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)

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 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
Pages263-274
Number of pages12
Publication statusPublished - 2011

Publication series

NameLECTURE NOTES IN COMPUTER SCIENCE

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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