Three-dimensional electron microscopy of entire cells.
ABSTRACT The digital processing of serial electron-microscope sections containing laser-induced topographical references allows a three-dimensional (3-D) reconstruction of entire cells at a depth resolution of 40-60 nm by the use of novel image analysis methods. The images are directly processed by a video-camera placed under the electron microscope in TEM mode or by the electron counting device in STEM mode. The deformations associated with the cutting of embedded cells are back-calculated by new computer algorithms developed for image analysis and treatment. They correct the artefacts caused by serial sectioning and automatically reconstruct the third dimension of the cells. Used in such a way, our data provide definitive information on the 3-D architecture of cells. This computer-assisted 3-D analysis represents a new tool for the documentation and analysis of cell ultrastructure and for morphometric studies. Furthermore, it is now possible for the observer to view the contents of the reconstructed tissue volume in a variety of different ways using computer-aided display techniques.
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ABSTRACT: The physical (microtomy), optical (microscopy), and radiologic (tomography) sectioning of biological objects and their digitization lead to stacks of images. Due to the sectioning process and disturbances, movement of objects during imaging for example, adjacent images of the image stack are not optimally aligned to each other. Such mismatches have to be corrected automatically by suitable registration methods. Here, a whole brain of a Sprague Dawley rat was serially sectioned and stained followed by digitizing the 20 μm thin histologic sections. We describe how to prepare the images for subsequent automatic intensity based registration. Different registration schemes are presented and their results compared to each other from an anatomical and mathematical perspective. In the first part we concentrate on rigid and affine linear methods and deal only with linear mismatches of the images. Digitized images of stained histologic sections often ex- hibit inhomogenities of the gray level distribution coming from staining and/or sectioning variations. Therefore, a method is developed that is robust with respect to inhomogenities and artifacts. Furthermore we combined this approach by minimizing a suitable distance measure for shear and rotation mismatches of foreground ob- jects after applying the principal axes transform. As a consequence of our investigations, we must emphasize that the combination of a robust principal axes based registration in combination with optimizing translation, rota- tion and shearing errors gives rise to the best reconstruction results from the mathematical and anatomical view point. Because the sectioning process introduces nonlinear deformations to the relative thin histologic sections as well, an elastic registration has to be applied to correct these deformations. In the second part of the study a detailed description of the advances of an elastic registration after affine linear registration of the rat brain is given. We found quantitative evidence that affine linear registration is a suitable starting point for the alignment of histologic sections but elastic registration must be performed to improve significantly the registration result. A strategy is presented that enables to register elastically the affine linear preregistered rat brainInternational Journal of Computer Vision 01/2007; 73:5-39. · 3.62 Impact Factor
- EURASIP J. Adv. Sig. Proc. 01/2007; 2007.
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ABSTRACT: A new framework for image representation, processing, and analysis is introduced and exposed through practical applications. The proposed approach is called logarithmic adaptive neighborhood image processing (LANIP) since it is based on the logarithmic image processing (LIP) and on the general adaptive neighborhood image processing (GANIP) approaches, that allow several intensity and spatial properties of the human brightness perception to be mathematically modeled and operationalized, and computationally implemented. The LANIP approach is mathematically, computationally, and practically relevant and is particularly connected to several human visual laws and characteristics such as: intensity range inversion, saturation characteristic, Webers and Fechners laws, psychophysical contrast, spatial adaptivity, multiscale adaptivity, morphological symmetry property. The LANIP approach is finally exposed in several areas: image multiscale decomposition, image restoration, image segmentation, and image enhancement, through biomedical materials and visual imaging applications.EURASIP Journal on Advances in Signal Processing 01/2007; 2007(1):114-114. · 0.89 Impact Factor