Henke Michael, Junker Astrid, Neumann Kerstin, Altmann Thomas, Gladilin Evgeny
Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
Front Plant Sci. 2018 Oct 16;9:1519. doi: 10.3389/fpls.2018.01519. eCollection 2018.
Modern facilities for high-throughput phenotyping provide plant scientists with a large amount of multi-modal image data. Combination of different image modalities is advantageous for image segmentation, quantitative trait derivation, and assessment of a more accurate and extended plant phenotype. However, visible light (VIS), fluorescence (FLU), and near-infrared (NIR) images taken with different cameras from different view points in different spatial resolutions exhibit not only relative geometrical transformations but also considerable structural differences that hamper a straightforward alignment and combined analysis of multi-modal image data. Conventional techniques of image registration are predominantly tailored to detection of relative geometrical transformations between two otherwise identical images, and become less accurate when applied to partially similar optical scenes. Here, we focus on a relatively new technical problem of FLU/VIS plant image registration. We present a framework for automated alignment of FLU/VIS plant images which is based on extension of the phase correlation (PC) approach - a frequency domain technique for image alignment, which relies on detection of a phase shift between two Fourier-space transforms. Primarily tailored to detection of affine image transformations between two structurally identical images, PC is known to be sensitive to structural image distortions. We investigate effects of image preprocessing and scaling on accuracy of image registration and suggest an integrative algorithmic scheme which allows to overcome shortcomings of conventional single-step PC by application to non-identical multi-modal images. Our experimental tests with FLU/VIS images of different plant species taken on different phenotyping facilities at different developmental stages, including difficult cases such as small plant shoots of non-specific shape and non-uniformly moving leaves, demonstrate improved performance of our extended PC approach within the scope of high-throughput plant phenotyping.
现代高通量表型分析设施为植物科学家提供了大量的多模态图像数据。不同图像模态的组合有利于图像分割、数量性状推导以及更准确和全面的植物表型评估。然而，使用不同相机从不同视角以不同空间分辨率拍摄的可见光（VIS）、荧光（FLU）和近红外（NIR）图像不仅存在相对几何变换，还存在显著的结构差异，这阻碍了多模态图像数据的直接对齐和联合分析。传统的图像配准技术主要针对检测两个其他方面相同的图像之间的相对几何变换，当应用于部分相似的光学场景时准确性会降低。在这里，我们专注于FLU/VIS植物图像配准这一相对较新的技术问题。我们提出了一个用于FLU/VIS植物图像自动对齐的框架，该框架基于相位相关（PC）方法的扩展——一种用于图像对齐的频域技术，它依赖于检测两个傅里叶空间变换之间的相位偏移。PC主要用于检测两个结构相同的图像之间的仿射图像变换，已知对结构图像失真敏感。我们研究了图像预处理和缩放对图像配准精度的影响，并提出了一种综合算法方案，该方案通过应用于不同的多模态图像来克服传统单步PC的缺点。我们对在不同表型分析设施上不同发育阶段拍摄的不同植物物种的FLU/VIS图像进行的实验测试，包括诸如非特定形状的小植物嫩枝和不均匀移动叶片等困难情况，证明了我们扩展的PC方法在高通量植物表型分析范围内的性能提升。
