Blood Vessel Detection via a Multi-window Parameter Transform

Abstract

A parallel algorithm to detect retinal blood vessels has been developed for use in an automated diabetic retinopathy detection system. Localized adaptive thresholding and a multi-window Radon transform (RT) are utilized to detect the vascular system in retinal images. Multi-window parameter transforms are intrinsically parallel and offer increased performance over conventional transforms. The image is adoptively thresholded and then the multi-window RT is applied at different window sizes or partition levels. Results from each partition level are combined and morphologically processed to improve final performance. Multiple partitions are necessary to account for the size variation present in retinal blood vessels. The algorithm was tested with 20 images, 10 normal and 10 abnormal and the results demonstrate the robustness of the algorithm in the presence of noise. An average true positive rate of 86.3 % with a false positive rate of 3.9% is accomplished with this algorithm when tested against hand-labeled data