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Research article2012Peer reviewed

Automatic measurement of compression wood cell attributes in fluorescence microscopy images

Selig, Bettina; Luengo, Cris; Bardage, Stig; Daniel, Geoffrey; Borgefors, Gunilla

Abstract

This paper presents a new automated method for analyzing compression wood fibers in fluorescence microscopy. Abnormal wood known as compression wood is present in almost every softwood tree harvested. Compression wood fibers show a different cell wall morphology and chemistry compared to normal wood fibers, and their mechanical and physical characteristics are considered detrimental for both construction wood and pulp and paper purposes. Currently there is the need for improved methodologies for characterization of lignin distribution in wood cell walls, such as from compression wood fibers, that will allow for a better understanding of fiber mechanical properties. Traditionally, analysis of fluorescence microscopy images of fiber cross-sections has been done manually, which is time consuming and subjective. Here, we present an automatic method, using digital image analysis, that detects and delineates softwood fibers in fluorescence microscopy images, dividing them into cell lumen, normal and highly lignified areas. It also quantifies the different areas, as well as measures cell wall thickness. The method is evaluated by comparing the automatic with a manual delineation. While the boundaries between the various fiber wall regions are detected using the automatic method with precision similar to inter and intra expert variability, the position of the boundary between lumen and the cell wall has a systematic shift that can be corrected. Our method allows for transverse structural characterization of compression wood fibers, which may allow for improved understanding of the micro-mechanical modeling of wood and pulp fibers.

Keywords

Fiber cross-sections; fluorescence microscopy; image processing; lignin; rays; snakes

Published in

Journal of Microscopy
2012, Volume: 246, number: 3, pages: 298-308

      SLU Authors

    • Selig, Bettina

      • Centre for Image Analysis, Swedish University of Agricultural Sciences
      • Luengo, Cris

        • Centre for Image Analysis, Swedish University of Agricultural Sciences
          • Daniel, Geoffrey

            • Department of Forest Products, Swedish University of Agricultural Sciences
            • Borgefors, Gunilla

              • Centre for Image Analysis, Swedish University of Agricultural Sciences

            UKÄ Subject classification

            Bioinformatics (Computational Biology)

            Publication identifier

            DOI: https://doi.org/10.1111/j.1365-2818.2012.03621.x

            Permanent link to this page (URI)

            https://res.slu.se/id/publ/42452