Retinal blood vessel detection using frequency analysis and local-mean-interpolation filters

Publisher Website
Google Scholar
Abstract
Unlike the existing automatic retinal blood vessel detection methods in which the vessels are detected by edge detection, thresholding or both (such as successive local probing) in the spatial domain, this paper presents a frequency-domain approach to the vessel detection problem. By having a frequency-domain analysis of the vessel signals, we found that the vessel signals between 0.1 and 0.25 on the normalized frequency scale showed a relatively high signal-to-noise ratio, and thus could be filtered out from the other image signals by using a band-pass filter. Instead of using a conventional digital filter, a band of Local-Mean-Interpolation (LMI) filters were employed. They provide not only the function of a band-pass filter that is needed, but also a number of desirable features from practical point of view, such as easy to implement, computationally fast, and high filtering performance. Twenty randomly selected color retinal images were used in testing the proposed method. The results showed that the vessel details could be successfully detected by this new method. When compared with the hand-labeled ground-truth segmentation and measured by the Figure of Merit (FOM = true positive/(1+false positive)), it was found that the method achieved an FOM of up to 0.79. As a final note, with some modifications, the method presented may be extended to the automatic detection of vessels (or other features/objects) in other 2D or 3D medical images, such as ultra-sound, CAT, MRI images.

This publication has 0 references indexed in Scilit: