Search results for " vessel segmentation"

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Analysis of normal human retinal vascular network architecture using multifractal geometry

2017

AIM To apply the multifractal analysis method as a quantitative approach to a comprehensive description of the microvascular network architecture of the normal human retina. METHODS Fifty volunteers were enrolled in this study in the Ophthalmological Clinic of Cluj-Napoca, Romania, between January 2012 and January 2014. A set of 100 segmented and skeletonised human retinal images, corresponding to normal states of the retina were studied. An automatic unsupervised method for retinal vessel segmentation was applied before multifractal analysis. The multifractal analysis of digital retinal images was made with computer algorithms, applying the standard box-counting method. Statistical analyse…

0301 basic medicineEarly detectionGeometryFundus (eye)03 medical and health scienceschemistry.chemical_compoundretinal vessel segmentationlcsh:OphthalmologyClinical ResearchMedicineSegmentationRetinal microvasculaturebusiness.industryRetinalMultifractal systemGeneralized dimensionsMultifractalRetinal vesselOphthalmology030104 developmental biologyMicrovascular NetworkRetinal image analysisStandard box-counting methodchemistryVascular networklcsh:RE1-994business
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A Comparative Study on Feature Selection for Retinal Vessel Segmentation Using FABC

2009

This paper presents a comparative study on five feature selection heuristics applied to a retinal image database called DRIVE. Features are chosen from a feature vector (encoding local information, but as well information from structures and shapes available in the image) constructed for each pixel in the field of view (FOV) of the image. After selecting the most discriminatory features, an AdaBoost classifier is applied for training. The results of classifications are used to compare the effectiveness of the five feature selection methods.

PixelSettore INF/01 - InformaticaComputer sciencebusiness.industryFeature vectorRetinal images vessel segmentation AdaBoost classifier feature selection.ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognitionFeature selectionFeature (computer vision)SegmentationComputer visionArtificial intelligenceHeuristicsbusinessFeature detection (computer vision)
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FABC: Retinal Vessel Segmentation Using AdaBoost

2010

This paper presents a method for automated vessel segmentation in retinal images. For each pixel in the field of view of the image, a 41-D feature vector is constructed, encoding information on the local intensity structure, spatial properties, and geometry at multiple scales. An AdaBoost classifier is trained on 789 914 gold standard examples of vessel and nonvessel pixels, then used for classifying previously unseen images. The algorithm was tested on the public digital retinal images for vessel extraction (DRIVE) set, frequently used in the literature and consisting of 40 manually labeled images with gold standard. Results were compared experimentally with those of eight algorithms as we…

Databases FactualComputer scienceFeature vectorFeature extractionNormal DistributionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processingModels BiologicalEdge detectionArtificial IntelligenceImage Processing Computer-AssistedHumansSegmentationComputer visionAdaBoostFluorescein AngiographyElectrical and Electronic EngineeringTraining setPixelContextual image classificationSettore INF/01 - Informaticabusiness.industryReproducibility of ResultsRetinal VesselsWavelet transformBayes TheoremPattern recognitionGeneral MedicineImage segmentationComputer Science ApplicationsComputingMethodologies_PATTERNRECOGNITIONROC CurveTest setAdaBoost classifier retinal images vessel segmentationArtificial intelligencebusinessAlgorithmsBiotechnology
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