6533b82efe1ef96bd1293dfc

RESEARCH PRODUCT

Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and K-Means Clustering

Domenico TegoloCarmen Alina Lupascu

subject

Self-organizing mapGround truthSettore INF/01 - InformaticaPixelbusiness.industryComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONk-means clusteringScale-space segmentationPattern recognitionRetinal vessels Self-Organizing Map K-MeansSegmentationComputer visionArtificial intelligenceCluster analysisbusinessHill climbing

description

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.

yearjournalcountryeditionlanguage
2011-01-01
https://doi.org/10.1007/978-3-642-21946-7_21