Abstract

The accurate automated extraction of arterial and venous (AV) trees in fundus images
subserves investigation into the correlation of global features of the retinal vasculature
with retinal abnormalities. The accurate extraction of AV trees also provides
the opportunity to analyse the physiology and hemodynamic of blood flow in retinal
vessel trees. A number of common diseases, including Diabetic Retinopathy, Cardiovascular
and Cerebrovascular diseases, directly affect the morphology of the retinal
vasculature. Early detection of these pathologies may prevent vision loss and reduce
the risk of other life-threatening diseases.
Automated extraction of AV trees requires complete segmentation and accurate
classification of retinal vessels. Unfortunately, the available segmentation techniques
are susceptible to a number of complications including vessel contrast, fuzzy edges,
variable image quality, media opacities, and vessel overlaps. Due to these sources of
errors, the available segmentation techniques produce partially segmented vascular
networks. Thus, extracting AV trees by accurately connecting and classifying the
disconnected segments is extremely complex.
This thesis provides a novel graph-based technique for accurate extraction of AV
trees from a network of disconnected and unclassified vessel segments in fundus
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images. The proposed technique performs three major tasks: junction identification,
local configuration, and global configuration.
A probabilistic approach is adopted that rigorously identifies junctions by examining
the mutual associations of segment ends. These associations are determined by
dynamically specifying regions at both ends of all segments. A supervised Naïve
Bayes inference model is developed that estimates the probability of each possible
configuration at a junction. The system enumerates all possible configurations and
estimates posterior probability of each configuration. The likelihood function estimates
the conditional probability of the configuration using the statistical parameters
of distribution of colour and geometrical features of joints. The parameters of feature
distributions and priors of configuration are obtained through supervised learning
phases. A second Naïve Bayes classifier estimates class probabilities of each vessel
segment utilizing colour and spatial properties of segments.
The global configuration works by translating the segment network into an STgraph
(a specialized form of dependency graph) representing the segments and their
possible connective associations. The unary and pairwise potentials for ST-graph
are estimated using the class and configuration probabilities obtained earlier. This
translates the classification and configuration problems into a general binary labelling
graph problem. The ST-graph is interpreted as a flow network for energy minimization
a minimum ST-graph cut is obtained using the Ford-Fulkerson algorithm, from
which the estimated AV trees are extracted.
The performance is evaluated by implementing the system on test images of
DRIVE dataset and comparing the obtained results with the ground truth data. The
ground truth data is obtained by establishing a new dataset for DRIVE images with
manually classified vessels. The system outperformed benchmark methods and
produced excellent results.