6533b854fe1ef96bd12ae0cb
RESEARCH PRODUCT
A multiscale approach to automatic and unsupervised retinal vessel segmentation using Self-Organizing Maps
Domenico TegoloCarmen Alina Lupascusubject
Self-organizing mapMajority ruleComputer science0206 medical engineeringComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologySelf-organizing mapFuzzy logicCLAHE030218 nuclear medicine & medical imagingRetinal vessel03 medical and health scienceschemistry.chemical_compound0302 clinical medicineMajority votingSegmentationComputer visionComputingMethodologies_COMPUTERGRAPHICSFuzzy C-Mean1707Settore INF/01 - Informaticabusiness.industrySTAPLERetinal020601 biomedical engineeringRetinal vesselHuman-Computer InteractionComputer Networks and CommunicationchemistryAdaptive histogram equalizationArtificial intelligencebusinessSoftwaredescription
In this paper an automatic unsupervised method for retinal vessel segmentation is described. Self-Organizing Map, modified Fuzzy C-Means, STAPLE algorithms and majority voting strategy were adopted to identify a segmentation of the retinal vessels. The performance of the proposed method was evaluated on the DRIVE database.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-06-23 |