Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Retinal Blood Vessel Segmentation by Means of Scale-Space Analysis and Region Growing
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
A Comparative Study on Feature Selection for Retinal Vessel Segmentation Using FABC
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
FABC: retinal vessel segmentation using adaboost
IEEE Transactions on Information Technology in Biomedicine
Distance functions, clustering algorithms and microarray data analysis
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation
IEEE Transactions on Image Processing
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In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. Three features are extracted from the tested image. The features are scaled down by a factor of 2 and mapped into a Self-Organizing Map. A modified Fuzzy C-Means clustering algorithm is used to divide the neuron units of the map 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 its best matching unit in 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 DRIVE database shows accurate extraction of vessels network and a good agreement between our segmentation and the ground truth. The mean accuracy, 0.9482 with a standard deviation of 0.0075, is outperforming the manual segmentation rates obtained by other widely used unsupervised methods. A good kappa value of 0.6565 is comparable with state-of-the-art supervised or unsupervised approaches.