Extracting root system architecture from X-ray micro computed tomography images using visual

251 Pages·2016·24.11 MB·English

Mairhofer, Stefan (2014) Extracting root system
architecture from X-ray micro computed tomography
images using visual tracking. PhD thesis, University of
Nottingham.
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Extracting
Root System Architecture from
X-ray Micro Computed Tomography
Images using Visual Tracking
Stefan Mairhofer, M.Sc.
Thesis submitted to the University of Nottingham
for the degree of Doctor of Philosophy
December 2014
i
Abstract
X-raymicrocomputedtomography(µCT)isincreasinglyappliedinplantbi-
ologyasanimagingsystemthatisvaluableforthestudyofrootdevelopment
in soil, since it allows the three-dimensional and non-destructive visualisa-
tion of plant root systems. Variations in the X-ray attenuation values of
root material and the overlap in measured intensity values between roots
and soil caused by water and organic matter represent major challenges to
the extraction of root system architecture. We propose a novel technique to
recover root system information from X-ray CT data, using a strategy based
on a visual tracking framework embedding a modi(cid:28)ed level set method that
is evolved using the Jensen-Shannon divergence. The model-guided search
arising from the visual tracking approach makes the method less sensitive
to the natural ambiguity of X-ray attenuation values in the image data and
thus allows a better extraction of the root system. The method is extended
by mechanisms that account for plagiatropic response in roots as well as col-
lision between root objects originating from di(cid:27)erent plants that are grown
and interact within the same soil environment. Experimental results on
monocot and dicot plants, grown in di(cid:27)erent soil textural types, show the
ability of successfully extracting root system information. Various global
root system traits are measured from the extracted data and compared to
results obtained with alternative methods.
ii
iii
Acknowledgements
I would like to express my deepest gratitude to my supervisors Professor
Tony Pridmore, Professor Malcolm Bennett and Professor Sacha Mooney,
for all the support and intellectual guidance throughout those years, which
has made this academic journey and research possible. Their knowledge and
expertise in their di(cid:27)erent (cid:28)elds has been a unique and invaluable contribu-
tion in the realisation of this interdisciplinary project and a constant source
ofinspiration. Iamespeciallygratefulforallthetrustandcon(cid:28)denceplaced
in me that has motivated and allowed me to explore my own ideas. I could
not imagine having had better mentors for my PhD study. I have been truly
privileged to have had the opportunity to be part of such an excellent group.
I am very grateful to Professor Ian Dryden. His lectures and pleasant
discussions we had on various aspects of statistics have been very helpful
and, although he might not be aware, have played an important role in the
presented and (cid:28)nal outcome of this work.
Much appreciation goes to Craig Sturrock for his assistance and patience
inhelpingmewithoperatingtheX-raymicrocomputedtomographyscanner
and for his e(cid:27)ort and commitment to provide high quality image data. I am
very thankful to Susan Zappala, Saoirse Tracy and Jonathan Helliwell, who
helped and taught me how to grow plants the right way and without whom
my plants would have never lived up to the stage of scanning.
I am thankful to my colleagues James Johnson, Tuan Nguyen and Haris
KhanwithwhomIhadcountlessstimulatingandinterestingdiscussionsover
the past years on various approaches and ideas in image analysis and visual
object tracking.
Special thanks go to my family, Norbert, Renate and Antonia, for the
emotionalsupportandencouragementthroughouttheprocessandwhonever
stopped believing in me. I am particularly thankful to On for the wonderful
and memorable time we shared together, for all the comfort given and for
being always by my side during this long and incredible journey. To them I
dedicate my thesis.
This research would not have been possible without the (cid:28)nancial sup-
portIreceivedfromtheUniversityofNottinghamInterdisciplinaryDoctoral
Training Centre (IDTC) in Integrative Biology.
iv
Contents v
Contents
1 Introduction 1
1.1 Motivation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Methods and techniques used in plant root studies 7
2.1 Plant growth systems facilitating root observation . . . . . . . 10
2.2 Principles of computed tomography . . . . . . . . . . . . . . . 14
2.2.1 X-ray (micro) computed tomography . . . . . . . . . . 15
2.2.2 CT image reconstruction techniques . . . . . . . . . . 19
2.2.3 Noise characteristics and quality of CT image data . . 24
2.3 Image analysis as a tool to aid in the extraction of plant roots 27
2.3.1 Extraction techniques for medical X-ray CT . . . . . . 28
2.3.2 Extraction of arterial structures . . . . . . . . . . . . . 30
2.3.3 Root extraction methods in 2D . . . . . . . . . . . . . 33
2.3.4 Root extraction methods in 3D . . . . . . . . . . . . . 38
2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3 Extraction of plant roots grown in soil 49
3.1 A visual tracking approach. . . . . . . . . . . . . . . . . . . . 50
3.2 The level set method . . . . . . . . . . . . . . . . . . . . . . . 53
3.3 Jensen-Shannon divergence . . . . . . . . . . . . . . . . . . . 60
3.4 Fourier shape descriptors . . . . . . . . . . . . . . . . . . . . . 62
3.5 A novel level set method for visual tracking . . . . . . . . . . 65
3.6 Extraction of plant root systems from X-ray µCT images . . . 70
3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
vi Contents
4 Extraction of plagiotropic root systems 79
4.1 Plant roots and plagiotropism . . . . . . . . . . . . . . . . . . 80
4.2 Recovering upwards oriented roots . . . . . . . . . . . . . . . 82
4.3 Extraction of plant root systems with plagiotropic roots . . . 90
4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
5 Visualisation and characterisation of root system architec-
tures 95
5.1 Three-dimensional volume rendering . . . . . . . . . . . . . . 95
5.1.1 Marching cubes . . . . . . . . . . . . . . . . . . . . . . 96
5.1.2 Volume ray-casting . . . . . . . . . . . . . . . . . . . . 98
5.2 Quanti(cid:28)cation of root system traits . . . . . . . . . . . . . . . 100
5.2.1 Traits de(cid:28)ning root system characteristics . . . . . . . 101
5.2.2 Measuring root system traits . . . . . . . . . . . . . . 104
5.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
6 Evaluation of the root extraction technique 107
6.1 Arti(cid:28)cially generated data . . . . . . . . . . . . . . . . . . . . 108
6.1.1 Generating arti(cid:28)cial test images . . . . . . . . . . . . . 108
6.1.2 Extracting arti(cid:28)cial root objects . . . . . . . . . . . . 111
6.1.3 Results and discussions . . . . . . . . . . . . . . . . . 114
6.2 Root segments buried in soil . . . . . . . . . . . . . . . . . . . 122
6.2.1 Extracting root segments . . . . . . . . . . . . . . . . 122
6.2.2 Results and discussions . . . . . . . . . . . . . . . . . 123
6.3 Plant root systems washed free from soil . . . . . . . . . . . . 127
6.3.1 Extracting plant root systems . . . . . . . . . . . . . . 127
6.3.2 Results and discussions . . . . . . . . . . . . . . . . . 128
6.4 Comparison to other extraction techniques . . . . . . . . . . . 134
6.4.1 Details of the extraction techniques . . . . . . . . . . . 135
6.4.2 Results and discussions . . . . . . . . . . . . . . . . . 136
6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
7 Extraction of multiple interacting root systems 143
7.1 Multiple level sets . . . . . . . . . . . . . . . . . . . . . . . . 144
7.2 Iterative closest point . . . . . . . . . . . . . . . . . . . . . . 147
7.3 Collision of target objects . . . . . . . . . . . . . . . . . . . . 149
7.4 Extracting data with colliding objects . . . . . . . . . . . . . 153
Contents vii
7.4.1 Arti(cid:28)cially generated data . . . . . . . . . . . . . . . . 155
7.4.2 X-ray µCT acquired data of plants . . . . . . . . . . . 155
7.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
8 Conclusions and future work 159
8.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
8.2 Alternative applications . . . . . . . . . . . . . . . . . . . . . 161
8.3 Future work and perspectives . . . . . . . . . . . . . . . . . . 163
Appendices 167
A Root Segments 169
B Plant Root Systems 175
viii Contents