This paper deals with automatic grading of nuclear cataract (NC) from slit-lamp images in order to reduce the efforts in traditional manual grading. Existing works on this topic have mostly used brightness and color of the eye lens for the task but not the visibility of lens parts. The main contribution of this paper is in utilizing the visibility cue by proposing gray level image gradient-based features for automatic grading of NC. Gradients are important for the task because in a healthy eye, clear visibility of lens parts leads to distinct edges in the lens region, but these edges fade as severity of cataract increases. Experiments performed on a large dataset of over 5000 slit-lamp images reveal that the proposed features perform better than the state-of-the-art features in terms of both speed and accuracy. Moreover, fusion of the proposed features with the prior ones gives results better than any of the two used alone.
The optic disk localization plays an important role in developing computer-aided diagnosis (CAD) systems for ocular
diseases such as glaucoma, diabetic retinopathy and age-related macula degeneration. In this paper, we propose an
intelligent fusion of methods for the localization of the optic disk in retinal fundus images. Three different approaches
are developed to detect the location of the optic disk separately. The first method is the maximum vessel crossing
method, which finds the region with the most number of blood vessel crossing points. The second one is the multichannel thresholding method, targeting the area with the highest intensity. The final method searches the vertical and horizontal region-of-interest separately on the basis of blood vessel structure and neighborhood entropy profile. Finally, these three methods are combined using an intelligent fusion method to improve the overall accuracy. The proposed algorithm was tested on the STARE database and the ORIGAlight database, each consisting of images with various pathologies. The preliminary result on the STARE database can achieve 81.5%, while a higher result of 99% can be obtained for the ORIGAlight database. The proposed method outperforms each individual approach and state-of-the-art method which utilizes an intensity-based approach. The result demonstrates a high potential for this method to be used in retinal CAD systems.
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.
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.
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.
The accurate localization of the optic cup in retinal images is important to assess the cup to disc ratio (CDR) for
glaucoma screening and management. Glaucoma is physiologically assessed by the increased excavation of the optic cup
within the optic nerve head, also known as the optic disc. The CDR is thus an important indicator of risk and severity of
glaucoma. In this paper, we propose a method of determining the cup boundary using non-stereographic retinal images
by the automatic detection of a morphological feature within the optic disc known as kinks. Kinks are defined as the
bendings of small vessels as they traverse from the disc to the cup, providing physiological validation for the cup
boundary. To detect kinks, localized patches are first generated from a preliminary cup boundary obtained via level set.
Features obtained using edge detection and wavelet transform are combined using a statistical approach rule to identify
likely vessel edges. The kinks are then obtained automatically by analyzing the detected vessel edges for angular
changes, and these kinks are subsequently used to obtain the cup boundary. A set of retinal images from the Singapore
Eye Research Institute was obtained to assess the performance of the method, with each image being clinically graded
for the CDR. From experiments, when kinks were used, the error on the CDR was reduced to less than 0.1 CDR units
relative to the clinical CDR, which is within the intra-observer variability of 0.2 CDR units.
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.
Conventional wireless communications make use of radio frequency. However, congestion and regulation of the spectrum have led to a search for alternatives. White-light-emitting diodes (WLEDs) can be modulated similarly to communications-wavelength infrared-light-emitting diodes. For WLEDs to be used for both wireless communication or an illumination, they have to be arranged in a manner that achieves a minimum signal power. To achieve this, an optimization routine has been created that optimizes the angular arrangement of the WLEDs to meet a predetermined illumination level. The irradiation profiles of the WLEDs are modeled for arbitrary tilt angles using a combination of theoretical expressions and experimental data. Lamps were created with 120 WLEDs each and implemented using the optimized angles. To demonstrate the use of the WLED wireless system in the transmission of audio signals, driving circuits were designed and implemented. The complete system was tested, and it is shown that the illumination levels meet the minimum required level. Characterization of the received signals shows a maximum ratio of bit energy to noise power density of 11.8 dB, corresponding to a bit error rate of 10−4.
Diffractive optical elements are commonly used to produce a regular array of spots or an arbitrary pattern from a single
coherent source. A challenge in the use of diffractive elements is the zeroth order in the reconstructed image. An
analysis of the zeroth order attributed to fabrication limitations is performed via simulation and the sensitivity of the
zeroth order intensity to surface relief height is determined. Two methods are proposed to reduce the zeroth order by
introducing a rectangular phase aperture to compensate for the zeroth order complex amplitude, and a checkerboard
phase plate to decouple the zeroth order intensity from the central zeroth order and redistribute the energy away from
the reconstructed image. The second method is found to be favourable in suppressing the zeroth order and a subsequent
analysis is performed to determine the tolerance of the technique to fabrication accuracies.
A modified genetic algorithm is proposed for the optimization of holographic diffusers for diffuse IR wireless home networking. The novel algorithm combines the conventional genetic algorithm and the simulated annealing algorithm, in which the simulated annealing algorithm is used to maintain a better diversity of chromosomes for the genetic algorithm. A better performance in locating the global minimum is demonstrated.