The retinal vasculature analysis plays an important role in the diagnosis of ophthalmological diseases, as well as general human disorders that manifest on the retina. The fundus photograph is a 2-D color image modality of the retina and is widely used in modern ophthalmology clinics due to its relatively low cost and its non-invasive access to the retina. However, due to the complexity of the retinal vasculature presented on the image and the large variation of the image quality, no automated method is able to re-construct the retinal vasculature (i.e. construct arteriovenous trees) satisfactorily, thus preventing its analysis on large-scale clinical datasets.
In this thesis, we present a systematic and complete study to automatically construct the retinal vasculature on fundus photographs and apply it to a clinical dataset. First of all, a preliminary study is conducted to detect and classify important landmarks in the retinal vasculature using a machine learning method. The evaluation of this method reveals the difficulty of identifying each landmark as an independent target. Then a novel and more global method is proposed to construct retinal arteriovenous trees (A/V trees). The strategy of the proposed method is to build an over-connected vessel network, and separate it into vascular trees, then classify them into A/V trees. Particularly, by taking advantages of specific properties of the retinal vasculature, global and local information are combined together to recognize landmarks of the vasculature. Instead of recognizing each landmark independently as other methods do, this method considers the relationship between landmarks in a more global manner, thus recognizing them simultaneously and globally. With a special graph design, each landmark is associated with multiple possible configurations and costs, and a near optimal solution is selected by minimizing the costs of landmarks and the global property of the whole vascular network. With each landmark recognized, the A/V trees are easily inferred with a pixel classification method. By doing so, local noise in the images and local errors during pre-processing are corrected to some degree, and small vessels that are difficult to classify locally can also be recognized. The proposed method is compared with another method and the evaluation demonstrates its superiority.
To demonstrate its potential applicability, we apply the proposed method on a cohort study data of HIV-infected patients with treatment. New metrics to analyze retinal vessel width is developed based on the A/V trees built using the proposed method, and it is compared with a conventional metric. Statistical analysis reveals the advantages of the new metric and thus indicates the benefit of the proposed method and its potential application on large datasets.