Evaluating Retinal Blood Vessels' Abnormal Tortuosity in Digital Image Fundus Submitted in ...

180 Pages·2016·3.1 MB·English

Evaluating Retinal Blood Vessels’
Abnormal Tortuosity in Digital Image
Fundus
Submitted in fulfilment of the requirements for the
Degree of MSc by research
School of Computer Science
University Of Lincoln
Mowda Abdalla
June 30, 2016
Acknowledgements
This project would not have been possible without the help and support of many
people. My greatest gratitude goes to my supervisors Dr. Bashir Al-Diri, and Prof.
Andrew Hunter who were abundantly helpful and provided invaluable support, help
and guidance. I am grateful to Dr. Majed Habeeb, Dr. Michelle Teoailing, Dr.
Bakhit Digry, Dr. Toke Bek and Dr. Areti Triantafyllou for their valuable help with
the new tortuosity datasets and the manual grading. I would also like to thank my
colleagues in the group of retinal images computing and understanding for the great
help and support throughout the project.
A special gratitude goes to Mr. Adrian Turner for his invaluable help
and support. Deepest gratitude are also for my parents, Ali Shammar and Hawa
Suliman, and to my husband who believed in me and supported me all the way.
Special thanks also go to my lovely daughters Aya and Dania. Finally, I would like
to convey warm thanks to all the staff and lecturers of University of Lincoln and
especially the group of postgraduate students of the School of Computer Science.
2
Abstract
Abnormaltortuosityofretinalbloodvesselsisoneoftheearlyindicatorsofanumber
of vascular diseases. Therefore early detection and evaluation of this phenomenon
can provide a window for early diagnosis and treatment. Currently clinicians rely on
a qualitative gross scale to estimate the degree of vessel tortuosity. There have been
many attempts to develop an accurate automated measure of tortuosity, yet it seems
that none of these measures has gained universal acceptance. This can be attributed
tothefactthatdescriptionsanddefinitionsofretinalvesseltortuosityareambiguous
and non-standard. In addition unified public datasets for different disease are not
regularly available. I have propose a tortuosity evaluation framework in order to
quantifythetortuosityofarteriesandveinsintwodimensionalcolourfundusimages.
The quantification methods within the framework include retinal vessel morphology
analysis based on the measurements of 66 features of blood vessels. These features
are grouped as follows: 1) Structural properties 2) Distance approach features 3)
Curvature approach features 4) Combined approach features 5) Signal approach
features. The features numbered 1 to 4 above are derived from literature. Item
number five are new features which I have proposed and developed in this thesis.
These features have been evaluated using a manually graded retinal tortu-
osity dataset as controlled set. I have also built three tortuosity datasets, each of
which contains two manual gradings. These datasets are: 1) A general tortuosity
dataset 2) A diabetic retinopathy dataset 3) A hypertensive retinopathy dataset. In
addition, I have investigated the differences in tortuosity patterns in hypertensive
and diabetic retinopathy. New pathology based datasets were used in this investi-
gation. These are the major contributions of this thesis.
3
Attestation
I understand the nature of plagiarism, and I am aware of University of Lincoln’s
policy on this. I certify that this study reports original work by myself during my
university project except for the following:
* The retina image shown in figure 2.2 was taken from
(http://www.stlukeseye.com/anatomy/retina.html).
* Images of the vision and parts of the eye in Section 2.5 were taken from
(http://www.allaboutvision.com/resources/anatomy.html)
and (http://www.tedmontgomery.com/the-eye/index.html)
* The tortuous vein image in Section 2.6, Figure 2.5 was taken from
(http://www.merckmanuals.com/home/
heart and blood vessel disorders/venous disorders/varicose veins.html)
Signature: Date:
4
Glossary
ANN Artificial Neural Network. 57, 59
AOC Arc Over Chord ratio. 45
Backpropagation backwards propagation of error. 119
CT Computed Tomography. 25
DTFT Discrete Fourier Transform of aperiodic signals. 55
FFT Fast Fourier transform. 52
FSHD facioScapuloHumeral Muscular Dystrophy. 17
FT Fourier Transform. 21, 52, 55, 56
ICC Intraclass Correlation Coefficient. 22
ICM Inflection Count Metric. 46
L-O-Out Leave-One-Out cross validation. 120
MDAC Mean direction angle change. 98
ML Machine Learning. 58
MRI Magnetic Resonance Imaging. 25
RF Random Forests. 57
RLV Relative Length Variation. 45
ROP Retinopathy of Prematurity. 17
RVTDS Retinal Vessel Tortuosity Dataset. 21, 22, 58, 66, 91, 100, 150
5
SOAM Sum Of Angles Measure. 46
X-ray Energetic High-Frequency Electromagnetic Radiation. 25
6
Contents
1 Introduction 17
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.2 Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.4 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.4.1 Aims and objectives . . . . . . . . . . . . . . . . . . . . . . . 20
1.5 Investigations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.6 Thesis overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2 Literature review 24
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.2 Digital image processing and analysis . . . . . . . . . . . . . . . . . . 25
2.3 Medical image processing and analysis . . . . . . . . . . . . . . . . . 26
2.3.1 Retinal image analysis . . . . . . . . . . . . . . . . . . . . . . 26
2.4 Blood vessel’s tortuosity . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.5 The retina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.5.1 Retinal blood vessels . . . . . . . . . . . . . . . . . . . . . . . 30
2.5.2 Retinal fundus . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.6 Retinal vessel’s tortuosity . . . . . . . . . . . . . . . . . . . . . . . . 33
2.7 Previous work on grading retinal vessels tortuosity . . . . . . . . . . . 36
2.7.1 Grading tortousity in other disciplines . . . . . . . . . . . . . 43
2.7.2 Tortuosity measure properties . . . . . . . . . . . . . . . . . . 44
2.8 Existing tortuosity evaluating features . . . . . . . . . . . . . . . . . 44
2.8.1 Distance approach . . . . . . . . . . . . . . . . . . . . . . . . 44
7
2.8.2 Curvature approach . . . . . . . . . . . . . . . . . . . . . . . . 47
2.8.3 Combined methods . . . . . . . . . . . . . . . . . . . . . . . . 52
2.9 Review of Fourier transform analysis . . . . . . . . . . . . . . . . . . 55
2.10 Data analysis methods . . . . . . . . . . . . . . . . . . . . . . . . . . 57
2.11 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3 Building tortuosity datasets 64
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.2 Tortuosity datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.3 The retinal vessel tortuosity dataset . . . . . . . . . . . . . . . . . . . 66
3.4 Proposed dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.4.1 Image dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.4.2 Graders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.4.3 Grading systems . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.5 Manual grading analysis . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.5.1 Inter observers agreement . . . . . . . . . . . . . . . . . . . . 72
3.5.2 Ordered and classified tortuosity dataset . . . . . . . . . . . . 75
3.5.3 Diabetic and hypertension datasets . . . . . . . . . . . . . . . 76
3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4 My framework 80
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.2.1 My new features . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.2.2 Features derived from the literature . . . . . . . . . . . . . . . 89
4.2.3 My proposed framework . . . . . . . . . . . . . . . . . . . . . 90
4.2.4 Framework features’ grouping and implementation . . . . . . . 91
4.3 Data analysis using the retinal vessel tortuosity dataset . . . . . . . . 100
4.3.1 Descriptive statistical analysis . . . . . . . . . . . . . . . . . . 101
4.3.2 Correlation analysis . . . . . . . . . . . . . . . . . . . . . . . . 106
4.3.3 Regression analysis . . . . . . . . . . . . . . . . . . . . . . . . 114
4.3.4 Machine learning analysis . . . . . . . . . . . . . . . . . . . . 117
4.3.5 Feature selection . . . . . . . . . . . . . . . . . . . . . . . . . 127
8
4.4 Data analysis using the new tortuosity dataset . . . . . . . . . . . . . 131
4.4.1 Correlation analysis . . . . . . . . . . . . . . . . . . . . . . . . 131
4.4.2 Artificial neural network analysis . . . . . . . . . . . . . . . . 134
4.4.3 Classifiers performances . . . . . . . . . . . . . . . . . . . . . 136
4.5 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 137
4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
5 Investigating tortuosity differences in hypertensive and diabetic
retinopathy 141
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
5.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
5.2.1 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
5.2.2 Features identification . . . . . . . . . . . . . . . . . . . . . . 142
5.2.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
5.3.1 Sample characteristics . . . . . . . . . . . . . . . . . . . . . . 143
5.3.2 All segments grouped by disease . . . . . . . . . . . . . . . . . 144
5.3.3 All segments grouped by disease then by vessel type . . . . . . 145
5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
6 Discussion, contribution and future work 150
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
6.1.1 Tortuosity evaluation framework . . . . . . . . . . . . . . . . . 150
6.1.2 Tortuosity differences in hypertensive and diabetic retinopathy 151
6.1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
6.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
6.2.1 Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
6.2.2 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
6.2.3 Tortuosity differences in hypertension and diabetic retinopathy153
6.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
Appendices 165
9
Appendices 165
.1 Appendix A:Review paper . . . . . . . . . . . . . . . . . . . . . . . . 166
.2 Appendix B: Implementation . . . . . . . . . . . . . . . . . . . . . . 174
.2.1 Distance approach features . . . . . . . . . . . . . . . . . . . . 174
.2.2 Curvature approach features . . . . . . . . . . . . . . . . . . . 175
.2.3 Combined approach features . . . . . . . . . . . . . . . . . . . 178
.2.4 My combined approach features . . . . . . . . . . . . . . . . . 178
.3 Appendix C: Statistical analysis . . . . . . . . . . . . . . . . . . . . . 179
.3.1 Regression analysis . . . . . . . . . . . . . . . . . . . . . . . . 179
.3.2 ANN learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
.3.3 Feature selection . . . . . . . . . . . . . . . . . . . . . . . . . 179
10