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RESEARCH PRODUCT
Exudates as Landmarks Identified through FCM Clustering in Retinal Images
Cesare ValentiTahreer DwickatDomenico TegoloHadi Hamadsubject
Computer scienceDiabetic retinopathy; Exudates; Fuzzy C-means clustering; Morphological processing; Retinal landmarks; SegmentationFundus (eye)Fuzzy logiclcsh:TechnologyField (computer science)030218 nuclear medicine & medical imaginglcsh:Chemistry03 medical and health sciences0302 clinical medicineFcm clusteringfuzzy C-means clusteringretinal landmarksGeneral Materials ScienceSegmentationSensitivity (control systems)Cluster analysisInstrumentationlcsh:QH301-705.5Fluid Flow and Transfer ProcessesSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniPixelSettore INF/01 - Informaticabusiness.industrylcsh:TProcess Chemistry and TechnologyexudatessegmentationGeneral EngineeringPattern recognitionlcsh:QC1-999Computer Science Applicationsdiabetic retinopathyComputingMethodologies_PATTERNRECOGNITIONlcsh:Biology (General)lcsh:QD1-999lcsh:TA1-2040Artificial intelligencebusinesslcsh:Engineering (General). Civil engineering (General)030217 neurology & neurosurgerylcsh:Physicsmorphological processingdescription
The aim of this work was to develop a method for the automatic identification of exudates, using an unsupervised clustering approach. The ability to classify each pixel as belonging to an eventual exudate, as a warning of disease, allows for the tracking of a patient&rsquo
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-12-25 | Applied Sciences |