This paper presents a novel unsupervised iterative algorithm for segmenting blood vessels in fundus images. The algorithm first enhances vessel pixels and extracts an initial segmentation using thresholding. It then iteratively identifies new vessel pixels using adaptive thresholding of the residual image, and regions them into the existing segmentation. A novel stopping criterion terminates the iterations when false edges are identified instead of actual vessels. The algorithm achieves 93.2%-95.35% segmentation accuracy on abnormal retinal images from the STARE dataset in an average of 2.45 to 8 seconds per image, depending on the dataset. It is also over 90% accurate for segmenting peripapillary vessels.

1. ITERATIVE VESSEL SEGMENTATION OF FUNDUS IMAGES
ABSTRACT
This paper presents a novel unsupervised iterative blood vessel segmentation
algorithm using fundus images. First, a vessel enhanced image is generated by tophat
reconstruction of the negative green plane image. An initial estimate of the segmented
vasculature is extracted by global thresholding the vessel enhanced image. Next, new
vessel pixels are identified iteratively by adaptive thresholding of the residual image
generated by masking out the existing segmented vessel estimate from the vessel
enhanced image. The new vessel pixels are then region grown into the existing vessel,
thereby resulting in an iterative enhancement of the segmented vessel structure. As the
iterations progress, the number of false edge pixels identified as new vessel pixels
increases compared to the number of actual vessel pixels. A key contribution of this
paper is a novel stopping criterion that terminates the iterative process leading to higher
vessel segmentation accuracy. This iterative algorithm is robust to the rate of new
vessel pixel addition since it achieves 93.2%- 95.35% vessel segmentation accuracy
with 0.9577-0.9638 area under ROC curve (AUC) on abnormal retinal images from the
STARE data set. The proposed algorithm is computationally efficient and consistent in
vessel segmentation performance for retinal images with variations due to pathology,
uneven illumination, pigmentation and fields of view since it achieves a vessel
segmentation accuracy of about 95% in an average time of 2.45 seconds, 3.95 seconds
and 8 seconds on images from three public data sets DRIVE, STARE, and CHASE

2. DB1, respectively. Additionally, the proposed algorithm has more than 90%
segmentation accuracy for segmenting peripapillary blood
vessels in the images from the DRIVE and CHASE DB1 data
sets.