An Unsupervised Retinal Vessel Extraction and Segmentation Method Based On a Tube Marked Point Process Model
Tianyu Li, Mary L. Comer, Josiane Zerubia
Abstract
Retinal vessel extraction and segmentation is essential for supporting diagnosis of eye-related diseases. In recent years, deep learning has been applied to vessel segmentation and achieved excellent performance. However, these supervised methods require accurate hand-labeled training data, which may not be available. In this paper, we propose an unsupervised segmentation method based on our previous connected tube marked point process (MPP) model. The vessel network is extracted by the connected-tube MPP model first. Then a new tube-based segmentation method is applied to the extracted tubes. We test this method on STARE and DRIVE databases and the results show that not only do we extract the retina vessel network accurately, but we also achieve high G-means score for vessel segmentation, without using labeled training data.