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UBC Theses and Dissertations

Automated fibre segmentation in micro CT images of paper Sharma, Yash

Abstract

Characterizing the structure of paper products made from Northern Bleached Softwood Kraft (NBSK) pulp after Low Consistency refining is a vital step to understanding the strengthening mechanisms of NBSK fibres as well as the effect of refining on the fibre morphology. X-ray micro tomographic (µCT) imaging coupled with advanced image analysis enables the characterization of the internal structure of paper at a very high resolution in 3D. In this work, a novel algorithm has been developed to isolate individual papermaking fibres in µCT images of paper handsheets as a first step to characterize the paper structure. The three step fibre segmentation algorithm segments the papermaking fibres by (i) tracking the hollow inside the fibres via a modified connected component methodology, (ii) extracting the fibre walls using a distance transform and (iii) labeling the fibres through collapsed sections by a final refinement step. Further, post processing algorithms have been developed to calculate the length, coarseness and relative bonded area of the fibres thus segmented. The algorithms have been validated by segmenting hollow aluminum tubes in test geometry similar in complexity to the paper structure. The capabilities and limitations of the algorithms have been evaluated by segmenting 2484 papermaking fibres within a 1mm x 1mm handsheet sample manufactured from NBSK pulp. The Fibre Segmentation algorithm is the first ever reported method for the automated and robust segmentation of the tortuous 3D morphology of papermaking fibres within 3D images of paper handsheets. The segmented structure thus obtained provides the capabilities to calculate several important properties of paper. The fibre segmentation algorithm is a unique and powerful tool to analyze the paper structure and provide novel insights into the papermaking process.

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Attribution-NonCommercial-NoDerivs 2.5 Canada

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