KEYWORDS: Visualization, Image segmentation, Electron microscopy, 3D modeling, Image processing, Natural surfaces, 3D image processing, Data acquisition, Image visualization, 3D visualizations
Transmission Electron Microscopy allows the visualisation of cell organelles and intracellular structures at high magnification. But this technique is physically limited to ultrathin sections of about 20 to 100 nm because the electrons have to get through the biological structures to image them. In order to reconstruct the third dimension, we have previously developed a method based on laser topographical references to correct the physical deformations linked with the sectioning process and to allow the fine registration of the images1. The volume information is reconstructed but the intracellular organelles which are limited by membranes, are still embedded in a matrix of opaque cytoplasm containing dense ribosomes and fibrils. The rendering of the surface, volume and the original 3D appearance of each organelle requires individual segmentation. This operation is presently the only conceivable way to visualise the internal organisation of cells. We expose here some methods and algorithms for extraction of organellescontours and their subsequent preliminary visualisation. The algorithms allow the representation of the internal cell structure and pave the way toward virtual immersion.
KEYWORDS: 3D image processing, Image processing, Scanning electron microscopy, Scanning transmission electron microscopy, Electron beams, Image analysis, Image resolution, Magnetism, 3D modeling, Transmission electron microscopy
The digital processing of electron microscopic images from serial sections containing laser-induced topographical references allows a 3-D reconstruction at a depth resolution of 30 to 40 nm of entire cells by the use of image analysis methods, as already demonstrated for Transmission Electron Microscopy (TEM) coupled with a video camera. We decided to use a Scanning Transmission Electron Microscope (STEM) to get higher contrast and better resolution at medium magnification. The scanning of our specimens at video frequencies is an attractive and easy way to link a STEM with an image processing system but the hysteresis of the electronic spools responsible for the magnetic deviation of the scanning electron beam induces deformations of images which have to be modelized and corrected before registration. Computer algorithms developed for image analysis and treatment correct the artifacts caused by the use of STEM and by serial sectioning to automatically reconstruct the third dimension of the cells. They permit the normalization of the images through logarithmic processing of the original grey level infonnation. The automatic extraction of cell limits allows to link the image analysis and treatments with image synthesis methods by minimal human intervention. The surface representation and the registered images provide an ultrastructural data base from which quantitative 3-D morphological parameters, as well as otherwise impossible visualizations, can be computed. This 3-D image processing named C.A.V.U.M. for Computer Aided Volumic Ultra-Microscopy offers a new tool for the documentation and analysis of cell ultrastructure and for 3-D morphometric studies at EM magnifications. Further, a virtual observer can be computed in such a way as to simulate a visit of the reconstructed object.
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