Image-based plant phenotyping with incremental learning and active contours
Image-based plant phenotyping with incremental learning and active contours
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DOI：
10.1016/j.ecoinf.2013.07.004
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发表时间：
2014-09-01
影响因子：
5.1
通讯作者：
Tsaftaris, Sotirios A.
中科院分区：
文献类型：
--
作者：
Minervini, Massimo;Abdelsamea, Mohammed M.;Tsaftaris, Sotirios A.
植物表型研究植物的基因组如何与环境相互作用，影响植物的可观察性状（表型组）。在我们追求高效和可持续农业的过程中，它变得越来越重要。虽然基因组测序变得越来越有效，但获取表型信息在很大程度上仍然是低通量的。当前用于自动化基于图像的植物表型分析的解决方案依赖于成像数据的半自动化或手动分析，或者依赖于伴随昂贵的硬件基础设施的昂贵且专有的软件。虽然已经进行了一些尝试来创建能够以自动化方式分析这样的图像的软件应用，但是大多数解决方案都是针对特定的采集场景和对实验设计的限制而定制的。在本文中，我们提出和测试，一种方法的分割和自动分析的时间推移植物图像表型实验在一般的实验室设置，可以适应场景的变化。该方法涉及最少的用户交互，这是建立随后可能进行的统计实验所必需的。在每个时间实例（即，数码照片），它分割图像中的植物，其中包含许多相同物种的标本。为了准确的植物分割，我们提出了一个矢量值水平集配方，结合颜色强度，局部纹理和先验知识的功能。先验知识采用高斯混合模型实现的植物外观模型，该模型利用来自先前分割实例的增量信息。所提出的方法进行了测试，拟南芥植物的图像与静态相机捕获许多科目在同一时间。我们的验证与地面实况分割和比较与国家的最先进的方法在文献中表明，所提出的方法是能够处理图像的复杂和不断变化的背景中的自动化的方式。结果表明，该方法的准确率为96.7%（骰子相似系数），高于其他方法。虽然在这里它是在单个植物物种上进行测试的，但事实上我们不采用形状驱动模型，也不依赖于完全监督分类（在大型数据集上训练），这增加了在各种实验室环境中研究不同植物物种的拟议解决方案的部署的容易性。我们的解决方案将伴随着一个易于使用的图形用户界面，为了便于采用，我们将向科学界提供该软件。(C)2013爱思唯尔有限公司版权所有。
Plant phenotyping investigates how a plant's genome, interacting with the environment, affects the observable traits of a plant (phenome). It is becoming increasingly important in our quest towards efficient and sustainable agriculture. While sequencing the genome is becoming increasingly efficient, acquiring phenotype information has remained largely of low throughput. Current solutions for automated image-based plant phenotyping, rely either on semi-automated or manual analysis of the imaging data, or on expensive and proprietary software which accompanies costly hardware infrastructure. While some attempts,have been made to create software applications that enable the analysis of such images in an automated fashion, most solutions are tailored to particular acquisition scenarios and restrictions on experimental design. In this paper we propose and test, a method for the segmentation and the automated analysis of time-lapse plant images from phenotyping experiments in a general laboratory setting, that can adapt to scene variability. The method involves minimal user interaction, necessary to establish the statistical experiments that may follow. At every time instance (i.e., a digital photograph), it segments the plants in images that contain many specimens of the same species. For accurate plant segmentation we propose a vector valued level set formulation that incorporates features of color intensity, local texture, and prior knowledge. Prior knowledge is incorporated using a plant appearance model implemented with Gaussian mixture models, which utilizes incrementally information from previously segmented instances. The proposed approach is tested on Arabidopsis plant images acquired with a static camera capturing many subjects at the same time. Our validation with ground truth segmentations and comparisons with state-of-the-art methods in the literature shows that the proposed method is able to handle images with complicated and changing background in an automated fashion. An accuracy of 96.7% (dice similarity coefficient) was observed, which was higher than other methods used for comparison. While here it was tested on a single plant species, the fact that we do not employ shape driven models and we do not rely on fully supervised classification (trained on a large dataset) increases the ease of deployment of the proposed solution for the study of different plant species in a variety of laboratory settings. Our solution will be accompanied by an easy to use graphical user interface and, to facilitate adoption, we will make the software available to the scientific community. (C) 2013 Elsevier B.V. All rights reserved.