In this dissertation, I investigated computing algorithms for automated retinal blood vessel detection. Changes in blood vessel structures are important indicators of many diseases such as diabetes, hypertension, etc. Blood vessel is also very useful in tracking of disease progression, and for biometric authentication. In this dissertation, I proposed two algorithms to detect blood vessel maps in retina. The first algorithm is based on integration of a Gaussian tracing scheme and a Gabor-variance filter. This algorithm traces the large blood vessel in retinal images enhanced with adaptive histogram equalization. Small vessels are traced on further enhanced images by a Gabor-variance filter. The second algorithm is called a radial contrast transform (RCT) algorithm, which converts the intensity information in spatial domain to a high dimensional radial contrast domain. Different feature descriptors are designed to improve the speed, sensitivity, and expandability of the vessel detection system. Performances comparison of the two algorithms with those in the literature shows favorable and robust results. Furthermore, a new performance measure based on central line of blood vessels is proposed as an alternative to more reliable assessment of detection schemes for small vessels, because the significant variations at the edges of small vessels need not be considered. The proposed algorithms were successfully tested in the field for early diabetic retinopathy (DR) screening. A highly modular code library to take advantage of the parallel processing power of multi-core computer architecture was tested in a clinical trial. Performance results showed that our scheme can achieve similar or even better performance than human expert readers for detection of micro-aneurysms on difficult images.

Ocular fundus image can provide information on pathological changes caused by local ocular diseases and early signs of certain systemic diseases. Automated analysis and interpretation of fundus images has become a necessary and important diagnostic procedure in ophthalmology. Among the features in ocular fundus image are the optic disc, fovea (central vision area), lesions, and retinal vessels. These features are useful in revealing the states of diseases in the form of measurable abnormalities such as length of diameter, change in color, and degree of tortuosity in the vessels. In addition, retinal vessels can also serve as landmarks for image-guided laser treatment of choroidal neovascularization. Thus, reliable methods for blood vessel detection that preserve various vessel measurements are needed. In this paper, we will examine the pathological issues in the analysis of retinal vessels in digital fundus images and give a survey of current image processing methods for extracting vessels in retinal images with a view to categorize them and highlight their differences and similarities. We have also implemented two major approaches using matched filter and mathematical morphology respectively and compared their performances. Some prospective research directions are identified. / Singapore-MIT Alliance (SMA)