Quantitative morphological analysis of 2D images of complex-shaped branching biological growth forms: the example of branching thalli of liverworts
© The Author(s) 2017
Received: 12 May 2016
Accepted: 9 February 2017
Published: 20 February 2017
Many organisms such as plants can be characterized as complex-shaped branching forms. The morphological quantification of the forms is a support for a number of areas such as the effects of environmental factors and species discrimination. To date, there is no software package suitable for our dataset representing the forms. We therefore formulate methods for extracting morphological measurements from images of the forms.
As a case study we analyze two-dimensional images of samples from four groups belonging to three species of thalloid liverworts, genus Riccardia. The images are pre-processed and converted into binary images, then skeletonized to obtain a skeleton image, in which features such as junctions and terminals are detected. Morphological measurements known to characterize and discriminate the species in the samples such as junction thickness, branch thickness, terminal thickness, branch length, branch angle, and terminal spacing are then quantified. The measurements are used to distinguish among the four groups of Riccardia and also between the two groups of Riccardia amazonica collected in different locations, Africa and South America. Canonical discriminant analysis results show that those measurements are able to discriminate among the four groups. Additionally, it is able to discriminate R. amazonica collected in Africa from those collected in South America.
This paper presents general automated methods implemented in our software for quantifying two-dimensional images of complex branching forms. The methods are used to compute a series of morphological measurements. We found significant results to distinguish Riccardia species by using the measurements. The methods are also applicable for analyzing other branching organisms. Our software is freely available under the GNU GPL.
KeywordsComplex-shaped branching forms Image analysis Quantitative morphological analysis Morphological variable Liverworts
Quantification of morphological characteristics of biological objects has continuously posed a challenge due to varieties in their morphological changes. The morphological variation offers channels for numerous studies, for example, the comprehension of causes, factors, and directions of biological processes , the influence of environmental changes [2, 3]. Besides, it is used in taxonomy to identify, describe, and classify species or taxa as well as evolutionary systematics study. The growth morphology of many modular organisms such as plants presents a branched and complex-shaped structure. Their growth can be indeterminate , making the quantification and analysis of their form more complicated.
There are three well known morphometric approaches for form analysis: traditional, landmark-based, and outline-based. Landmark-based morphometrics  considers discrete anatomical loci, while outline-based captures outlines of form structures. Both are more suitable for non-modular organisms, but less applicable for the analysis of indeterminate growth forms of modular organisms, which prefers traditional approach by measuring linear distances (width, length), angles, and ratios.
There are several 2D and 3D imaging technologies used to gather quantitative characters related to the growth form. Although advances of 3D imaging techniques can theoretically quantify the characters more accurately, for some plant organisms such as liverworts, which are used in our study, their characteristic features are thin and flat; therefore, 2D imaging techniques are more suitable than 3D which are complicated in terms of procedures, implementation, and executing time. Moreover, in case of field work, it is more practical for 2D image acquisition by using a camera or a microscope.
Methods in morphological analysis of 2D images of the growth forms of branching organisms have been developed in several studies [6, 7]. In these studies the analysis is based on the construction of a 2D morphological skeleton of a branching object in the images. The morphological skeleton of the branching object can be used to measure various biologically relevant characteristics. Many steps in this analysis have been done in a manual way requiring visual interaction in many places. To date, there are open 2D image analysis software that perform on plant organs such as leaves (LeafJ , LAMINA , and leaf processor ). These programs can measure mainly shape, length, width, perimeter, surface of leaves, and leaf venation pattern, but they are not designed to measure some other important morphological traits, which are very essential to our samples, such as branch thickness and branch spacing. Root system architecture (RSA) software (DART , root system analyzer , RootReader2D ) are the most similar to our software. Their algorithms share some similar features such as junction radius, branch length, and branch angle; however, those also lack the measurements of branch thickness and branch spacing.
Riccardia, a plant genus in the liverwort family Aneuraceae, is represented by pinnate to tripinnate thalloid plants creeping or erecting on various substrates (rocks, dead wood, soil), and grows mainly in tropical areas and always in humid climate. Its dimension ranges from some millimeters to a few centimeters. It is the largest genus among the family Aneuraceae, with more than 300 names , which should be a hundred of accepted species after greatly-needed revision . The genus has mystified bryologists for many years due to its polymorphic morphology. For taxonomical studies, the species are still doubtful in taxonomic classification and the morphological variability of Riccardia across its large geographical range has not been extensively studied, in particular, African Riccardia. In the literature, African Riccardia are described mostly by their morphological characters such as axis width, length, and angles [16–23], while other characters can also be expressed, for example, Riccardia amazonica is described as winged (wing is at least 2 rows of unistratose cells at the margin of the thallus) [23, 24] and as not winged [18, 19]. In any case, due to such plastic phenotypes, it is not easy to express their variability.
The aim in this study is to develop a general and semi-automatic software implementing methods to quantitatively measure and analyze morphological characters from a class of 2D image of complex-shaped branching objects stemming from indeterminate growth. The morphological characters are junction thickness, branch thickness, terminal thickness, branch length, branch angle, and terminal spacing. The methods are developed in the context of a review of African (and Indian Ocean) Riccardia which have never really been studied at the continental scale nor in an integrative framework. The morphometrical approach presented here will be used at a larger scale in order to be compared with molecular species delineation .
The experimental images used in this work originate from two sources: (1) artificial images, which are used to systematically test the software (2) images of our samples. Each sample was precisely laid on a glass slide in a droplet of water, and their images were then taken with a Nikon Coolpix P6000 through a binocular microscope.
We used the raster graphics editor, GIMP 2.6.12  and the image processing package based on ImageJ, Fiji , to process original color sample images. The color image is converted to 8-bit grayscale, thereafter thresholding is performed using the Ostu method to obtain a binary image. However, the given threshold value is then adjusted to obtain the best binary image closet to the original image. Therefore, different threshold values are assigned for different images. We use morphological operations to improve the quality of the image by using opening operation to remove some stray foreground pixels in the background and closing operation to fill holes in the foreground.
Skeletonization is the process of reducing an image to its skeleton. By reducing an object to only a skeleton, unimportant features and image noise are filtered out. Additionally, it is easier to determine critical features such as connection points and end points as well as greatly speeding up any subsequent analysis of images. Skeletonization algorithms are generally classified into three categories: (1) distance transforming method, which converts the original image into foreground and background elements, generates a distance map where each element gives the distance to the nearest background element and then detects ridges in the distance map as skeletal points. This method guarantees a central position of the skeleton, but it is sensitive to the noise, and generally doesn’t guarantee the skeleton connectivity [29, 30]. (2) Voronoi-Skeleton method, which calculates the Voronoi diagram generated by the boundary points or pixels. When the number of boundary points goes to infinity, the corresponding diagram converges to the skeleton . It generates a connected skeleton, however it is a time consuming process especially for large objects. Therefore, it is not suitable to be applied complicated images to branching objects used in our study. (3) Thinning methods, which remove the pixels from the object boundary that will not change the topology of the object until obtaining a single-pixel-wide skeleton. Thus the method preserves the topology and connectivity of the skeleton, and guarantees the medial position of the skeleton [32, 33].
For our purpose, we needed a method that guaranteed the connectivity and topology of the skeleton in order to form the skeleton graph; therefore the thinning method was adopted. There are many thinning algorithms available such as the Zhang Suen algorithm , the Rosenfeld algorithm  and the Hilditch algorithm . The skeletonization in this study was based on the thinning algorithm by Zhang and Suen because it is robust, fast, and easy to implement. The algorithm uses a lookup table to repeatedly remove pixels from the edges of objects in a binary image until a single-pixel-wide skeleton remains. After the skeletonization is done, the following significant features are extracted: (1) junction, a point having three or more adjacent points (branches) in a skeleton. (2) Terminal, the endpoint or tip of the skeleton.
Skeleton graph generation
We used morphometric methods to automatically quantify a number of localized morphological variables. These variables are thought to be useful in various applications, for instance, growth study that tells branch splitting rate, environmental influences on growth, and species classification that uses them as continuous characters to differentiate species. The variables are further used to discriminate species among the genus Riccardia as our case study. The measurement results are initially calculated in pixels. A scale tool provided by our software allows the user to define the pixel to other unit scale and all the measurements will be calculated from the scale setting.
Junction thickness (da)
Branch thickness (db)
The thickness of the branch centered on a point along the skeleton after its last found da. The circular disc (Fig. 6b) representing db is created by using the euclidean distance map which calculates the shortest distance from a point along the skeleton to the boundary of the da or the background of the image.
Terminal thickness (dc)
The thickness of the terminal branch centered at the tip of the skeleton. Similar to da, the circular disc (Fig. 6c) representing dc is created by using the euclidean distance map which calculates the shortest distance to the background of the image.
The number of branch pixels along the skeleton and euclidean distance between two successive vertices (Fig. 6d).
The angle between the two vectors originating from a junction in the skeleton and the center of successive db disc (Fig. 6e).
The euclidean distance from a tip of a terminal branch to the nearest tip of another terminal branch (Fig. 6f).
The morphological quantification results obtained by our software are analyzed with analysis of variance (ANOVA) to confirm the significant differences among the four taxonomical groups of Riccardia by taking one morphological variable at a time. Multivariate analysis of variance (MANOVA) assesses the statistical significance of the group differences by considering all of the variables simultaneously. Our analysis goal is to distinguish a group from the four groups by considering the variables; therefore, canonical discriminant analysis (CDA) was applied by finding the combinations of the variables that maximize the discrimination of the predefined groups, testing whether the means of those groups are significantly different, and computing classification rate. Statistical analysis was performed using RStudio Team .
Descriptive statistics of the measured morphological variables according to the four groups of samples
Mean ± SD
N = 37
R. amazonica _SA
N = 26
N = 25
N = 50
0.4 ± 0.09
0.49 ± 0.14
0.31 ± 0.05
0.67 ± 0.06
0.28 ± 0.07
0.34 ± 0.08
0.22 ± 0.04
0.45 ± 0.11
0.18 ± 0.05
0.22 ± 0.07
0.15 ± 0.03
0.29 ± 0.07
1.03 ± 0.25
1.31 ± 0.35
1.47 ± 0.46
1.28 ± 0.28
0.99 ± 0.31
1.33 ± 0.3
1.33 ± 0.43
1.18 ± 0.31
119.02 ± 15.94
118.88 ± 18.02
128.19 ± 14.74
112.69 ± 14.74
From Fig. 7, the distribution of the mean values of each of the six morphological variables among the four groups appears that they differ from one another, except branch length and the branch angle between R. amazonica SA and R. compacta. Also, the result of ANOVA reveals significant differences among the four groups by considering each of the variables (Additional file 1: Table SI4).
p values of six morphological variables for each pair of the four groups using ANOVA
R.a.AF vs R.a.SA
R.a. AF vs R.ob
R.a.AF vs R.co
R.a.SA vs R.ob
R.a.SA vs R.co
R.ob vs R.co
Correlation coefficients between the measured variables and its corresponding p values
Amongst these correlated variables and their corresponding p-values Table 3, we found the four strongest significantly (p < 0.0001) correlated variables among the four groups. The strongest linear correlation (r = 0.96, p < 0.0001) was observed between junction thickness (da) and branch thickness (db). The other two strong correlations were between branch thickness (db) and terminal thickness (dc) (r = 0.89, p < 0.0001) and correlation (r = 0.87, p < 0.0001) between junction thickness (da) and terminal thickness (dc). Additionally, branch length (bl) is also correlated (r = 0.72, p < 0.0001) with branch spacing (bs).
Summary of canonical discriminant functions
Discriminant function coefficients (a) and group means (b)
Discriminant function 1
Discriminant function 2
Discriminant function 3
Discriminant function 1
Discriminant function 2
Discriminant function 3
Figure 9 shows that of the two canonical variables Can1 is able to clearly distinguish between R. compacta and R. obtusa. It can also separate R. amazonica_AF and R. amazonica_SA from R. compacta and R. obtusa with small overlap. However, Can1 cannot be used to separate R. amazonica_AF from R. amazonica_SA. In this case Can2 can be helpful, but with substantial overlap. Therefore, to achieve a good separation of the four groups, it would be best to use both the first and second discriminant functions together, since the first discriminant function can separate R. compacta and R. obtusa very well, and the second discriminant function can separate R. amazonica_AF and R. amazonica_SA.
The classification matrix of the four groups as a result of canonical discriminant analysis
R. amazonica AF
R. amazonica SA
The classification matrix of R. amazonica from Africa and South America as a result of canonical discriminant analysis
R. amazonica AF
R. amazonica SA
R. amazonica AF
R. amazonica SA
We develop a semi-automated software to quantify some important morphological characters of liverworts which represent irregular and complex-shaped branching organisms. The characters are used for the purpose of species discrimination in genus Riccardia.
In our software, we use 2D image analysis techniques to automatically quantify the morphological variables. Most measurements are performed automatically. Some manual operations may still be required, such as removal of spurious branches that are created during the skeletonization due to boundary irregularities on the object in the image and loops in the skeleton which arise from overlapping branches. For these loops, automatic loop breaking is very complicated due to difficulty in deciding which edge in the loop should be deleted. Its complication is varied according to the number of loops which can lead to high possibility to produce false measurement and analysis. The software can report the number of loops as well as their locations, however, user has to decide how to do with the loops, which can be (1) manually removing an edge forming the loop, (2) edit but preserve the main characteristics of original sample image as much as possible by inserting a single pixel-wide gap to separate the overlapping branch or (3) prepare samples without loop. For our experiment, we have done (2) in order to compute the measurement automatically. For some thalli images presenting several overlapping branches, these branches were manually separated into new images without overlapping. Two or three images can be generated from the original and treated separately by the software. The original image with overlapping branches was also measured in order to compare the data.
Comparison of quantitative data using different morphological characters among the three species of African Riccardia
R. amazonica (AF)
Main axis width
Up to 900 µm
Main axis length
Up to 15 mm
Terminal branch length
350–2375 µm (no differences between primary and terminal)
Terminal branch width
Up to 525 µm
Angle between branches
Up to 30°
Up to 20 mm long
Up to 7 mm
Primary branch width
Up to 500 µm
Classification rate from discriminant analysis showed that the species can be discriminated with 70.3% accuracy using the six morphological characteristics. The rate indicates the significance of morphological traits in the discrimination of Riccardia species. This result may confirm the indication that genetical differences could be expressed in the general dimensions of the thalli. The R. amazonica samples collected from South America and Africa were supposed to belong to the same species. With a classical morpho-anatomical revision of R. amazonica, a study of the historical material and recent collections  showed that some doubts remained on the inclusion of South American and African material in the same species. Therefore, we also investigated the two groups based on the morphological measurements. The ANOVA analysis shows a discrimination between the two groups and the CDA result shows samples correctly classified (Table 7) to their original groups were 81%.
We suggest, from our samples, that R. amazonica from South America and Africa show the significant differences in their morphometric features, and we propose the hypothesis that they could belong to different species. This hypothesis of species should be included in the revision of integrative taxonomy, which is the most consensual framework of today taxonomists [41–44]. It is engaged on the genus Riccardia in Africa, including both molecular and morphological analysis . Morphological analysis is probably not as powerful as molecular analysis to delineate species because phenotype can be influenced by both original genotype and environmental conditions. However, this approach can be used as supplementary tool combined with other approach, such as molecular delineation methods (automatic barcode gap discovery (ABGD), generalized mixed yule coalescent (GYMC), haplowebs, see ). In case of congruence of the different results morphometry can support species hypothesis. On the opposite side, if the morphometric results separate samples that are recognized as the same species by other methods, it could suggest some other directions of investigation, for example, among ecological conditions to explain these differences. The morphological approach can also allow clear interspecific variations analysis. However, morphological variation together with molecular analysis and biogeographic studies are the best efficient way to classify species more accurately.
A framework and software for taxonomic study using morphometric approach on 2D image have been presented in this paper. Our results provided evidence that quantitative characteristics determined by image processing and analysis techniques used in our software can be useful for taxonomic differentiation of the genus Riccardia. Also the characteristics are valuable for discriminating same species influenced by surrounding conditions from different geographical locations (R. amazonica collected from South America and Africa). Furthermore, our morphometric software can be applied to quantify branching growth form of other modular organisms.
analysis of variance
automatic barcode gap discovery
canonical discriminant analysis
convention on international trade in endangered species of wild fauna and flora
- GNU GPL:
GNU general public license
generalized mixed yule coalescent
multivariate analysis of variance
root system architecture
PK wrote the software and performed the statistical analysis. CR designed the experiment, obtained and photographed the samples and analyzed the data. FJ contributed to the software and data analysis. JK participated in the experiment design and coordination. All authors participated in the writing, and approved the final manuscript. All authors read and approved the final manscript.
The authors declare that they have no competing interests.
Availability of data and materials
All data supporting the findings of this study are available from the authors upon reasonable request.
Ethics and consent to participate
The samples used in our study have been given permission and consent by the herbaria listed in Additional file 1 (see Table SI1 and Table SI2).
This study is financially supported by Royal Thai Government (Ministry of Science and Technology), Thailand.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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