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Two different approaches based on Fourier and fractal analyses, are applied for particle shape characterisation. The advantages and limitations of both approaches as well as their fields of application are discussed. Examples of the application of these approaches for particles of various types are given.

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Abstract. 2014 A method for shape reconstruction and extraction from objects that have a certain regularity but are observed in a scanning electron microscopy image with some degree of overlap is presented. The proposed algorithm first calculates the curvature at each contour point of the object in the digitized binary image in order to detect the vertexes. Reconstruction of the shape of overlapping objects is then based on geometrical considerations using the information from the vertex co-ordinates. The procedure is independent of the size and orientation of the objects. The method is applied to the shape reconstruction of partially overlapping tabular silver halide microcrystals.

X-ray radiographic images of paintings often show little or no contrast. In order to increase the contrast in radiographic images we measured the X-ray spectrum of a low power X-ray tube, after passing through the painting, with a high energy-resolution SDD detector. To obtain images, the detector is collimated with a 400 μm diameter pinhole and the painting was moved through the beam in the x and y-direction using a dwell time of a few seconds per pixel. The data obtained consists of a data cube of, typically, 200 × 200 pixels and a 512-channel X-ray spectrum for each pixel, spanning the energy range from 0 to 40 keV. Having the absorbance spectrum available for each pixel, we are able, a posteriori, to produce images by edge subtraction for any given element. In this way high contrast, element-specific, images can be obtained. Because of the high energy-resolution a much simpler edge subtraction algorithm can be applied. We also used principal-component imaging to obtain, in a more automated way, images with high contrast. Some of these images can easily be attributed to specific elements. It turns out that preprocessing of the spectral data is crucial for the success of the multivariate image processing.