Untreated glaucoma leads to permanent damage of the optic nerve and resultant visual field loss, which can
progress to blindness. As glaucoma often produces additional pathological cupping of the optic disc (OD), cupdisc-
ratio is one measure that is widely used for glaucoma diagnosis. This paper presents an OD localization
method that automatically segments the OD and so can be applied for the cup-disc-ratio based glaucoma diagnosis.
The proposed OD segmentation method is based on the observations that the OD is normally much
brighter and at the same time have a smoother texture characteristics compared with other regions within retinal
images. Given a retinal image we first capture the ODs smooth texture characteristic by a contrast image that
is constructed based on the local maximum and minimum pixel lightness within a small neighborhood window.
The centre of the OD can then be determined according to the density of the candidate OD pixels that are
detected by retinal image pixels of the lowest contrast. After that, an OD region is approximately determined by
a pair of morphological operations and the OD boundary is finally determined by an ellipse that is fitted by the
convex hull of the detected OD region. Experiments over 71 retinal images of different qualities show that the
OD region overlapping reaches up to 90.37% according to the OD boundary ellipses determined by our proposed
method and the one manually plotted by an ophthalmologist.
Glaucoma is a leading cause of blindness. The presence and extent of progression of glaucoma can be determined if the
optic cup can be accurately segmented from retinal images. In this paper, we present a framework which improves the
detection of the optic cup. First, a region of interest is obtained from the retinal fundus image, and a pallor-based
preliminary cup contour estimate is determined. Patches are then extracted from the ROI along this contour. To improve
the usability of the patches, adaptive methods are introduced to ensure the patches are within the optic disc and to
minimize redundant information. The patches are then analyzed for vessels by an edge transform which generates pixel
segments of likely vessel candidates. Wavelet, color and gradient information are used as input features for a SVM
model to classify the candidates as vessel or non-vessel. Subsequently, a rigourous non-parametric method is adopted in
which a bi-stage multi-resolution approach is used to probe and localize the location of kinks along the vessels. Finally,
contenxtual information is used to fuse pallor and kink information to obtain an enhanced optic cup segmentation. Using
a batch of 21 images obtained from the Singapore Eye Research Institute, the new method results in a 12.64% reduction
in the average overlap error against a pallor only cup, indicating viable improvements in the segmentation and supporting
the use of kinks for optic cup detection.
Pathological myopia is the seventh leading cause of blindness. We introduce a framework based on PAMELA
(PAthological Myopia dEtection through peripapilLary Atrophy) for the detection of pathological myopia from fundus
images. The framework consists of a pre-processing stage which extracts a region of interest centered on the optic disc.
Subsequently, three analysis modules focus on detecting specific visual indicators. The optic disc tilt ratio module gives
a measure of the axial elongation of the eye through inference from the deformation of the optic disc. In the texturebased
ROI assessment module, contextual knowledge is used to demarcate the ROI into four distinct, clinically-relevant
zones in which information from an entropy transform of the ROI is analyzed and metrics generated. In particular, the
preferential appearance of peripapillary atrophy (PPA) in the temporal zone compared to the nasal zone is utilized by
calculating ratios of the metrics. The PPA detection module obtains an outer boundary through a level-set method, and
subtracts this region against the optic disc boundary. Temporal and nasal zones are obtained from the remnants to
generate associated hue and color values. The outputs of the three modules are used as in a SVM model to determine the
presence of pathological myopia in a retinal fundus image. Using images from the Singapore Eye Research Institute, the
proposed framework reported an optimized accuracy of 90% and a sensitivity and specificity of 0.85 and 0.95
respectively, indicating promise for the use of the proposed system as a screening tool for pathological myopia.
Retinal image analysis is used by clinicians to diagnose and identify, if any, pathologies present in a patient's eye. The
developments and applications of computer-aided diagnosis (CAD) systems in medical imaging have been rapidly
increasing over the years. In this paper, we propose a system to classify left and right eye retinal images automatically.
This paper describes our two-pronged approach to classify left and right retinal images by using the position of the
central retinal vessel within the optic disc, and by the location of the macula with respect to the optic nerve head. We
present a framework to automatically identify the locations of the key anatomical structures of the eye- macula, optic
disc, central retinal vessels within the optic disc and the ISNT regions. A SVM model for left and right eye retinal image
classification is trained based on the features from the detection and segmentation. An advantage of this is that other
image processing algorithms can be focused on regions where diseases or pathologies and more likely to occur, thereby
increasing the efficiency and accuracy of the retinal CAD system/pathology detection.
We have tested our system on 102 retinal images, consisting of 51 left and right images each and achieved and accuracy
of 94.1176%. The high experimental accuracy and robustness of this system demonstrates that there is potential for this
system to be integrated and applied with other retinal CAD system, such as ARGALI, for a priori information in
automatic mass screening and diagnosis of retinal diseases.
Glaucoma is an irreversible ocular disease leading to permanent blindness. However, early detection can be effective in
slowing or halting the progression of the disease. Physiologically, glaucoma progression is quantified by increased
excavation of the optic cup. This progression can be quantified in retinal fundus images via the optic cup to disc ratio
(CDR), since in increased glaucomatous neuropathy, the relative size of the optic cup to the optic disc is increased. The
ARGALI framework constitutes of various segmentation approaches employing level set, color intensity thresholds and
ellipse fitting for the extraction of the optic cup and disc from retinal images as preliminary steps. Following this,
different combinations of the obtained results are then utilized to calculate the corresponding CDR values. The
individual results are subsequently fused using a neural network. The learning function of the neural network is trained
with a set of 100 retinal images For testing, a separate set 40 images is then used to compare the obtained CDR against a
clinically graded CDR, and it is shown that the neural network-based result performs better than the individual
components, with 96% of the results within intra-observer variability. The results indicate good promise for the further
development of ARGALI as a tool for the early detection of glaucoma.