Automatic extraction of the structure of the retinal blood vessel network of premature infants.

OBJECTIVE Automatically detect the structure of blood vessels in ROP infants to allow ophthalmologist to analyze and detect the symptom early. MATERIAL AND METHOD This study presents a set of methods for detection of the skeletonized structure of premature infant's low-contrast retinal blood vessel network. Steps has been optimized for this study, namely statistically optimized LOG edge detection filter, Otsu thresholding, Medial Axis transform skeletonization, pruning, and edge thinning. RESULTS A set of 100 test images are grouped together into five testing groups based on their similar characteristics and clinicians suggestions. The authors applied the series of methods proposed on all the 100 images. The result from the algorithm was compared with ophthalmologists' hand-drawn ground truth and it can detect the blood vessel with a high specificity of 0.9879 and sensitivity of 0.8935. CONCLUSION The authors' algorithm can detect blood vessels effectively even though the image quality may not be good, have high noise, and low contrast. The algorithm can also detect the blood vessel at important locations such as the edge of the retina.