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Segmentation of tomographic data by hierarchical watershed transform

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The aim of the proposed watershed based image segmentation technique is to split images into spatially homogeneous regions, which can be further processed by different image analysis tools. The advantage of such approach, in comparison to pixel oriented processing, is its lower sensitivity to superimposed noise due to averaging of regions properties over their area. The watershed segmentation technique is based on interpretation of an image as a topographic relief and on simulation of flow of water along steepest descent paths called downstreams. Thus, for each local minimum of the image, a drainage region is defined, which, if computed for a gradient image, represents an area with approximately constant properties. The segmentation technique is further extended for multi-scale image analysis by means of Gaussian smoothing. The aim of smoothing is to suppress image details that are smaller than standard deviation of the Gaussian. However, smoothing results not only in the desired increase of region size, but it also affects position of region boundaries, at least for larger standard deviations of the Gaussian filter. Therefore a new technique is proposed, based on region hierarchies, which enables to transfer region contours with precise position from the levels with low smoothing to levels with higher smoothing. Thus, segmentation of an image into large regions, but with exact contours, is obtained.
Rocznik
Tom
Strony
MI161--169
Opis fizyczny
Bibliogr. 7 poz., rys.
Twórcy
autor
  • Commision for Scientific Visualization (VISKOM), Austrian Academy of Sciences, Vienna, Austria
  • Commision for Scientific Visualization (VISKOM), Austrian Academy of Sciences, Vienna, Austria
Bibliografia
  • [1] M. E. Brummer, R. M. Mersereau, R.L.Eisner, and R.J.R.Lewine. Automatic detection of brain contours in {MRI} data sets. IEEE Transactions on Medical Imaging, 12(2):153–166, June 1993.
  • [2] J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6):679–698, November 1986.
  • [3] J. J. Koenderink. The structure of images. Biolg. Cybern., 50:364–370, 1988.
  • [4] T. Schiemann, U. Tiede, M. Bomans, and K. H. Höhne. Interactive 3D-segmentation. In R. A. Robb, editor, Visualization in Biomedical Computing 1992, Proc. SPIE 1808, pages 376--383. SPIE, Chapel Hill, NC, 1992.
  • [5] M. Šrámek. Interactive segmentation of tissues for medical imaging. In Václav Hlaváč and Tomáš Pajdla, editors, Czech Pattern Recognition Workshop '93, pages 164–171, Temešvár u Písku, Czech Republic, November 4th-6 th, 1993.
  • [6] L. Vincent and P. Soille. Watersheds in digital spaces: An efficient algorithm based on immersion simulation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(6):583–598, June 1991.
  • [7] X. Zeng, L. H. Staib, R. T. Schultz, and J. S. Duncan. Segmentation and measurement of the cortex from 3{D} {MR} using coupled surfaces propagation. IEEE Trans. Med. Imaging, 18(10), 1999.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0023-0023
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