Feature-based characterisation, i.e. the characterisation of surface topography based on the isolation of relevant topographic formations (features) and their dimensional assessment, is a developing field of surface texture metrology. Feature-based approaches provide dimensional assessments of individual features (area, width, height, etc) as well as statistical properties of feature aggregations (e.g. mean, standard deviation, etc), which may be more intuitive or related to functionality. For powder bed fusion surfaces, a commonly investigated feature of interest is the particles or spatter present on the surface. In this work, we address segmentation, a necessary step of feature-based characterisation, where the measured surface topography is spatially partitioned into regions to isolate the targeted features from their surroundings. Three topography segmentation methods are investigated: morphological segmentation on edges, contour stability analysis and active contours. To perform the comparison, three powder bed fusion surfaces obtained at differing build orientations (0°, 30° and 90°) and measured using focus variation microscopy are subjected to the three segmentation approaches - optimised to isolate spatter and particles on the surface. The comparison of the segmentation methods focuses on performance in feature identification (i.e. the capability to correctly detect the presence of features) and performance in feature boundary determination (i.e. the capability to correctly trace the boundaries of each feature). Results show that no segmentation method is consistently superior for all test cases, but the comparison approach is useful to explore and optimise segmentation alternatives for feature-based characterisation scenarios.

X-ray computed tomography (XCT) is a non-destructive imaging technique that has recently gained interest as a tool to measure surface topography. Large acquisition times are a major shortcoming of XCT. One contributing factor to the acquisition time is that a measurement can require the acquisition of thousands of radiographic projections. This paper explores the combined effects of undersampling, i.e. taking fewer radiographic projections and sinogram interpolation, i.e. estimating the missing radiographic projections by interpolation. Different degrees of sinogram interpolation are investigated through the measurement of the surfaces of a metal, additively manufactured part. The quality of the measurement result is assessed via the analysis of the reconstructed volumes, through the computation of quantitative indicators of spatial resolution and noise, and via the analysis of surface topographies extracted from the reconstructed volumes. The quality of the surfaces is assessed through the use of statistical models designed to estimate repeatability errors in the reconstruction, and through the computation of surface texture parameters. Results obtained with no undersampling and no sinogram interpolation are taken as reference. It is shown that noise in the volumetric reconstruction increases with respect to the reference with larger degrees of undersampling, but the increment can be partly compensated by sinogram interpolation with the effects on spatial resolution more difficult to interpret. The computation of surface texture parameters results in similar values for all but one case, the largest undersampling. The topography of the reconstructions indicate that the repeatability error remains similar in all experimental conditions, excluding the case of largest undersampling. Overall, the results indicate that a reduction of the acquisition time of XCT topography measurement is feasible. However, the obtained surface topographies suffer, and large undersampling creates unrecoverable negative effects on the spatial resolution, the noise characteristics and the obtained topographies, even when sinogram interpolation is used.