Arabidopsis roots segmentation based on morphological operations and CRFs
This work addresses the tedious and time-consuming manual segmentation task for plant scientists studying root architecture, though it is incremental as it builds on existing methods with simpler imaging.
The paper tackles the problem of segmenting Arabidopsis thaliana roots from images by proposing an unsupervised method using morphological operations and Conditional Random Fields, achieving results that can be applied to conventional scanner images with minimal user intervention.
Arabidopsis thaliana is a plant species widely utilized by scientists to estimate the impact of genetic differences in root morphological features. For this purpose, images of this plant after genetic modifications are taken to study differences in the root architecture. This task requires manual segmentations of radicular structures, although this is a particularly tedious and time-consuming labor. In this work, we present an unsupervised method for Arabidopsis thaliana root segmentation based on morphological operations and fully-connected Conditional Random Fields. Although other approaches have been proposed to this purpose, all of them are based on more complex and expensive imaging modalities. Our results prove that our method can be easily applied over images taken using conventional scanners, with a minor user intervention. A first data set, our results and a fully open source implementation are available online.