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

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.Comment: 11 page

Automatic Segmentation of Trees in Dynamic Outdoor Environments

Segmentation in dynamic outdoor environments can be difficult when the illumination levels and other aspects of the scene cannot be controlled. Specifically in orchard and vineyard automation contexts, a background material is often used to shield a camera\u27s field of view from other rows of crops. In this paper, we describe a method that uses superpixels to determine low texture regions of the image that correspond to the background material, and then show how this information can be integrated with the color distribution of the image to compute optimal segmentation parameters to segment objects of interest. Quantitative and qualitative experiments demonstrate the suitability of this approach for dynamic outdoor environments, specifically for tree reconstruction and apple flower detection application

Medial Axis Approximation and Regularization

Medial axis is a classical shape descriptor. Among many good properties, medial axis is thin, centered in the shape, and topology preserving. Therefore, it is constantly sought after by researchers and practitioners in their respective domains. However, two barriers remain that hinder wide adoption of medial axis. First, exact computation of medial axis is very difficult. Hence, in practice medial axis is approximated discretely. Though abundant approximation methods exist, they are either limited in scalability, insufficient in theoretical soundness, or susceptible to numerical issues. Second, medial axis is easily disturbed by small noises on its defining shape. A majority of current works define a significance measure to prune noises on medial axis. Among them, local measures are widely available due to their efficiency, but can be either too aggressive or conservative. While global measures outperform local ones in differentiating noises from features, they are rarely well-defined or efficient to compute. In this dissertation, we attempt to address these issues with sound, robust and efficient solutions. In Chapter 2, we propose a novel medial axis approximation called voxel core. We show voxel core is topologically and geometrically convergent to the true medial axis. We then describe a straightforward implementation as a result of our simple definition. In a variety of experiments, our method is shown to be efficient and robust in delivering topological promises on a wide range of shapes. In Chapter 3, we present Erosion Thickness (ET) to regularize instability. ET is the first global measure in 3D that is well-defined and efficient to compute. To demonstrate its usefulness, we utilize ET to generate a family of shape revealing and topology preserving skeletons. Finally, we point out future directions, and potential applications of our works in real world problems

Geometric Algorithms for Modeling Plant Roots from Images

Roots, considered as the ”hidden half of the plant”, are essential to a plant’s health and pro- ductivity. Understanding root architecture has the potential to enhance efforts towards im- proving crop yield. In this dissertation we develop geometric approaches to non-destructively characterize the full architecture of the root system from 3D imaging while making com- putational advances in topological optimization. First, we develop a global optimization algorithm to remove topological noise, with applications in both root imaging and com- puter graphics. Second, we use our topology simplification algorithm, other methods from computer graphics, and customized algorithms to develop a high-throughput pipeline for computing hierarchy and fine-grained architectural traits from 3D imaging of maize roots. Finally, we develop an algorithm for consistently simplifying the topology of nested shapes, with a motivating application in temporal root system analysis. Along the way, we con- tribute to the computer graphics community a pair of topological simplification algorithms both for repairing a single 3D shape and for repairing a sequence of nested shapes

Quantitative Trait Locus Mapping Reveals Regions of the Maize Genome Controlling Root System Architecture.

<p>Root system architecture (RSA) is the spatial distribution of roots of individual plants. As part of a collaborative effort I adapted a gellan gum based system for imaging and phenotyping of root systems in maize. This system was first used to perform a survey of 26 distinct maize varieties of the Nested Association Mapping (NAM) population. The analysis of these data showed a large amount of variation between different RSA, in particular demonstrating tradeoffs between architectures favoring sparse, but far reaching, root networks versus those favoring small but dense root networks. To study this further I imaged and phenotyped the B73 (compact) x Ki3 (exploratory) mapping population. These data were used to map 102 quantitative trait loci (QTL). A large portion of these QTL had large, ranging from 5.48% to 23.8%. Majority of these QTLs were grouped into 9 clusters across the genome, with each cluster favoring either the compact of exploratory RSA. In summary, our study demonstrates the power of the gellan based system to locate loci controlling root system architecture of maize, by combining rapid and highly detailed imaging techniques with semi-automated computation phenotyping.</p>Dissertatio

Analyse der Wurzelarchitektur von Gerste (<em>Hordeum vulgare</em>) unter verschiedenen Umweltbedingungen

Innerhalb der letzten Jahre zeigte der Aufruf zu einer "second green revolution" die Notwendigkeit zur Entwicklung leistungsfähiger Kulturpflanzen mit robustem Verhalten auf wechselnde Umweltbedingungen und limitierte Nährstoffangebote. Die Pflanzenwurzel spielt eine zentrale Rolle bei der Versorgung der Pflanze mit Nährstoffen und Wasser, und die Wurzelarchitektur hat demnach Einfluss auf die Entwicklung und Produktivität der Pflanze. Die Wurzelarchitektur wurde aufgrund methodischer Schwierigkeiten wenig erforscht und daher als mögliches Kriterium zur verbesserten Selektion in der Pflanzenzüchtung wenig berücksichtigt. In der vorliegenden Arbeit wurde als Teilprojekt des Verbundvorhabens „Phenomics, Transcriptomics und Genomics - ein integrierter Ansatz zur Effizienzsteigerung in der Selektion trockenstresstoleranter Gerste“ die Wurzelarchitektur von 6 deutschen Sommerbraugersten (Hordeum vulgare, L. cv. Wisa, LfL24727, Barke, Grace, Braemar, Tatum) zerstörungsfrei im Labor untersucht. Zur Erfassung der Wurzelarchitektur wurden die Gerstensamen oberflächensterilisiert und in einem transparenten Wachstumsmedium in einem axenischen System angezogen. Die Gerstenpflanzen wurden unter verschiedenen Umweltbedingungen, d. h. unter osmotischem Stress, Phosphatmangel und Nitratmangel sowie unter Kontrollbedingungen angezogen. Anschließend erfolgte auf einer speziell angefertigten, rotierenden Bildaufnahme-Plattform die digitale Bildaufnahme des Wurzelsystems der juvenilen Pflanzen nach 16 Tagen Wachstum. Dabei entstanden pro Pflanze bis zu 36 Aufnahmen des Wurzelsystems in einer vollen 360° Umdrehung. Die Digitalbilder des Wurzelsystems wurden zur Berechnung von diversen Parametern, mit welchen die Wurzelarchitektur beschrieben werden kann, verwendet. Dies erfolgte mit dem Einsatz einer dafür entwickelten Bildverarbeitungssoftware. Die Software GiA Roots wurde in Kooperation verschiedener Arbeitsgruppen von Prof. Dr. Philip Benfey, Duke University, Durham NC, USA entwickelt und ermöglicht die Berechnung von 19 verschiedenen Wurzelparametern aus Bilderserien. Pro Gerstensorte und Behandlung wurden von mindestens 10 Individuen die Wurzelarchitektur-Parameter mit GiA Roots 2D (Iyer-Pascuzzi et al., 2010; Galkovskyi et al., 2012) berechnet und anschließend statistisch ausgewertet. Die Ziele dieser Arbeit waren, neben der Etablierung der Anzuchtmethoden der Gerste im axenischen System und der Etablierung einer Hochdurchsatz Bildaufnahme- und Bildverarbeitungspipeline im Labor vor Ort, die zerstörungsfreie Untersuchung der verwendeten Gerstensorten auf phänotypische Unterschiede, um die Robustheit des Wurzelsystems bzgl. der verschiedenen Umweltbedingungen zu charakterisieren. Die Analysen ergaben, dass die Wurzelarchitektur der 6 Sommergersten statistisch signifikante Unterschiede auf einem 5% Niveau bei vielen der gemessenen Wurzelparameter aufweist. Bei Wachstum unter Kontrollbedingungen wurden zwei Wurzel-Phänotypen festgestellt, die Sorten Grace und Barke besitzen ein kleineres Wurzelsystem im Vergleich zu den Sorten Wisa, LfL24727, Braemar und Tatum. Auch zeigten sich unterschiedlich stark ausgeprägte Reaktionen der Sorten bzgl. der Stressversuche. Die Sorten Grace und Braemar zeigten eine, wie in der Literatur unter osmotischem Stress beschriebene, angepasste Veränderung ihrer Wurzelarchitektur. Die Sorten LfL24727, Grace, Braemar und Tatum zeigten außerdem ein an Phosphatmangel adaptiertes Wurzelsystem. Unter Nitratmangel wurden unterschiedlich starke, signifikante Veränderungen der Wurzelarchitektur bei jeder der getesteten Sorten beobachtet. Da die Sorten Grace und Braemar auf alle Stressversuche ein adaptiertes Verhalten des Wurzelsystems zeigten, wurden sie als robuste Sorten eingestuft. Die jeweiligen Reaktionen des juvenilen Wurzelsystems können genutzt werden, um auf bestimmte Eigenschaften der Sorten Rückschlüsse zu ziehen. Dies bietet den Pflanzenzüchtern die Möglichkeit, kombiniert mit ihren Erfahrungen über die Eigenschaften der Sorten, die hier beschriebene Methode zur Analyse der Wurzelarchitektur juveniler Gerste als zusätzliches Instrument zur Beurteilung von Sorten einzusetzen.Analysis of root system architecture (RSA) of six spring barley varieties under different environmental conditions Within the last years the call to a "second green revolution" showed the need for the development of efficient crops with robust behavior at varying environmental conditions and limited nutrient constraints. The plants’ root system plays a central role in plant nutrition and, therefore, the root architecture has influence on the development and productiveness of a plant. However, because of the methodical difficulties little attention was investigated to explore the root architecture and, hence, it was long ignored as a possible selection criterion for improved plant breeding. In the present work, the root architecture of 6 German spring barley varieties (Hordeum vulgare, L. cv. Wisa, LfL24727, Barke, Grace, Braemar and Tatum) was examined using a non-destructive approach. This was a sub project of the research project „Phenomics, Transcriptomics und Genomics - ein integrierter Ansatz zur Effizienzsteigerung in der Selektion trockenstresstoleranter Gerste“. To study the root architecture, the barley seeds were surface sterilized and grown in a transparent gellan gum under sterile conditions. Plants were grown under differential environmental conditions (osmotic stress, phosphate and nitrogen deficiency and control conditions). To obtain images of the 16-days-old roots, plants were imaged on a semi-automated rotating imaging platform. Up to 36 images per plant were captured every 10° through a full 360° rotation. Out of these images 19 root architecture parameters were calculated using GiA Roots software (Iyer-Pascuzzi et al., 2010). In cooperation with different working groups, GiA Root was developed by Prof. Dr. Philip Benfey, Duke University, Durham NC, USA. It was developed as a high-throughput image analyzing pipeline for image series and allows 19 different root architecture parameters to be considered. In this work more than 10 individuals of each barley variety and each treatment were used for image analysis followed by statistical analysis. The objectives of this work were to establish the plant culture methods and the imaging-pipeline and the analysis of the barley plants for phenotypic characteristics to evaluate the robustness of the roots under different conditions. Statistical analysis revealed that many significant differences exist at 5 per cent between the cultivars’ root parameter. Two different root phenotypes were identified grown on control medium. The varieties Barke and Grace showed a comparably small root system in contrast to the cultivars LfL24727, Grace, Braemar and Tatum. The root system responded differently to the stress treatments. The cultivars Grace and Braemar showed an adaptive reaction of the root system to osmotic stress. The cultivars LfL24727, Grace, Braemar and Tatum changed their root architecture and showed a smaller and more branched root system at the ground surface. Under nitrate deficiency all the cultivars showed distinct responses of the root system. The cultivars Grace and Braemar exhibited the most adaptive reaction and are therefore considered to be robust cultivars. The respective reactions of the juvenile root system can be used to draw conclusions on certain qualities of the cultivars. This offers the plant breeders the possibility to use this imaging system as an additional instrument for breeding selection

Tree Topology Estimation

<p>Tree-like structures are fundamental in nature. A wide variety of two-dimensional imaging techniques allow us to image trees. However, an image of a tree typically includes spurious branch crossings and the original relationships of ancestry among edges may be lost. We present a methodology for estimating the most likely topology of a rooted, directed, three-dimensional tree given a single two-dimensional image of it. We regularize this inverse problem via a prior parametric tree-growth model that realistically captures the morphology of a wide variety of trees. We show that the problem of estimating the optimal tree has linear complexity if ancestry is known, but is NP-hard if it is lost. For the latter case, we present both a greedy approximation algorithm and a heuristic search algorithm that effectively explore the space of possible trees. Experimental results on retinal vessel, plant root, and synthetic tree datasets show that our methodology is both accurate and efficient.</p>Dissertatio