Automatic image processing morphometric method for the analysis of tracheid double wall thickness tested on juvenile Picea omorika trees exposed to static bending

Abstract

Measurements of various anatomical characteristics of wood cells are of great importance in research of wood structure, either for the evaluation of environmental influences or for estimation of wood quality. We present and test an automatic image processing morphometric method for the analysis of tracheid double wall thickness. A new algorithm of image analysis was developed. It uses morphological processing of structural elements with the different orientations from distance maps to analyze tracheid double wall thickness distribution separately for radial walls, tangential walls, and cell corners. For testing the performance of the method, we used confocal laser scanning microscopy images of stem cross-sections of juvenile Picea omorika trees exposed to long-term static bending. As a response to mechanical stress, conifers form compression wood (CW), which occurs in a range of gradations from near normal wood (NW) to severe CW. However, visual detection of compression wood severity, more precisely the determination of mild CW, is difficult. One of the anatomic features that characterize CW is increased wall thickness. After testing proposed automatic image processing morphometric method for the analysis of tracheid double wall thickness separately for radial walls, tangential walls and cell corners, combined with statistical analysis, we could suggest it as a tool for estimation of compression wood severity, or for estimation and gradation of changes in tracheid cell wall thickness as a response to environmental influences during growth and developmental process.