Erik L. G. Wernersson

Postprocessing method for reducing phase effects in reconstructed microcomputed-tomography data

With increased resolution in x-ray computed tomography, refraction adds increasingly to the attenuation signal. Though potentially beneficial, the artifacts caused by refraction often need to be removed from the image. In this paper, we propose a postprocessing method, based on deconvolution, that is able to remove these artifacts after conventional reconstruction. This method poses two advantages over existing projection-based (preprocessing) phase-retrieval or phase-removal algorithms. First, evaluation of the parameters can be done very quickly, improving the overall speed of the method. Second, postprocessing methods can be applied when projection data is not available, which occurs in several commercial systems with closed software or when projection data has been deleted. It is shown that the proposed method performs comparably to state-of-the-art methods in terms of image quality.

Segmentation of Wood Fibres in 3D CT Images Using Graph Cuts

To completely segment all individual wood fibres in volume images of fibrous materials presents a challenging problem but is important in understanding the micro mechanical properties of composite materials. This paper presents a filter that identifies and closes pores in wood fibre walls, simplifying the shape of the fibres. After this filter, a novel segmentation method based on graph cuts identifies individual fibres. The methods are validated on a realistic synthetic fibre data set and then applied on μ CT images of wood fibre composites.

Accurate Estimation of Gaussian and Mean Curvature in Volumetric Images

Curvature is a useful low level surface descriptor of wood fibres in {3D} micro-CT images of paper and composite materials. It may for instance be used to differentiate between the outside and the inside (lumen) of wood fibre. Since the image acquisition introduces noise, some kind of smoothing is required to obtain accurate estimates of curvature. However, in these materials, the fibres of interest are frequently both thin and densely packed. In this paper, we show how existing methods fail to accurately capture curvature information under these circumstances. Maintained resolution and smoothing of noise are two competing goals. In some situations, existing methods will even estimate the wrong signs of the principal curvatures. We also present a novel method, which is shown to have better performance in several experiments. This new method will generically produce better curvature estimates for thin objects and objects in close proximity.

Extracting fiber and network connectivity data using microtomography images of paper

We apply image analysis methods based on micro-computed tomography (μCT) to extract the parameters that characterize the structure and bonding parameters in the fiber network of paper. The scaling ...

Light scattering in fibrous media with different degrees of in-plane fiber alignment

Fiber orientation is an important structural property in paper and other fibrous materials. In this study we explore the relation between light scattering and in-plane fiber orientation in paper sheets. Light diffusion from a focused light source is simulated using a Monte Carlo technique where parameters describing the paper micro-structure were determined from 3D x-ray computed tomography images. Measurements and simulations on both spatially resolved reflectance and transmittance light scattering patterns show an elliptical shape where the main axis is aligned towards the fiber orientation. Good qualitative agreement was found at low intensities and the results indicate that fiber orientation in thin fiber-based materials can be determined using spatially resolved reflectance or transmittance.

Generating synthetic μCT images of wood fibre materials

X-ray Computerized Tomography at micrometer resolution (μCT) is an important tool for understanding the properties of wood fibre materials such as paper, carton and wood fibre composites. While many image analysis methods have been developed for μCT images in wood science, the evaluation of these methods if often not thorough enough because of the lack of a dataset with ground truth. This paper describes the generation of synthetic μCT volumes of wood fibre materials. Fibres with a high degree of morphological variations are modeled and densely packed into a volume of the material. Using a simulation of the μCT image acquisition process, realistic synthetic images are obtained. This simulation uses noise characterized from a set of μCT images. The synthetic images have a known ground truth, and can therefore be used when evaluating image analysis methods.