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.
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Lupaşcu, C.A., Tegolo, D. (2011). Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and K-Means Clustering. In: Rizzo, R., Lisboa, P.J.G. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2010. Lecture Notes in Computer Science(), vol 6685. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21946-7_21
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DOI: https://doi.org/10.1007/978-3-642-21946-7_21
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