Automatic Classification of Plant Cells According to Tissue Type using Anatomical Features Obtained by the Distance Transform

Abstract

Image analysis was used to develop a faster and more objective method for the quantitative measurement of plant anatomy. The size, number and anatomical features of individual cells were measured automatically using methods based on image skeletonization and the distance transform. The variation in cell wall thickness around individual cells was measured by masking the distance transform of a segmented binary cell wall image with a skeleton of the same image to extract distance values along the boundary between neighbouring cells. The measurements were used to create a multi-dimensional feature space in which individual cells were classified automatically according to their tissue type. Cells were classified into seven types using discriminant analysis, and the performance of the classification rule was examined by cross-validation. The potential use of these methods as a research tool, and in plant breeding programmes is discussed.