Image Analysis on Wood Fiber Cross-Section Images
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Lignification of wood fibers has a significant impact on wood properties. To measure the distribution of lignin in compression wood fiber cross-section images, a crisp segmentation method had been developed. It segments the lumen, the normally lignified cell wall and the highly lignified cell wall of each fiber. In order to refine this given segmentation the following two fuzzy segmentation methods were evaluated in this thesis: Iterative Relative Multi Objects Fuzzy Connectedness and Weighted Distance Transform on Curved Space. The crisp segmentation is used for the multi-seed selection.
The crisp and the two fuzzy segmentations are then evaluated by comparing with the manual segmentation. It shows that Iterative Relative Multi Objects Fuzzy Connectedness has the best performance on segmenting the lumen, whereas Weighted Distance Transform on Curved Space outperforms the two other methods regarding the normally lignified cell wall and the highly lignified cell wall.
Place, publisher, year, edition, pages
IT, 11 028
IdentifiersURN: urn:nbn:se:uu:diva-156428OAI: oai:DiVA.org:uu-156428DiVA: diva2:431576
Master Programme in Computer Science
Luengo, CrisJansson, Anders