Segmenting root systems in X-ray computed tomography images using level sets

TL;DR

This paper presents a novel level set-based method for segmenting plant root systems in large X-ray CT images, improving accuracy and efficiency over existing techniques.

Contribution

The paper introduces a specialized level set approach with an occupancy grid for effective large-volume root segmentation in X-ray CT images.

Findings

Method performs favorably compared to state-of-the-art techniques.

Efficient linear-time distance map computation in narrow-band regions.

Successfully applied to multiple plant species and media.

Abstract

The segmentation of plant roots from soil and other growing media in X-ray computed tomography images is needed to effectively study the root system architecture without excavation. However, segmentation is a challenging problem in this context because the root and non-root regions share similar features. In this paper, we describe a method based on level sets and specifically adapted for this segmentation problem. In particular, we deal with the issues of using a level sets approach on large image volumes for root segmentation, and track active regions of the front using an occupancy grid. This method allows for straightforward modifications to a narrow-band algorithm such that excessive forward and backward movements of the front can be avoided, distance map computations in a narrow band context can be done in linear time through modification of Meijster et al.'s distance transform algorithm, and regions of the image volume are iteratively used to estimate distributions for root versus non-root classes. Results are shown of three plant species of different maturity levels, grown in three different media. Our method compares favorably to a state-of-the-art method for root segmentation in X-ray CT image volumes.