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Adaptive approximation of the boundary surface of a neuron in confocal microscopy volumetric images

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Abstract

In biomedical visualisation, the isosurface is usually used to represent (approximate) the boundary surface of the structure within biomedical volumetric images. However, in many confocal microscopy volumetric images of neurons, the grey values of the object and/or background are usually uneven. Therefore a fixed isosurface is not suitable for use in approximating the boundary surface of the neuron. A method is proposed to construct the adaptively approximating surface of the boundary surface of the neuron. In this method, the boundary surface of the neuron could be locally and adaptively approximated with different surface patches in different local regions. Consequently, the approximation accuracy has been considerably improved.

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References

  • Avila, R. S., Sobierajski, L. M., andKaufman, A. E. (1994): ‘Visualizing nerve cells’,IEEE Comput. Graph. Appl., Sept., pp. 11–13

  • Chan, F. H. Y., Lam, F. K., andZhu, H. (1998): ‘Adaptive thresholding by variational method’,IEEE Trans. Image Process.,17, pp. 468–473

    Google Scholar 

  • Cheng, P. C., Acharya, R., Lin, T. H., Samarabandu, J. K., Wang, G., Shinozaki, D. M., Berezney, R., Meng, C. L., Tarng, W. H., Liou, W. S., Tan, T. C., Summer, R. G., Kuang, H., andMusial, C. (1992). ‘3-D image analysis and visualization in light microscopy and X-ray Micro-Tomography’,in Kriete, A. (Ed.): ‘Visualization in biomedical microscopies: 3-D imaging and computer applications’, (VCH, New York, 1992), pp. 361–398

    Google Scholar 

  • Chow, C. K. andKaneko, T. (1972): ‘Automatic boundary detection of the left ventricle from cineangiograms’,Comput. Biomed. Res.,5, pp. 388–410

    Article  Google Scholar 

  • Elvins, T. T. (1992): ‘A survey of algorithms for volume visualization’,Comput. Graph.,26, pp. 194–199

    Google Scholar 

  • Haralick, R. M. (1984): ‘Digital step edges from zero crossing of second directional derivatives’,IEEE Trans Pattern Anal. Mach. Intell.,6, pp. 58–68

    Google Scholar 

  • Heng, P. A., Wang, L., Wong, T. T., Leung, K. S., andCheng, J. C. (2001): ‘Edge surfaces extraction from 3D images’,in Sonka, M., andHanson, K. M. (Eds): ‘Proc. medical imaging 2001: image processing’, vol. 4322 (SPIE, 2001), pp.407–416

  • Jung, G. S., andPark, R. H. (1988): ‘Automatic edge extraction using locally adaptive threshold’,Electron. Lett.,24, pp. 711–712

    Google Scholar 

  • Kriete, A. (1992): ‘Visualization in biomedical microscopies: 3-D imaging and computer applications’ (VCH, New York, 1992)

    Google Scholar 

  • Lorensen, W. E., andCline, H. E. (1987): ‘Marching, cubes: a high resolution 3D surface construction algorithm’,Comput. Graph., July, pp. 163–169

  • Marr, D., andHildreth, E. (1980): ‘Theory of edge detection’,Proc. R. Soc. Lond.,B207, pp. 187–217

    Google Scholar 

  • Martone, M. E., Gupta, A., Wong, M. Qian, X., Sosinsky, G., Ludaesher, B., andEllisman, M. H. (2002): ‘A cell centered database for electron tomographic data’,J. Struct. Biol.,138, pp. 145–155

    Article  Google Scholar 

  • Mueller, K., andCrawfis, R. (1998): ‘Eliminating popping artifacts in sheet buffer-based splitting’. IEEE Visualization'98, Chapel Hill, October 1998, pp. 239–245

  • Nakagawa, Y., andRosenfeld, A. (1979): ‘Some experiments on variable thresholding’,Pattern Recognit.,11, pp. 191–204

    Google Scholar 

  • Nielson, G. M., andHamann, B. (1991): ‘The asymptotic decider: resolving the ambiguity in Marching Cubes’. IEEE Proc. Visualization'91, pp. 83–91

  • Peter, V. H., andDavid, M. C. (1996): ‘Automatic gradient threshold determination for edge detection’,IEEE Trans. Image Process.,5, pp. 784–787

    Google Scholar 

  • Pudney, C., Robins, M., Robbins, B., andKovesi, P. (1996): ‘Surface detection in 3D confocal microscope image via local energy and ridge tracing’,J. Comput. Assist. Microsc.,8, pp. 5–20

    Google Scholar 

  • Rosenfeld, A., andKak, A. (1982): ‘Digital picture processing’, vol. 1 (Academic Press, 1982)

  • Sarti, A., De Solorzano, C. O., Lockett, S., andMalladi, R. (2000): ‘A geometric model for 3-D confocal image analysis’,IEEE Trans Biomed. Eng.,47, pp. 1600–1609

    Google Scholar 

  • Wallen, P., Carlsson, K., andMossberg, K. (1992): ‘Confocal laser scanning microscopy as a tool for studying the 3D morphology of nerve cell’ inKriete, A. (Ed.): ‘Visualization in biomedical microscopies: 3-D imaging and computer applications’ (VCH, New York, 1992), pp. 109–144

    Google Scholar 

  • Wang, L., Heng, P. A., Wong, T. T., andBai, J. (2002): ‘Multi-isovalues selection by clustering gray values of boundary surfaces’, inSonka, M., andFitzpatrick, J. M. (Eds): ‘Proc. medical imaging 2002: image processing’, vol 4684 pp. 1195–1203 (SPIE, 2002)

  • Wang, L., andBai, J. (2003): ‘Threshold selection by clustering gray levels of boundary’,Pattern Recognit. Lett.,24, pp. 1983–1999

    Google Scholar 

  • Yanorvitz, S. D., andBruckstein, A. M. (1989): ‘A new method for image segmentation’,Comput. Vis. Graph. Image Process. 46, pp. 82–95

    Google Scholar 

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Correspondence to J. Bai.

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Wang, L., Bai, J. & Ying, K. Adaptive approximation of the boundary surface of a neuron in confocal microscopy volumetric images. Med. Biol. Eng. Comput. 41, 601–607 (2003). https://doi.org/10.1007/BF02345324

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  • DOI: https://doi.org/10.1007/BF02345324

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