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

Domenico Tegolo, Carmen Alina Lupascu

Research output: Chapter in Book/Report/Conference proceedingChapter

32 Citations (Scopus)


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 in2 classes. The entire image is again input for the self-organizing map, and the class ofeach 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 accurateextraction of vessels network and a good agreement between our segmentation andthe ground truth. The mean accuracy is 0.9459 with a standard deviation of 0.0094 isoutperforming the manual segmentation rates obtained by other widely used unsupervisedmethods. A good kappa value of 0.6562 is inline with state-of-the-art supervisedand unsupervised approaches.
Original languageEnglish
Title of host publicationComputational Intelligence Methods for Bioinformatics and Biostatistics, 7th International meeting, Cibb 2010, Palermo, Italy, September 2010, Revised Selected Papers
Number of pages12
Publication statusPublished - 2011

Publication series


All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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