29. Porter T, Duff T: Compositing digital images. Comp Graph. 18, pp. 253-259, 1984.
30. Borgefors G: Distance transformations in digital images. Comp Vis, Graph, and Im Proc, 34(3), pp. 344-371, 1986.
31. Holländer I, Sramek M: An Interactive Tool for Manipulation and Presentation of 3D Tomographic Data. In: Lemke HU, Inamura K, Jaffee CC, Felix R, (Eds): CAR'93 Computer Assisted Radiology, pp. 278-383, Springer-Verlag, Berlin, 1993.
32. Cleary JC, Wyvill G: Analysis of an algorithm for fast ray tracing using uniform space subdivision. The Visual Computer, 4(2), pp. 65-83, 1988.
33. Spackman J, Willis P: The SMART navigation of a ray through an oct-tree. Computers Graphics, 15(2), pp. 185-194, 1991.
34. Levoy M: Efficient ray tracing of volume data. ACM Trans on Comp Graph, 9(3), pp. 245-261, 1990.