Fast and accurate retinal vasculature tracing and kernel-Isomap-based feature selection

The blood vessels in the retina have a characteristic radiating pattern, while there exists a significant variation dependent on the individual and/or medical condition. Extracting the geometric properties of these blood vessels have several important applications, such as biometrics (for identification) and medical diagnosis. In this paper, we will focus on biometric applications. For this, we propose a fast and accurate algorithm for tracing the blood vessels, and compare several candidate summary features based on the tracing results. Existing tracing algorithms based on a detailed analysis of the image can be too slow to quickly process a large volume of retinal images in real time (e.g., at a security check point). In order to select good features that can be extracted from the traces, we used kernel Isomap to test the distance between different retinal images as projected onto their respective feature spaces. We tested the following feature set: (1) angle among branches, (2) the number of fiber based on distance, (3) distance between branches, and (4) inner product among branches. Our results indicate that features 3 and 4 are prime candidates for use in fast, realtime biometric tasks. We expect our method to lead to fast and accurate biometric systems based on retinal images.