Visual tracking for the recovery of multiple interacting plant root systems from X-ray μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt}

We propose a visual object tracking framework for the extraction of multiple interacting plant root systems from three-dimensional X-ray micro computed tomography images of plants grown in soil. Our method is based on a level set framework guided by a greyscale intensity distribution model to identify object boundaries in image cross-sections. Root objects are followed through the data volume, while updating the tracker’s appearance models to adapt to changing intensity values. In the presence of multiple root systems, multiple trackers can be used, but need to distinguish target objects from one another in order to correctly associate roots with their originating plants. Since root objects are expected to exhibit similar greyscale intensity distributions, shape information is used to constrain the evolving level set interfaces in order to lock trackers to their correct targets. The proposed method is tested on root systems of wheat plants grown in soil.

[1]  Tony P. Pridmore,et al.  Visual Object Tracking for the Extraction of Multiple Interacting Plant Root Systems , 2014, ECCV Workshops.

[2]  Peter N. Yianilos,et al.  Data structures and algorithms for nearest neighbor search in general metric spaces , 1993, SODA '93.

[3]  H. Schenk,et al.  Root competition: beyond resource depletion , 2006 .

[4]  J. Lynch Root Architecture and Plant Productivity , 1995, Plant physiology.

[5]  D. Chopp Computing Minimal Surfaces via Level Set Curvature Flow , 1993 .

[6]  Stefan Mairhofer,et al.  RooTrak: Automated Recovery of Three-Dimensional Plant Root Architecture in Soil from X-Ray Microcomputed Tomography Images Using Visual Tracking1[W] , 2011, Plant Physiology.

[7]  Gösta H. Granlund,et al.  Fourier Preprocessing for Hand Print Character Recognition , 1972, IEEE Transactions on Computers.

[8]  S. Mooney,et al.  Effects of X-Ray Dose On Rhizosphere Studies Using X-Ray Computed Tomography , 2013, PloS one.

[9]  Milan Sonka,et al.  Intrathoracic airway trees: segmentation and airway morphology analysis from low-dose CT scans , 2005, IEEE Transactions on Medical Imaging.

[10]  Joshua S. Weitz,et al.  Leaf Extraction and Analysis Framework Graphical User Interface: Segmenting and Analyzing the Structure of Leaf Veins and Areoles1[W][OA] , 2010, Plant Physiology.

[11]  Jianhua Lin,et al.  Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.

[12]  D. Aykac,et al.  Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images , 2003, IEEE Transactions on Medical Imaging.

[13]  Anders Kaestner,et al.  Visualizing three-dimensional root networks using computed tomography , 2006 .

[14]  Frank Dellaert,et al.  MCMC-based particle filtering for tracking a variable number of interacting targets , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  R. MacCurdy,et al.  Three-Dimensional Root Phenotyping with a Novel Imaging and Software Platform1[C][W][OA] , 2011, Plant Physiology.

[16]  Erik Meijering,et al.  Neuron tracing in perspective , 2010, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[17]  J. Sethian Level Set Techniques for Tracking Interfaces ; Fast Algorithms , Multiple Regions , Grid Generation , and Shape / Character Recognition , 2006 .

[18]  J. Perret,et al.  Non-destructive visualization and quantification of roots using computed tomography , 2007 .

[19]  Ulrich Schurr,et al.  Combined MRI-PET dissects dynamic changes in plant structures and functions. , 2009, The Plant journal : for cell and molecular biology.

[20]  Azriel Rosenfeld,et al.  Connectivity in Digital Pictures , 1970, JACM.

[21]  Stefan Mairhofer,et al.  On the evaluation of methods for the recovery of plant root systems from X-ray computed tomography images. , 2015, Functional plant biology : FPB.

[22]  Paul L. Rosin Unimodal thresholding , 2001, Pattern Recognit..

[23]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Nicolas Flasque,et al.  Acquisition, segmentation and tracking of the cerebral vascular tree on 3D magnetic resonance angiography images , 2001, Medical Image Anal..

[25]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid , 2012 .

[26]  Alex M. Andrew,et al.  Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science (2nd edition) , 2000 .

[27]  Chris Moran,et al.  X-ray computed tomography to quantify tree rooting spatial distributions , 1999 .

[28]  Mathieu Javaux,et al.  Root System Markup Language: Toward a Unified Root Architecture Description Language1[OPEN] , 2015, Plant Physiology.

[29]  M. Crespi,et al.  Plant root growth, architecture and function , 2009, Plant and Soil.

[30]  S. Mooney,et al.  Developing X-ray Computed Tomography to non-invasively image 3-D root systems architecture in soil , 2011, Plant and Soil.

[31]  Peter J. Gregory,et al.  Rhizosphere geometry and heterogeneity arising from root-mediated physical and chemical processes. , 2005, The New phytologist.

[32]  J. Alison Noble,et al.  An adaptive segmentation algorithm for time-of-flight MRA data , 1999, IEEE Transactions on Medical Imaging.

[33]  Ulrich Schurr,et al.  Direct comparison of MRI and X-ray CT technologies for 3D imaging of root systems in soil: potential and challenges for root trait quantification , 2015, Plant Methods.

[34]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[35]  Milan Sonka,et al.  Rule-based detection of intrathoracic airway trees , 1996, IEEE Trans. Medical Imaging.

[36]  J. Hopmans,et al.  Three dimensional imaging of plant roots in situ with X-ray Computed Tomography , 1997, Plant and Soil.

[37]  Philippe Lucidarme,et al.  On the use of depth camera for 3D phenotyping of entire plants , 2012 .

[38]  Luc Vincent,et al.  Morphological grayscale reconstruction in image analysis: applications and efficient algorithms , 1993, IEEE Trans. Image Process..

[39]  Stefan Mairhofer,et al.  Recovering complete plant root system architectures from soil via X-ray μ-Computed Tomography , 2013, Plant Methods.

[40]  Shiv O. Prasher,et al.  Advances in the acquisition and analysis of CT scan data to isolate a crop root system from the soil medium and quantify root system complexity in 3-D space , 2006 .

[41]  A. Greenberg,et al.  High-Resolution Inflorescence Phenotyping Using a Novel Image-Analysis Pipeline, PANorama1[W][OPEN] , 2014, Plant Physiology.

[42]  Xavier Draye,et al.  Novel scanning procedure enabling the vectorization of entire rhizotron-grown root systems , 2013, Plant Methods.

[43]  J. Lynch,et al.  Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field , 2011, Plant and Soil.

[44]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[45]  Hongkai Zhao,et al.  A fast sweeping method for Eikonal equations , 2004, Math. Comput..

[46]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.