Automatic unsupervised segmentation of retinal vessels using self-organizing maps and K-means clustering

Authors:
Carmen Alina Lupascu;Domenico Tegolo
Affiliations:
Dipartimento di Matematica e Informatica, Università degli Studi di Palermo, Palermo, Italy;Dipartimento di Matematica e Informatica, Università degli Studi di Palermo, Palermo, Italy
Venue:
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
Year:
2010

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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, 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.