Research into automatic retina image analysis has become increasingly important,
not just in ophthalmology but also in other clinical specialities such as cardiology
and neurology. In the retina, blood vessels can be directly visualised non-invasively
in-vivo, and hence it serves as a "window" to cardiovascular and neurovascular
complications. Biomarker research, i.e. investigating associations between the
morphology of the retinal vasculature (as a means of revealing microvascular health
or disease) and particular conditions affecting the body or brain could play an
important role in detecting disease early enough to impact on patient treatment and
care. A fundamental requirement of biomarker research is access to large datasets
to achieve sufficient power and significance when ascertaining associations between
retinal measures and clinical characterisation of disease.
Crucially, the vascular changes that appear can affect arteries and veins
differently. An essential part of automatic systems for retinal morphology
quantification and biomarker extraction is, therefore, a computational method for
classifying vessels into arteries and veins. Artery-vein classification enables the
efficient extraction of biomarkers such as the Arteriolar to Venular Ratio, which is
a well-established predictor of stroke and other cardiovascular events. While structural
parameters of the retinal vasculature such as vessels calibre, branching angle, and
tortuosity may individually convey some information regarding specific aspects of
the health of the retinal vascular network, they do not convey a global summary of
the branching pattern and its state or condition. The retinal vascular tree can be
considered a fractal structure as it has a branching pattern that exhibits the property
of self-similarity. Fractal analysis, therefore, provides an additional means for the
quantitative study of changes to the retinal vascular network and may be of use in
detecting abnormalities related to retinopathy and systemic diseases.
In this thesis, new developments to fully automated retinal vessel classification
and fractal analysis were explored in order to extract potential biomarkers. These novel
processes were tested and validated on several datasets of retinal images acquired with
fundus cameras.
The major contributions of this thesis include: 1) developing a fully automated
retinal blood vessel classification technique, 2) developing a fractal analysis technique
that quantifies regional as well as global branching complexity, 3) validating the
methods using multiple datasets, and 4) applying the proposed methods in multiple
retinal vasculature analysis studies
