A review of feature-based retinal image analysis

Retinal imaging is a fundamental tool in ophthalmic diagnostics. The potential use of retinal imaging within screening programs, with consequent need to analyze large numbers of images with high throughput, is pushing the digital image analysis field to find new solutions for the extraction of specific information from the retinal image. The aim of this review is to explore the latest progress in image processing techniques able to recognize specific retinal image features. and potential features of disease. In particular, this review aims to describe publically available retinal image databases, highlight different performance evaluators commonly used within the field, outline current approaches in feature-based retinal image analysis, and to map related trends. This review found two key areas to be addressed for the future development of automatic retinal image analysis: fundus image quality and the affect image processing may impose on relevant clinical information within the images. Performance evaluators of the algorithms reviewed are very promising, however absolute values are difficult to interpret when validating system suitability for use within clinical practice

Segmentation, registration,and selective watermarking of retinal images

In this dissertation, I investigated some fundamental issues related to medical image segmentation, registration, and watermarking. I used color retinal fundus images to perform my study because of the rich representation of different objects (blood vessels, microaneurysms, hemorrhages, exudates, etc.) that are pathologically important and have close resemblance in shapes and colors. To attack this complex subject, I developed a divide-and-conquer strategy to address related issues step-by-step and to optimize the parameters of different algorithm steps. Most, if not all, objects in our discussion are related. The algorithms for detection, registration, and protection of different objects need to consider how to differentiate the foreground from the background and be able to correctly characterize the features of the image objects and their geometric properties. To address these problems, I characterized the shapes of blood vessels in retinal images and proposed the algorithms to extract the features of blood vessels. A tracing algorithm was developed for the detection of blood vessels along the vascular network. Due to the noise interference and various image qualities, the robust segmentation techniques were used for the accurate characterization of the objects shapes and verification. Based on the segmentation results, a registration algorithm was developed, which uses the bifurcation and cross-over points of blood vessels to establish the correspondence between the images and derive the transformation that aligns the images. A Region-of-Interest (ROI) based watermarking scheme was proposed for image authenticity. It uses linear segments extracted from the image as reference locations for embedding and detecting watermark. Global and locally-randomized synchronization schemes were proposed for bit-sequence synchronization of a watermark. The scheme is robust against common image processing and geometric distortions (rotation and scaling), and it can detect alternations such as moving or removing of the image content

Human retinal oximetry using spectral imaging

The principal aim of the research described in this thesis was to develop a technique of non-invasively measuring the oxygen saturation within the retinal vasculature of human subjects (retinal oximetry). The evaluation of a hyperspectral fundus camera used to acquire retinal images in different wavelengths of visible light, and the image analysis techniques used to perform retinal oximetry are described. Validation of the oximetry techniques was performed using an artificial eye containing human blood of known oxygen saturation: the calculated oxygen saturation was compared to the gold standard measurement. The mean differences between the calculated and measured oxygen saturations were small. Hyperspectral imaging/oximetry of normal subjects was performed to characterize the oximetric features of the retinal vasculature. The mean oxygen saturation (± SD) of the temporal retinal arterioles and venules were 110.8% (± 11.8%) and 27.7% (± 3.2%) respectively. The application of the retinal oximetry technique was explored in patients with retinal arterial and venous occlusion to determine whether oximetric changes in the retinal vasculature could be detected. Variation in measured oxygen saturation of the retinal arterioles and venules respectively were apparent, and corresponded with angiographic features of retinal capillary loss. The techniques were applied to patients with asymmetrical primary open angle glaucoma to determine whether oximetric changes could be detected. The mean oxygen saturation of the temporal retinal venules were significantly higher [44.8% (± 24.2%)] in the more advanced glaucomatous eyes compared to normal subjects. Hyperoxia of the retinal venules suggests reduced oxygen consumption as a consequence of inner retinal dysfunction in glaucoma. However, because of the small sample size, further research on a larger population of subjects is required to support this finding. Hyperspectral imaging could be used to detect oximetric abnormalities in the retinal vasculature in patients with retinovascular occlusion and glaucoma