Insegt Fibre: a user-friendly software for individual fibre segmentation

Insegt Fibre is a software toolbox for volumetric fibre segmentation. The toolbox comes with scripts to detect the centres of individual fibres in 2D and 3D from tomograms acquired through X-ray imaging, and a graphical user interface to verify the accuracy of the resulting 3D tracks. In addition, there is a script to characterise fibre orientations in 3D and a script to match corresponding fibres across a 4D time-lapse sequence, which enables the characterisation of composite micro-structural changes of individual fibres. Insegt Fibre is based on a segmentation method that uses a dictionary of image patches which has been trained to model the patterns/features that are repeated in the image data at a certain scale defined by the patch size. Thus, the dictionary-learning segmentation algorithm has proven highly successful in modelling fibres that have regular cross-sections, commonly found in fibre reinforced composite materials. The algorithm is robust to noise and artefacts in the data and therefore excels in measuring fibre geometry from a variety of scan qualities, even when fibres are densely packed and the boundaries between them are unclear. Insegt Fibre is simple to use, it comes with a manual and it requires minimal input from the user. Besides presenting the recent user-friendly version of this robust method for measuring the 3D tracks of fibres from X-ray CT data, the paper gives an overview of the possibilities that the method gives with regards to characterisation of composite micro-structure and fibre behaviour under load. Thanks to the precision to which fibre geometry can be characterised with this method, it is now possible to follow how each individual fibre changes across data-sets acquired under progressive loading conditions. All in all, Insegt Fibre makes image-based characterisation of fibrous materials simpler, more accessible and applicable to a broader range of studies.