Arabidopsis roots segmentation based on morphological operations and CRFs

TL;DR

This paper introduces an unsupervised, cost-effective method for segmenting Arabidopsis thaliana roots in images using morphological operations and fully-connected CRFs, reducing manual effort and reliance on complex imaging.

Contribution

The paper presents a novel, simple, and open-source segmentation approach that works with standard scanner images, unlike previous methods requiring complex imaging modalities.

Findings

Effective segmentation with minimal user intervention

Applicable to images from conventional scanners

Open-source implementation available online

Abstract

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.