Automatic image analysis

For other quantification, specialized graticules are available, including point counting, grids, concentric circles, and special scales. The latest methods of quantification involve automatic image analysis. 

England, B.M. Mikka, R.A. Bagnall, B.J. Petrographic Characterization of Coal Using Automatic Image Analysis, J. Microscopy. 1979, 116, 329-336. 

Rilschoff, J., Plate, K. H., Contractor, H., Kem, S., Zimmermann, R., and Thomas, C. 1990. Evaluation of nucleolus organizer regions (AgNORs) by automatic image analysis a contribution to the standardization. J. Pathol. 767 113-118. 

Plasier B, Lloyd DR, Paul GC, Thomas CR, Al-Rubeai M (1999), Automatic image analysis for quantification of apoptosis in animal cell culture by annexin-V affinity assay, J. Immunol. Methods 229 81-95. 

Groen et. al. [7] determined the optimum procedure for automatic focusing of a microscope. Kenny [8] examined the errors associated with detecting the edge of the particle image and outlined a technique, suitable for automatic image analysis for minimizing this error. 

Automatic image analysis is a six step process (Figure 3.4) (1) image formation, (2) image scanning, (3) feature detection, (4) feature analysis by count, shape, size or other selected parameter, (5) data processing and analyzing (6) data presentation. 

Buehler Omnimet II is a high resolution automatic image analysis system. 

Optomax V is a simple to use automatic image analysis system employing dual microprocessors combined with high speed measurement circuitry. The system uses up to four high performance TV cameras to provide direct viewing of macroscopic or microscopic images. It counts and measures image features such as area, perimeter, Feret diameters, horizontal and vertical intercepts and so on. 

Some TEMs have a closed circuit television system fitted so that the images can be fed directly to an automatic image analysis system. 

The optical microscope can be used to observe dispersed particles and floes, with particle sizing carried out using manual, semiautomatic, or automatic image analysis techniques. 

In this paper we describe the use of a scanning electron microscope (SEM) equipped with automatic-image-analysis (AIA) capability for the determination of coal mineralogies Because one of the main limitations of the SEM-AIA technique is its inability to distinguish the various iron-bearing minerals, Fe Mdssbauer spectroscopy is used to supplement data from SEM-AIA with respect to iron-bearing minerals The combination of these two techniques usually provides a detailed, quantitative characterization of the minerals in coal ... 

Jones, M. R, 1977. Automatic image analysis. In Zussman, J. (ed.) Physical Methods in Determinative Mineralogy. Academic Press, New York 167-199. 

Anwander, A. and others, "New Methods for Clinker Phase Recognition Using Automatic Image Analysis," Proceedings of the 20" International Conference on Cement Microscopy, International Cement Microscopy Association, Guadalajara, Jalisco, 1998, pp. 259-269. 

LC Sawyer. Determination of fiberglass lengths Sample preparation and automatic image analysis. Polymer Eng Sci 19 377-382, 1979. 

Particle size measurements are usually made from micrographs. The method is extremely tedious if it is done manually. Automatic image analysis of micrographs or an electronic display can reduce the amount of work considerably. Normally a large number of particles (a few hundred) need to be measured. As would be apparent, microscopy produces a particle size distribution based on the numbCT of particles within an appropriate size range. 

Whilst the qualitative analysis of filler dispersion in polymer composites poses its own difficulties, quantitative evalnation of mixing in these systems creates further challenges. Firstly, to establish the spatial location or size distribution of the additive, a statistically representative number of particles must be examined, preferably from various fields of view within the specimen. Providing there is sufficient contrast between the phases, as is discussed later, automatic image analysis techniques can be applied to rapidly assimilate and process data. Secondly, additive particles frequently have an irregular geometry and may also be exposed in a two-dimensional array at sections other than their mid-point, (i.e., only the tips of the particles may be on view). Thirdly, there is the question of how to define mixing and express this numerically. 

In synthetic fillers it is sometimes difficult to separate fundamental from aggregate shape but, where there is sufficient incentive, then ways will usually be found to overcome such difficulties. Such incentives arose in carbon blacks and more recently in precipitated silica, where shapes are very complex, but an understanding is critical to their high value usage in the tyre industry. Much work has been done, especially by Medalia and Heckman [16], and by Hess and co-workers [17], to develop automatic image analysis procedures. Using such procedures, all the aspects described previously have been investigated. This work has much to teach us about other filler particles. 

Packer, H. L., and C. R. Thomas (1990). Morphological measurements on filamentous micro-organisms by fully automatic image analysis, Biotechnol. Bioeng., 35, 870-881. 

Batra, S.W.T. (1988) Automatic image analysis for rapid identification of Africanized honey bees. In Africanized Honey Bees and Bee Mites (ed G.R. Needham), Ellis Horwood, Chichester, UK, pp. 260-263. Baylac, M., ViUemant, C. and Simbolotti, G. (2003) Combining geometric morphometries with pattern recognition for the investigation of species complexes. Biological Journal of the Linnean Society, 80 89-98. 

These optical and chemical staining procedures are often necessary to develop sufficient contrast in a specimen so that automatic image analysis can take quantitative measurements of composition and grain size. 

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