@article{2822,
  abstract     = {Identification of genes that control root system architecture in crop plants requires innovations that enable high-throughput and accurate measurements of root system architecture through time. We demonstrate the ability of a semiautomated 3D in vivo imaging and digital phenotyping pipeline to interrogate the quantitative genetic basis of root system growth in a rice biparental mapping population, Bala x Azucena. We phenotyped >1,400 3D root models and >57,000 2D images for a suite of 25 traits that quantified the distribution, shape, extent of exploration, and the intrinsic size of root networks at days 12, 14, and 16 of growth in a gellan gum medium. From these data we identified 89 quantitative trait loci, some of which correspond to those found previously in soil-grown plants, and provide evidence for genetic tradeoffs in root growth allocations, such as between the extent and thoroughness of exploration. We also developed a multivariate method for generating and mapping central root architecture phenotypes and used it to identify five major quantitative trait loci (r2 = 24-37%), two of which were not identified by our univariate analysis. Our imaging and analytical platform provides a means to identify genes with high potential for improving root traits and agronomic qualities of crops.},
  author       = {Topp, Christopher and Iyer Pascuzzi, Anjali and Anderson, Jill and Lee, Cheng and Zurek, Paul and Symonova, Olga and Zheng, Ying and Bucksch, Alexander and Mileyko, Yuriy and Galkovskyi, Taras and Moore, Brad and Harer, John and Edelsbrunner, Herbert and Mitchell Olds, Thomas and Weitz, Joshua and Benfey, Philip},
  journal      = {PNAS},
  number       = {18},
  pages        = {E1695 -- E1704},
  publisher    = {National Academy of Sciences},
  title        = {{3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture}},
  doi          = {10.1073/pnas.1304354110},
  volume       = {110},
  year         = {2013},
}

@inproceedings{2903,
  abstract     = {In order to enjoy a digital version of the Jordan Curve Theorem, it is common to use the closed topology for the foreground and the open topology for the background of a 2-dimensional binary image. In this paper, we introduce a single topology that enjoys this theorem for all thresholds decomposing a real-valued image into foreground and background. This topology is easy to construct and it generalizes to n-dimensional images.},
  author       = {Edelsbrunner, Herbert and Symonova, Olga},
  location     = {New Brunswick, NJ, USA },
  pages        = {41 -- 48},
  publisher    = {IEEE},
  title        = {{The adaptive topology of a digital image}},
  doi          = {10.1109/ISVD.2012.11},
  year         = {2012},
}