MowJoe: a method for automated-high throughput dissected leaf phenotyping

BackgroundAccurate and automated phenotyping of leaf images is necessary for high throughput studies of leaf form like genome-wide association analysis and other forms of quantitative trait locus mapping. Dissected leaves (also referred to as compound) that are subdivided into individual units are an attractive system to study diversification of form. However, there are only few software tools for their automated analysis. Thus, high-throughput image processing algorithms are needed that can partition these leaves in their phenotypically relevant units and calculate morphological features based on these units.ResultsWe have developed MowJoe, an image processing algorithm that dissects a dissected leaf into leaflets, petiolule, rachis and petioles. It employs image skeletonization to convert leaves into graphs, and thereafter applies algorithms operating on graph structures. This partitioning of a leaf allows the derivation of morphological features such as leaf size, or eccentricity of leaflets. Furthermore, MowJoe automatically places landmarks onto the terminal leaflet that can be used for further leaf shape analysis. It generates specific output files that can directly be imported into downstream shape analysis tools. We applied the algorithm to two accessions of Cardamine hirsuta and show that our features are able to robustly discriminate between these accessions.ConclusionMowJoe is a tool for the semi-automated, quantitative high throughput shape analysis of dissected leaf images. It provides the statistical power for the detection of the genetic basis of quantitative morphological variations.

[1]  Y. Eshed,et al.  Morphogenesis of Simple and Compound Leaves: A Critical Review , 2010, Plant Cell.

[2]  Christian Peter Klingenberg,et al.  Evolution and development of shape: integrating quantitative approaches , 2010, Nature Reviews Genetics.

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

[4]  Daniel Parnham,et al.  LeafAnalyser: a computational method for rapid and large-scale analyses of leaf shape variation. , 2008, The Plant journal : for cell and molecular biology.

[5]  P. Prusinkiewicz,et al.  Model for the regulation of Arabidopsis thaliana leaf margin development , 2011, Proceedings of the National Academy of Sciences.

[6]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[7]  Hanno Scharr,et al.  Leaf segmentation in plant phenotyping: a collation study , 2016, Machine Vision and Applications.

[8]  C. Klingenberg.  MorphoJ: an integrated software package for geometric morphometrics , 2011, Molecular ecology resources.

[9]  J. Trygg,et al.  LAMINA: a tool for rapid quantification of leaf size and shape parameters , 2008, BMC Plant Biology.

[10]  D. Chitwood,et al.  Divergent leaf shapes among Passiflora species arise from a shared juvenile morphology , 2016, bioRxiv.

[11]  Laure Tougne,et al.  A model-based approach for compound leaves understanding and identification , 2013, ICIP.

[12]  Khalid Saeed,et al.  K3M: A universal algorithm for image skeletonization and a review of thinning techniques , 2010, Int. J. Appl. Math. Comput. Sci..

[13]  G. P. Lohmann.  Eigenshape analysis of microfossils: A general morphometric procedure for describing changes in shape , 1983 .

[14]  Arnab Bhattacharya,et al.  A Plant Identification System using Shape and Morphological Features on Segmented Leaflets: Team IITK, CLEF 2012 , 2012, CLEF.

[15]  R. Mott,et al.  Cardamine hirsuta: a versatile genetic system for comparative studies. , 2014, The Plant journal : for cell and molecular biology.

[16]  M. Barkoulas,et al.  Weeds of change: Cardamine hirsuta as a new model system for studying dissected leaf development , 2009, Journal of Plant Research.

[17]  Karl J. Niklas,et al.  The evolution and functional significance of leaf shape in the angiosperms. , 2011, Functional plant biology : FPB.

[18]  Achim Tresch,et al.  Semi-automated 3D Leaf Reconstruction and Analysis of Trichome Patterning from Light Microscopic Images , 2013, PLoS Comput. Biol..

[19]  J. Krieger,et al.  Controlling for Curvature in the Quantification of Leaf Form , 2010 .

[20]  Miltos Tsiantis,et al.  A developmental framework for dissected leaf formation in the Arabidopsis relative Cardamine hirsuta , 2008, Nature Genetics.

[21]  Nian Liu,et al.  Improved deep belief networks and multi-feature fusion for leaf identification , 2016, Neurocomputing.

[22]  Miltos Tsiantis,et al.  The genetic basis for differences in leaf form between Arabidopsis thaliana and its wild relative Cardamine hirsuta , 2006, Nature Genetics.

[23]  Guido Sanguinetti,et al.  LEAFPROCESSOR: a new leaf phenotyping tool using contour bending energy and shape cluster analysis. , 2010, The New phytologist.

[24]  Norman MacLeod,et al.  Generalizing and extending the eigenshape method of shape space visualization and analysis , 1999, Paleobiology.

[25]  Guillaume Lobet,et al.  Image Analysis in Plant Sciences: Publish Then Perish. , 2017, Trends in plant science.

[26]  F. Vuolo,et al.  Leaf Shape Evolution Through Duplication, Regulatory Diversification, and Loss of a Homeobox Gene , 2014, Science.

[27]  L. Rieseberg,et al.  Phenotypic selection on leaf ecophysiological traits in Helianthus. , 2009, The New phytologist.

[28]  Christine M. Palmer,et al.  LeafJ: an ImageJ plugin for semi-automated leaf shape measurement. , 2013, Journal of visualized experiments : JoVE.

[29]  Kevin W. Eliceiri,et al.  ImageJ2: ImageJ for the next generation of scientific image data , 2017, BMC Bioinformatics.

[30]  Nozha Boujemaa,et al.  Multiple leaflets-based identification approach for compound leaf species , 2014, EMR@ICMR.

[31]  W. John Kress,et al.  Leafsnap: A Computer Vision System for Automatic Plant Species Identification , 2012, ECCV.

[32]  E. Coen,et al.  Generation of Leaf Shape Through Early Patterns of Growth and Tissue Polarity , 2012, Science.

[33]  José Luis Micol,et al.  Mutational spaces for leaf shape and size , 2008, HFSP journal.

[34]  S. Ferson,et al.  Measuring shape variation of two-dimensional outlines , 1985 .

[35]  Achim Tresch,et al.  Heterochrony underpins natural variation in Cardamine hirsuta leaf form , 2015, Proceedings of the National Academy of Sciences.

[36]  Allison J. Miller,et al.  Digital Morphometrics of Two North American Grapevines (Vitis: Vitaceae) Quantifies Leaf Variation between Species, within Species, and among Individuals , 2017, Front. Plant Sci..

[37]  Arnold J. Bloom,et al.  Easy Leaf Area: Automated digital image analysis for rapid and accurate measurement of leaf area1 , 2014, Applications in plant sciences.

[38]  Arezki Boudaoud,et al.  Multiscale quantification of morphodynamics: MorphoLeaf software for 2D shape analysis , 2016, Development.

[39]  Bin Wang,et al.  Similarity based leaf image retrieval using multiscale R-angle description , 2016, Inf. Sci..

[40]  Kevin W. Eliceiri,et al.  ImageJ for the Next Generation of Scientific Image Data , 2019, Microscopy and Microanalysis.

[41]  M. Nogueira,et al.  ADAPTIVE RADIATIONS, ECOLOGICAL SPECIALIZATION, AND THE EVOLUTIONARY INTEGRATION OF COMPLEX MORPHOLOGICAL STRUCTURES , 2010, Evolution; international journal of organic evolution.