Stereology parameter

Figure 2. An example of typical computer print out showing (a) the histogram of pixel frequency versus intensity with values of maximum and minimum intensity (b) adjusted raw data image (c) binary image and (d) calculated stereological parameters.

Histograms of distribution of stereological parameters in the investigated powders, whose morphology is shown in Fig. 7, are presented in Fig. 10-12 (for hydroxyapatite) and in Fig. 8, 13-15 (for zirconium ceramics with addition of 8%wt. Y2O3) and Fig. 9, 16-18 (for zirconium ceramics with addition of 20%wt. Y2O3). 

Two approaches can be used to model crystallization kinetics of triglycerides and fat. If the microscopic parameters can be determined, the use of microscopic models is the most appropriate, because it applies directly the theory of nucleation and growth. For example, in the case of spherulitic crystallization, kinetic parameters can be determined experimentally. Solidification can then be modeled in a detailed way with a numerical or stochastic model for the nucleation and growth of crystals. The latter kind of microscopic model is very interesting because it also gives the stereological parameters of the microstructure. Probabilistic or numerical models are easier to use, but they provide only the evolution of the latent heat or the evolution of the solid fraction in the sample. 

Charleston et al. (1996) studied the effects of long-term subclinical exposure to methylmercury on the number of neurons, oligodendrocytes, astrocytes, microglia, endothelial cells and pericytes within the thalamus from the left side of the brain of the monkey Macaca fascicularis. These parameters were determined by use of the Optical Volume Fractionator stereological method. The accumulated burden of... 

Previous work (3, 6) has suggested that the spacing between the rubber domains is the critical parameter which correlates the DBTT with rubber content and particle-size. Work to date, however, appears to have calculated this interparticle spacing from the measured particle-size distribution and the known volume fraction of rubber added to the blend, rather than from a direct stereological analysis of electron micrographs. The former method assumes that there is no occlusion of the nylon continuous phase within the rubber particles during mixing. 

The mathematical task of reconstructing the material s three-dimensional structure parameters from measured data obtained on a section of the material is called stereology. Different principles are used for areal, linear, and point analyses. The problem of scale is solved with a fractal approach, making the length of a curve a function of the size of the measuring device (22). 

The parameters that are commonly measured include particle numbers, diameters, areas, perimeters and ferets. A flexible system can be programmed to measure anything, such as arc lengths in diffraction patterns. In the example described above, an image of circular structures of varying diameters, image analysis methods are used to measure a statistical number of these structures. Stereological formulae provide the size distribution of the spheres. 

Fig. 4 presents the morphology of the powders used for preparation of graphite-steel composites. The values of statistical parameters of the particles of steel and graphite powders are presented in Fig. 5 and Fig. 6 in the form of histograms. Table 3 contains statistical parameters of stereological values of the used powders. 

Table 3. Statistical parameters of stereological values for the powders used in the study.

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