Automatic Unsupervised Segmentation of Retinal Vessels using Self-Organizing Maps and K-means clustering

Abstract

In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. A self-organizing map is trained on a portion of the same image that is tested and K-means clustering algorithm is used to divide the map units in 2 classes. The entire image is again input for the self-organizing map, and the class of each pixel will be the class of the best matching unit on the self-organizing map. Finally, the vessel network is post-processed using a hill climbing strategy on the connected components of the segmented image. The experimental evaluation on the publicly available DRIVE database shows accurate extraction of vessels network and a good agreement between our segmentation and the ground truth. The mean accuracy is 0.9459 with a standard deviation of 0.0094 is outperforming the manual segmentation rates obtained by other widely used unsupervised methods. A good kappa value of 0.6562 is inline with state-of-the-art supervised and unsupervised approaches.