0000000000063868

AUTHOR

Andrea Giachetti

showing 3 related works from this author

Novel VAMPIRE algorithms for quantitative analysis of the retinal vasculature

2013

This paper summarizes three recent, novel algorithms developed within VAMPIRE, namely optic disc and macula detection, arteryvein classification, and enhancement of binary vessel masks, and their performance assessment. VAMPIRE is an international collaboration growing a suite of software tools to allow efficient quantification of morphological properties of the retinal vasculature in large collections of fundus camera images. VAMPIRE measurements are currently mostly used in biomarker research, i.e., investigating associations between the morphology of the retinal vasculature and a number of clinical and cognitive conditions.

retinaRetinaSettore INF/01 - InformaticaContextual image classificationbusiness.industryComputer scienceVampireRetinalImage segmentationClassificationFeature detectionRetina; Feature detection; Segmentation; Classification; Biomarkerschemistry.chemical_compoundSegmentationmedicine.anatomical_structurechemistrymedicineSegmentationComputer visionArtificial intelligencebusinessAlgorithmBiomarkersOptic discFeature detection (computer vision)2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC)
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VAMPIRE: Vessel assessment and measurement platform for images of the REtina

2011

We present VAMPIRE, a software application for efficient, semi-automatic quantification of retinal vessel properties with large collections of fundus camera images. VAMPIRE is also an international collaborative project of four image processing groups and five clinical centres. The system provides automatic detection of retinal landmarks (optic disc, vasculature), and quantifies key parameters used frequently in investigative studies: vessel width, vessel branching coefficients, and tortuosity. The ultimate vision is to make VAMPIRE available as a public tool, to support quantification and analysis of large collections of fundus camera images.

Opthalmology; image processing; retinaEngineeringVesselgenetic structuresOpthalmologyImage processingRetinal ImagesRetinaRetina; Image; VesselSoftwareMedical imagingmedicineHumansSegmentationComputer visionRetinaSettore INF/01 - Informaticabusiness.industryVampireRetinal VesselsImage segmentationeye diseasesimage processingFractalsVAMPIREmedicine.anatomical_structureImageArtificial intelligenceAdvanced image processing and mathematical modeling techniquesbusinessOptic disc2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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SHREC 2020: Retrieval of digital surfaces with similar geometric reliefs

2020

Abstract This paper presents the methods that have participated in the SHREC’20 contest on retrieval of surface patches with similar geometric reliefs and the analysis of their performance over the benchmark created for this challenge. The goal of the context is to verify the possibility of retrieving 3D models only based on the reliefs that are present on their surface and to compare methods that are suitable for this task. This problem is related to many real world applications, such as the classification of cultural heritage goods or the analysis of different materials. To address this challenge, it is necessary to characterize the local ”geometric pattern” information, possibly forgetti…

Information retrievalForgettingGeometric patternComputer scienceGeneral Engineering020207 software engineering3d modelContext (language use)02 engineering and technologyCONTESTComputer Graphics and Computer-Aided DesignTask (project management)Human-Computer InteractionCultural heritageBenchmark (surveying)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputers & Graphics
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