Multiscale imaging of plants: current approaches and challenges
© Rousseau et al.; licensee BioMed Central. 2015
Received: 9 October 2014
Accepted: 25 January 2015
Published: 10 February 2015
We review a set of recent multiscale imaging techniques, producing high-resolution images of interest for plant sciences. These techniques are promising because they match the multiscale structure of plants. However, the use of such high-resolution images is challenging in the perspective of their application to high-throughput phenotyping on large populations of plants, because of the memory cost for their data storage and the computational cost for their processing to extract information. We discuss how this renews the interest for multiscale image processing tools such as wavelets, fractals and recent variants to analyse such high-resolution images.
Finding the good practices to perform high-throughput phenotyping of large populations of plants is a current challenge to meet the high-throughput capacity of genotyping and push forward the knowledge on the development of plants in different environments. Because they allow contactless and noninvasive measurements, imaging techniques are regarded as tools of highest interest in this context, to provide anatomical or physiological objective traits and outperform the limit of human vision either in terms of sensitivity, accuracy or throughput. Conversely, plant sciences constitute a new field of application for computer vision which traditionally, when applied in life sciences, used to focus more on biomedical imaging. Among the specificities of computer vision for plant sciences that are not found in biomedical imaging, is the possibility to monitor, continuously over the whole life cycle, the process of growth on structures possessing complex 3D multiscale organisation with a part visible in the air (shoot) and a part hidden in the soil (root).
There has been a significant increase in interest in plant imaging and image analysis methods in recent years, but most of the techniques proposed focus on measurements at a single scale - cell, organ, whole plant, etc. This is in contrast to modelling efforts which have stressed multiscale approaches. Such numerical models have been proposed at the scale of the entire structure of plants from iterated replication processes using L-systems (see [1,2] for reviews). Such replication processes have been shown able to reproduce the fractal organization of plant structures as measured on entire real plants. These can also serve to model the root systems  and have recently been used to validate image processing algorithms for root segmentation . Multiple plant modeling coupled to agronomical models have also been developed  and allow the numerical validation of image processing algorithms at the scale of canopy. Replication processes have also been modeled at the cellular scale with possibilities of explanatory physical mechanisms for the shape of the plant at higher scales . As another instance, the so-called dead leaves model takes inspiration from the foliage of plants, with leaves of different sizes and illumination which are reproduced at various scales with occlusions [7-9]. Such models have been shown to produce fractal patterns with controllable properties, and in return they offer models for the multiscale constitution of plants.
Due to the increase in size and resolution of the imaging sensors and to the development of efficient registration methods, the number of scales accessible in imaging is now ready to meet the multiscale structure of plants. In this review article, we present a set of recent high-resolution imaging techniques which cover the plant scales from molecules in the cell up to the field, and we detail how this renews the interest of scale-analysis tools for image processing.
Multiscale high-resolution imaging in plant sciences
Multiple scale high-resolution imaging in plant sciences
From molecule to cell
10 nm to 10 μm
From cell to organs
0.1 μm to dcm
X-ray PCT ,
From nodules to root system
μm to m
From leaf to entire shoot
mm to 10 m
From shoot to canopy
m to hm
The list of imaging techniques given in Table 1 is not exhaustive (see [32-34] for recent reviews). This familly of new imaging systems bring some challenges that would be interesting to be discussed in the field of instrumentation when applied to plants. To point only one, the new microscopies of Table 1 have been introduced for applications of broad interest in life sciences and often demonstrated on organisms which serve as models for biology, such as C-elegans, zebra fish, mice, Drosophilae fly or Arabidopsis thaliana... Consequently, the non-invasiveness property of the light used to acquire images of such a variety of organisms is mainly expressed as nonphototoxic if it does not kill the organism on a time scale linked with the time required by instrumentation for image acquisition. For specific applications on plants however, imaging the development can necessitate long time-lapsed acquisitions. For instance, imbibition and germination of a seed take hours while elongation of a seedling several days. At these stages of development illustrated in Figure 1, plants are supposed to grow in dark conditions in the soil with no light exposure, as light strongly modifies the physiology of seedlings since it activates the process of photosynthesis. It would therefore be important to revisit, as recently done for seedling in , the concept of phototoxicity, by adapting wavelength, energy and duration of the light used by the family of mutiscale microscopies when applied to plants.
In this review, we rather put the stress on current approaches and challenges brought by new imaging systems at the level of image processing. The point here is that techniques of Table 1 have in common, although working at very different metric and biological scales, to produce images requiring a huge capacity of data storage. This is due to the increasing size, resolution and dynamic of imaging sensors, but also to the coupling of imaging systems with motorized scanning systems. By this coupling, multiple views can be acquired and registered to produce high-resolution imaging. Multiview imaging is common practice in remote sensing. This is now extending to the scale of a single plant with rotating plates, or at the scale of the cells with microscope scanners. For instance, the OCT system used to produce Figure 1 is associated to microstage translation systems, in such a way that the imaging technique can, after registration, capture in 3D and at the cell resolution, hundreds of such entire seedlings in a single run, resulting in some Giga bytes of data. This is 106 more than what has to be stored for one single plant imaged with a standard imaging resolution. Such large images can still be opened by a software like ImageJ but image processing, even some basic ones, can become very slow. This high memory cost, specially in the perspective of high-througput phenotyping for large population of plants, calls for adapted approaches. We propose a review of the most prominent of them in the following.
Image processing tools for multiscale imaging
Combining modalities with different scales
Multiple scale image processing tools available under the free and open software ImageJ
Image processing task
ImageJ plugin weblink
Multiscale blob extraction
Multiscale vessellness extraction
Nonlocal mean denoising
Multiscale color analysis
The selection of structures appearing in the images at given scales can be realized with filters. The design of these filters has to incorporate some prior knowledge on the shape of the objects to be found at each scale. Among the strategies for the design of bank of filters, wavelets have shown to be a very powerful approach for application in plant sciences, see  for a review of the late 90’s, which continue to be investigated to select patterns on leaves [41-44] or on canopies [45,46]. The wavelet approach is versatile since a large panel of wavelet functions have been designed. A wavelet is a wave-like oscillation with an amplitude that begins at zero, increases, and then decreases back to zero on a scale which can be defined by the user. Such functions are expected to constitute good filters when they share common features with the shape of the objects to be extracted. Some familly of filters have specifically been developed to extract given shapes. Let us shortly underline the vesselness filter  which enhances area in images where the gradient in the image is almost null in one direction and much higher in the other perpendicular directions. This situation is found with any tubular structures. Therefore, although initially developed to enhance biomedical images with vascular vessels the vesslness filter is also very much suited to enhance tubular structures met in plant sciences such as cell walls, leaf veins or branches in trees. Based on the same philosophy, enhancing areas with high gradient in all directions extract the blob-like structures  (cells, nodules, spherical fruits, …), or also enhancing areas with high gradient in only one direction in space extracts surface-like structures (cellular layers, plant leaves,…). These familly of filters are available under ImageJ as mentioned in Table 2.
When no prior knowledge on the shapes or scales of the objects of interest in the image is available, it is necessary to use self-adaptive methods to automatically select the appropriate scales of interest. Such methods are known as wavelet packets decomposition. However, in this case, the choice of the wavelet and of the range of scales to be analysed still have to be performed by the user. Another self-adaptive method, of more recent introduction, is the empirical mode decomposition also called Hilbert-Huang transform, where the scale analysis is purely based on the data itself. Data-dependent modes, corresponding to the local frequency data, are extracted by the analysis to decompose the signal, instead of a decomposition on preexisting elemental functions such as wavelets. Introduced for monodimensional signals , empirical mode decomposition has then been extended to images  and successfully applied to texture characterization . The dominant modes of the decomposition single out the main scales in the signals or images under analysis, and keeping only the dominant modes offers natural methods for parsimonious representation and for data compression. Efficient compression schemes have been developed for landscapes captured in remote sensing for scales from canopy to field . Such compression approaches by scale selection remain open for investigation for the other scales of Table 1.
Characterizing multiscale signatures
Instead of selecting specific scales of interest, another approach is to characterize the global organization over multiple scales in the images. Nontrivial regularities developing in a self-similar way across a significant range of scales usually identify the existence of a fractal organization. Fractal concepts have been shown relevant to the description of plants, of their roots and shoots, which often show self-similar organizations across scales [55-59]. Especially, such organizations lead to high surface areas at the interfaces with the environment, ensuring for the plant efficient capture of nutrients and energy.
Self-similarity accross scales, i.e. fractal features, can thus be found in various properties of images from plants. For instance, they have been reported in the spatial organization of gray-level luminance images from outdoor scenes of woods and plants [60,61]. This is manifested by scale-free power-law evolutions present in the frequency spectrum of luminance images or also in their spatial correlation functions. Also, the colorimetric organization of natural images including landscapes with plants has been reported to carry self-similarity and fractal properties [62-64]. More recently, multiscale analysis has been undertaken for plant images obtained from another imaging technique delivering depth images of a physical scene . The depth map images from outdoor scenes of woods and plants as in [60,61] were shown in  to also reveal self-similarity and fractal properties. Such multiscale image analyses revealing and characterizing fractal properties in plants are important to contribute to their understanding, since the fractal and multiscale organization of plants has a direct impact on their functioning, for instance for efficient interactions with their environment as evoked above [58,66-68]. Also, fractal characterization of plants is useful to devise synthetic models of plants with sufficient realism .
High-resolution multiscale imaging in plant sciences was until recently limited to the domain of remote sensing. It is now also possible to capture entire roots or shoots of plants, at various stages of development, with cellular or subcellular spatial resolution. These high-resolution imagings are producing huge amounts of data, specially when they are applied to large populations of plants in high-throughput phenotyping. In this framework, we have highlighted here some current approaches connected to the multiscale analysis of plants and pointed toward efficient computational implementation under the free and open software ImageJ in Table 2. Open problems emerge for image compression and image characterization. Multiscale approaches are specifically relevant for the new microscopies such as those presented in Table 1; these are more recent and have received so far, in a multiscale perspective, less attention than remote-sensing imaging or than proximal detection in the field.
This work received supports from the French Government supervised by the Agence Nationale de la Recherche in the framework of the program Investissements d’Avenir under reference ANR-11-BTBR-0007 (AKER program).
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