AgED: Extraction and Evaluation of Elliptic Fourier Descriptors

from Image Data in Phenotype Assessment Applications

orgen Brandt

and Alexander Heyl

Federal Institute for Risk Assessment, Diedersdorfer Weg 1, D-12277 Berlin, Germany

Institute of Biology/Applied Genetics, Dahlem Centre of Plant Sciences (DCPS), Freie Universit

at Berlin,

Albrecht-Thaer-Weg 6, D-14195 Berlin, Germany

Keywords:

Image Analysis, Statistical Evaluation, Feature Extraction, Software, Coleochaete scutata, Fourier Descrip-

tors, Kernel Density Estimation.

Abstract:

In biological experiments, phenotype evaluation is a common challenge. In a wide variety of applications,

the phenotypic features of organisms have to be measured and statistically assessed. This is especially im-

portant as differences between wild-type and mutant or treated and untreated organisms are often very subtle.

Here, we propose a set of digital image transformations that implement preprocessing, feature extraction and

statistical analysis of image data that is typically generated in a biological experiment. Moreover we present

AgED - Analysis given Experimental Data, a software toolkit that facilitates the process of phenotypic feature

evaluation from digital image data in an automatized fashion. Suitable statistical analysis and visualization is

performed and controlled via a Graphical User Interface. Furthermore, the use of open data structures allows

for the convenient reuse of the acquired feature data with miscellaneous data-mining software and scientiﬁc

workﬂow systems. The functionality of this software tool is demonstrated and validated by repeating a phyto-

hormone response experiment carried out on the fresh water alga Coleochaete scutata. The results showed that

the timely and automatic processing of digital image data aides the researcher and rationalizes the formerly

lengthy and, at times, error prone data evaluation in spreadsheet documents. Furthermore, the software toolkit

AgED establishes a comparable evaluation standard and provides ready-to-publish graphic export facilities.

1 INTRODUCTION

Many biological experiments share a common setup.

A condition like a mutation or the exposition to stress

or a chemical or physical condition is assessed for

its effect on the phenotype of an organism. The re-

searcher often assumes that the condition alters the vi-

sual appearance of the organism under investigation.

Such an effect needs to be quantiﬁed and the observed

difference has to be statistically veriﬁed in order to

connect the phenotype to the altered condition. Dig-

ital images form the basic data source for this kind

of experimental setup. In the course of a biological

experiment hundreds or thousands of high resolution

images may be produced. Therefore, efﬁcient and

parallel image processing is mandatory. Furthermore,

the publication of results requires graphic export fa-

cilities. Here, we introduce the software toolkit AgED

that enables a set of methods to quantify and evalu-

ate features from image data in an automatic fashion.

While the automatic evaluation of the image data in

our software is emphasized, the Graphical User Inter-

face (GUI) is designed to empower the user to review

the computation’s results and guide the data process-

ing at any given step. Thus, AgED provides an image

processing pipeline that allows for the answering of

the question, whether or not a condition inﬂuences a

phenotypic trait.

The usefulness of this software is demonstrated by

conducting a phytohormone response experiment in-

volving the fresh-water alga Coleochaete scutata. A

similar experiment has been carried out in the past

(Sprißler, 2007) thus, the obtained results can be com-

pared. The differences to conventional assessment

methods are described and the requirements and lim-

itations of the image source as well as the species un-

der consideration are pointed out.

The software relies on Elliptic Fourier Descriptors

(Kuhl and Giardina, 1982). In the biological con-

text Elliptic Fourier Descriptors (EFDs) have been ap-

proved for cell- and nuclear shapes (Diaz et al., 1989)

as well as for leaf-forms (Pryer and Heam, 2009).

324

Brandt J. and Heyl A..

AgED: Extraction and Evaluation of Elliptic Fourier Descriptors from Image Data in Phenotype Assessment Applications.

DOI: 10.5220/0004249903240327

In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS-2013), pages 324-327

ISBN: 978-989-8565-35-8

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

They have been extensively used for assessing shapes

in plants (Chen and Nelson, 2004; Truong et al., 2005;

Yoshioka et al., 2006) as well as in animals (Laurie

et al., 1997; Costa et al., 2012).

Figure 1: Image processing pipeline. The pipeline is dev-

ided in three major processing steps: In the preprocessing

step the raster image is transformed until only the shape out-

lines remain. In the feature extraction step Elliptic Fourier

Descriptors (EFDs) are calculated. Eventually the probabil-

ity densities are estimated in the evaluation step.

2 METHODS AND ALGORITHMS

In this section we present the algorithms that consti-

tute the image processing pipeline and the subsequent

statistical evaluation. This pipeline can be divided in

three larger blocks. Figure 1 depicts this workﬂow

and summarizes the tasks accomplished in each single

block. The general analysis is based on the EFD mag-

nitudes of all detected shapes in all available images.

Including the Principal Components Analysis (PCA),

the described pipeline consists of well established al-

gorithms that have proven useful to determine phe-

notype shape characteristics and have been applied

in previous software development efforts (Iwata and

Ukai, 2002). In an experimental setup where subtle

tendencies have to be detected from possibly thou-

sands of images, the comparison of probability den-

sities in favor of single realizations is required. This

functionality is provided in the evaluation step of the

pipeline. Here, we describe these processing steps in

detail.

Image Preprocessing. The main task of image pre-

processing is the distinguishing of the specimen’s

shape from the background of the image. First,

color information is disposed of resulting in a

black and white image. Optionally, the image is

scaled down by a factor to a size where it still

contains the important information but needs less

memory and can be processed faster. Further-

more, the image may be inverted, accounting for

the fact whether it is the background or the shapes

that appear bright in the image. Next, a low-pass

ﬁlter is applied in order to reduce noise and to

make the subsequent algorithms more stable. This

step too is optional. The optimal size of the ﬁlter

kernel depends in part on the scale-down factor

in the previous step. Thresholding is performed

subsequently (see Figure 2). For the study, pre-

sented here, we assume that a very small frac-

tion of shape information is outweighed by a large

fraction of background information. Hence, the

mean brightness of the whole image is very close

to the mean brightness of the background, which

is unknown in advance. The optimal threshold

is, therefore, deﬁned as the standard deviation of

the brightness multiplied by a user deﬁned factor.

Subsequently, edge detection is performed by ap-

plying a Sobel operator. Eventually, single shapes

are identiﬁed via Connected Component Labeling

(Samet and Tamminen, 1988).

Feature Extraction. From the labeled edge informa-

tion that results from image preprocessing, the

outlines of the shapes can be extracted (see Fig-

ure 3). Each outline is represented as a list of

complex numbers. From this list the set of EFDs

is calclulated (Kuhl and Giardina, 1982). These

are normalized which yields a scale- and rotation-

independent representation of the shapes under

consideration (Ferson et al., 1985). In addition

it is possible to perform a Principal Components

Analysis (PCA). If EFDs are linearly correlated, a

PCA may reveal a discriminating feature.

Statistical Evaluation. The EFDs and Principal

Components (PCs) taken together represent the

feature set to be assessed. An EFD or PC can

be viewed as a continuous random variable. Dif-

ferent methods are available for deriving a prob-

ability density function (PDF) for a random vari-

able. A type of probability distribution may be

assumed and the parameters for that distribution

may be estimated. A non-parametric approach

would be to estimate histograms. For AgED, how-

ever, we decided to apply the Kernel Density Es-

timation (KDE) method (Parzen, 1962), another

non-parametric approach.

AgED:ExtractionandEvaluationofEllipticFourierDescriptorsfromImageDatainPhenotypeAssessmentApplications

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Figure 2: Cross section of a Coleochaete scutata thallus.

Top: Image part showing a single thallus. The blue line in-

dicates the location of the cross section. Bottom: The orig-

inal value (cyan), the low-pass ﬁltered value (blue) and the

classiﬁcation threshold (green). The Gaussian ﬁlter kernel

had a standard deviation of σ = 100 pixels. The abscissa

represents pixel length units while the ordinate is the value

in standard deviations.

3 RESULT VALIDATION

In this section we validate the results obtained

from the AgED software toolkit by comparing them

with results generated in a manual manner. We

chose to perform an experiment similar to one per-

formed by Sprißlser and collegues (Sprißler, 2007)

in which the effectiveness of the phytohormone 2,4-

Dichlorophenoxyacetic acid (2,4D) was tested. The

experiment founded on the observation that the alga

formed three different shape classes at different ra-

tios. These classes were (i) disc-shaped, (ii) partially

disc-shaped and (iii) unstructured. For the treated and

the control group all specimen where classiﬁed and

counted. A clear increase in the frequence of un-

structured thalli was observed for the group that was

treated with 2,4D in comparison to the control group.

Unfortunately, a direct repetition of the exper-

iment using EFDs cannot be accomplished. This

would require the unique translation of the verbal de-

scription of the shape classes into EFD ranges. Never-

theless, we can expect that a difference in shape class

frequency also leads to a difference in the EFD distri-

butions. Furthermore, from the deﬁnition of EFDs we

would expect an unstructured thallus to yield stronger

magnitudes in the higher frequencies of the spectrum

than a round one (see Figure 4). In this way we can

compare the results from the original experiment and

our repetition applying EFDs.

The comparison of EFD distributions for the 2,4D

treated group and the control group show that EFDs

reﬂect this chemical condition. Higher frequency

magnitudes were larger in tendency for 2,4D treated

thalli than for the control group. Thus, the phytohor-

mone effect can be effectively assessed automatically

using the AgED software toolkit.

It turned out that the input images that were con-

sidered in this experiment were particularly challeng-

ing in the way that: (i) Nonuniform illumination lev-

els between and in images, (ii) the presence of edges

and scratches associated to the carrier material of the

specimen (iii) short-comings of some optic devices

such as blur or poor magniﬁcation had to be dealt with

in a set of 600 images. Herein, the AgED software

proved robust againt the aforementioned difﬁculties.

However, some false positives had to be removed by

hand. This removing could easily be accomplished

via the GUI by unchecking the falsely identiﬁed thal-

lus shapes.

Other automatic image analysis approaches con-

cerned with Coleochaete scutata (Dupuy et al., 2010)

addressed its growth over time. The methods pre-

sented here, in contrast, identify general traits that can

be assessed one-shot and without the need to observe

specimen over time.

4 THE SOFTWARE TOOLKIT

The AgED software toolkit applies the described im-

age processing pipeline (see Section 2) to a set of la-

beled images. It requires the presence of a Java Vir-

tual Machine (JVM) and an installation of the Java

Advanced Imaging (JAI) library. It provides a Graph-

ical User Interface (GUI) that lets the user choose ap-

propriate parameters for the particular problem and

image collection at hand. The software, furthermore,

provides a way to organize the associated images, la-

bels, extracted shapes and visualized results. It gives

the user the possibility to take control over each step

in the processing. This way the user can verify any

given intermediate result in the pipeline and compen-

sate for the challenges his unique experimental setup

may pose. The so generated feature set can be stored

in an SQLite database instance that allows for the fur-

ther processing of the data in general purpose scien-

tiﬁc software or scientiﬁc workﬂow systems (Deel-

man et al., 2008).

Figure 3: Examples for thallus shapes extracted with the de-

lineated preprocessing scheme. The regularity of the shapes

was measured by calculating the Elliptic Fourier Descrip-

tors (EFD).

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AgED provides three different views on the image

analysis pipeline: (i) The ﬁle system view lets the user

browse images in his ﬁle system. Furthermore, he can

label each image ﬁle with a class label. (ii) The shape

view allows for the inspection of each single shape

and associated EFDs that have been extracted from

the previously labeled image set. Note that one image

can contain several shapes. Also, the user can select

and deselect single shapes to be excluded from evalu-

ation. False positives and outliers can be removed this

way. (iii) In the evaluation view the probability den-

sities that have been calculated from the EFDs and its

Principal Components can be examined. In this view

the user can compare results and export the ﬁgures as

image ﬁles.

5 CONCLUSIONS

The results of this study showed that the automatic

and timely processing of high-resolution digital im-

age data for biological phenotype assessment appli-

cations is feasible. The AgED software toolkit fa-

cilitates and guides the preprocessing and allows the

browsing of the evaluation results. The applicabil-

ity of the AgED software tool has been exempliﬁed

and validated on a real-world problem, that illustrates

some pitfalls diverse in severity. We have showed that

the AgED software deals effectively with these prob-

lems. While the presented image processing pipeline

readily scales to the automatic processing of a few

thousand images, the data evaluation step neverthe-

less demands some user interaction. We showed that

AgED reduces the time needed from the experimen-

tal setup to the deduction of the experiment’s result.

Furthermore, the extracted feature set as well as the

evaluation results are stored in a format that can be

adopted by other statistical software systems.

The software AgED can be downloaded from

http://sourceforge.net/projects/aged/. This repository

also comes with detailed instructions and examples.

Figure 4: Three thalli of Coleochaete scutata and their out-

lines (left). The according Fourier spectrum of these shapes

(right). The spectrum is displayed as fraction of the DC-

component. Irregular shapes convey larger magnitudes in

the Fourier spectrum than circular shapes.

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