Wing Kee Damon Wong

ORIGA -light : An online retinal fundus image database for glaucoma analysis and research

Retinal fundus image is an important modality to document the health of the retina and is widely used to diagnose ocular diseases such as glaucoma, diabetic retinopathy and age-related macular degeneration. However, the enormous amount of retinal data obtained nowadays mostly stored locally; and the valuable embedded clinical knowledge is not efficiently exploited. In this paper we present an online depository, ORIGA-light, which aims to share clinical groundtruth retinal images with the public; provide open access for researchers to benchmark their computer-aided segmentation algorithms. An in-house image segmentation and grading tool is developed to facilitate the construction of ORIGA-light. A quantified objective benchmarking method is proposed, focusing on optic disc and cup segmentation and Cup-to-Disc Ratio (CDR). Currently, ORIGA-light contains 650 retinal images annotated by trained professionals from Singapore Eye Research Institute. A wide collection of image signs, critical for glaucoma diagnosis, are annotated. We will update the system continuously with more clinical ground-truth images. ORIGA-light is available for online access upon request.

Convex hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis

Glaucoma is the second leading cause of blindness. Glaucoma can be diagnosed through measurement of neuro-retinal optic cup-to-disc ratio (CDR). Automatic calculation of optic cup boundary is challenging due to the interweavement of blood vessels with the surrounding tissues around the cup. A Convex Hull based Neuro-Retinal Optic Cup Ellipse Optimization algorithm improves the accuracy of the boundary estimation. The algorithm’s effectiveness is demonstrated on 70 clinical patient’s data set collected from Singapore Eye Research Institute. The root mean squared error of the new algorithm is 43% better than the ARGALI system which is the state-of-the-art. This further leads to a large clinical evaluation of the algorithm involving 15 thousand patients from Australia and Singapore.

Neuro-Retinal Optic Cup Detection in Glaucoma Diagnosis

Glaucoma is the one of the two major causes of blindness, which can be diagnosed through measurement of neuro-retinal optic cup-to-disc ratio (CDR). Automatic calculation of optic cup boundary is challenging due to the interweavement of blood vessels with the surrounding tissues around the cup. A multimodality fusion approach for neuroretinal cup detection improves the accuracy of the boundary estimation. The algorithm’s effectiveness is demonstrated on 71 manually segmented retina fundus images collected from Singapore Eye Research Institute. By comparing our automatic cup height measurement to ground truth, we found that our method accurately detected neuro-retinal cup height for 69 images, achieved 97.2% accuracy. The evaluation was based on a criterion that is more stringent than the clinically acceptable interor intra-observer variability. This further leads to a large clinical evaluation of the algorithm involving 15 thousand patients from Australia and Singapore. Keywordsglaucoma; neuro-retinal optic cup; computer-aideddiagnosis; ellipse fitting; convex hull

Automatic optic disc segmentation based on image brightness and contrast

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.

Automatic fundus image classification for computer-aided diagonsis

With the advances of computer technology, more and more computer-aided diagnosis (CAD) systems have been developed to provide the “second opinion”. This paper reports an automatic fundus image classification technique that is designed to screen out the severely degraded fundus images that cannot be processed by traditional CAD systems. The proposed technique classifies fundus images based on the image range property. In particular, it first calculates a number of range images from a fundus image at different resolutions. A feature vector is then constructed based on the histogram of the calculated range images. Finally, fundus images can be classified by a linear discriminant classifier that is built by learning from a large number of normal and abnormal training fundus images. Experiments over 644 fundus images of different qualities show that the classification accuracy of the proposed technique reaches above 96%.

Genome-Wide Association study for glaucoma

The Genome-Wide Association (GWA) study is the latest approach in the development of genetic studies and is renowned for its widespread success in identifying disease variants within the genome for various common diseases. It is a highly popular study amongst geneticists worldwide, evident from the numerous GWA studies conducted in laboratories all over the world. This paper introduces various GWA study designs currently recognized, including other aspects such as the software tools and its progress thus far. Especially, the paper reviews the genetic studies for glaucoma, an ocular disease which can lead to irreversible and permanent vision loss. Glaucomatous progression can be slowed or even halted if detected early; however, genetic information on glaucoma has not been well established yet. Therefore, by conducting a GWA study on glaucoma to find comprehensive associated genetic variants, the early detection of glaucoma through GWA may finally be seen as a possibility.