Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response

We describe an automated method to locate and outline blood vessels in images of the ocular fundus. Such a tool should prove useful to eyecare specialists for purposes of patient screening, treatment evaluation, and clinical stuck Our method differs from previously known methods in that it uses local and global vessel features cooperatively to segment the vessel net-work. A comparison of our method against hand-labeled ground truth segmentations of five images yielded 65% sensitivity and 81% specificity. A previously known technique yielded 69% sensitivity and 63% specificity. For a baseline, we also compared the ground truth against a second hand labeling, yielding 80% sensitivity and 90% specificity. These numbers indicate our method improves upon the previously known technique, but that further improvement is still possible.