Search results for "Retinal Image"

showing 10 items of 22 documents

A Fast Multiresolution Approach Useful for Retinal Image Segmentation

2018

Retinal diseases such as retinopathy of prematurity (ROP), diabetic and hypertensive retinopathy present several deformities of fundus oculi which can be analyzed both during screening and monitoring such as the increase of tortuosity, lesions of tissues, exudates and hemorrhages. In particular, one of the first morphological changes of vessel structures is the increase of tortuosity. The aim of this work is the enhancement and the detection of the principal characteristics in retinal image by exploiting a non-supervised and automated methodology. With respect to the well-known image analysis through Gabor or Gaussian filters, our approach uses a filter bank that resembles the “à trous” wav…

0301 basic medicine03 medical and health sciences030104 developmental biologySettore INF/01 - Informaticabusiness.industryComputer scienceRetinal image segmentationComputer visionArtificial intelligencebusinessElliptical Gaussian filters Directional Map Retinal Vessel Fundus OculiProceedings of the 7th International Conference on Pattern Recognition Applications and Methods
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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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Accurate estimation of retinal vessel width using bagged decision trees and an extended multiresolution Hermite model.

2012

We present an algorithm estimating the width of retinal vessels in fundus camera images. The algorithm uses a novel parametric surface model of the cross-sectional intensities of vessels, and ensembles of bagged decision trees to estimate the local width from the parameters of the best-fit surface. We report comparative tests with REVIEW, currently the public database of reference for retinal width estimation, containing 16 images with 193 annotated vessel segments and 5066 profile points annotated manually by three independent experts. Comparative tests are reported also with our own set of 378 vessel widths selected sparsely in 38 images from the Tayside Scotland diabetic retinopathy scre…

Accurate estimationComputer scienceStability (learning theory)Decision treeHealth Informaticscomputer.software_genreSensitivity and SpecificityPattern Recognition AutomatedSet (abstract data type)Parametric surfaceImage Interpretation Computer-AssistedHumansRadiology Nuclear Medicine and imagingFluorescein AngiographyHermite polynomialsDiabetic RetinopathySettore INF/01 - InformaticaRadiological and Ultrasound TechnologyReproducibility of ResultsRetinal VesselsImage EnhancementComputer Graphics and Computer-Aided DesignData setComputer Vision and Pattern RecognitionData miningRetinal images Vessel width Multiresolution Hermite model Ensembles of bagged decision trees Medical image analysiscomputerAlgorithmsTest dataRetinoscopyMedical image analysis
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Intraocular Telescopic System Design: Optical and Visual Simulation in a Human Eye Model

2016

Purpose. To design an intraocular telescopic system (ITS) for magnifying retinal image and to simulate its optical and visual performance after implantation in a human eye model. Methods. Design and simulation were carried out with a ray-tracing and optical design software. Two different ITS were designed, and their visual performance was simulated using the Liou-Brennan eye model. The difference between the ITS was their lenses’ placement in the eye model and their powers. Ray tracing in both centered and decentered situations was carried out for both ITS while visual Strehl ratio (VSOTF) was computed using custom-made MATLAB code. Results. The results show that between 0.4 and 0.8 mm of d…

Article Subjectbusiness.industryRetinal damageComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONStrehl ratioMatlab codeRetinal image03 medical and health sciencesOphthalmology0302 clinical medicinemedicine.anatomical_structurelcsh:Ophthalmologylcsh:RE1-994030221 ophthalmology & optometryMedicineSoftware designSystems designRay tracing (graphics)Computer visionHuman eyeArtificial intelligencebusiness030217 neurology & neurosurgeryResearch ArticleJournal of Ophthalmology
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Steerable wavelet transform for atlas based retinal lesion segmentation

2013

International audience; Computer aided diagnosis and follow up can help in prevention and treatment of diabetes and its related complications. Screening of diabetes related disease in the eyes is done by a special low cost fundus camera. A follow up of the patients visiting at di fferent time intervals for screening brings us to the problem of image analysis for change detection and its cost per patient. It is very likely that human annotations for the lesions may be erroneous and often time consuming. Since the ethnic background plays a signi cant role in retinal pigment epithelium, visibility of the choroidal vasculature and overall retinal luminance in patients and retinal images, an eth…

Computer scienceRetinal lesionImage processing[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]LuminanceFundus camera030218 nuclear medicine & medical imaging03 medical and health scienceschemistry.chemical_compound0302 clinical medicine[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]medicineSegmentationComputer visionRetinaRetinal pigment epitheliumDiabetic Retinopathybusiness.industryAtlas (topology)Atals segmentationWavelet transform[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]RetinalDiabetic retinopathymedicine.diseaseSteerable filtersmedicine.anatomical_structurechemistryComputer-aided diagnosis030221 ophthalmology & optometryRetinal ImageArtificial intelligencebusinessChange detection
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Transition from self tilt to object tilt during maintained lateral tilt in parabolic flight.

1991

Abstract 19 young healthy subjects were subjected to parabolic rollercoaster flight. A horizontal luminous line was seen by the subjects in a headfixed goggle device. During the hypergravic phases of parabolic flight the luminous line seemed to rotate into and during the hypogravic phase against the direction of static head tilt. Ocular counter rotation and activity of the neck position receptors cannot explain these subjective rotations. We conclude that information from the otolith system, converging with visual information within the brain, dislocated the headfixed visual target line. While the retinal image of the luminous line remains unchanged, loading and unloading the otoliths in pa…

Counter rotationgenetic structuresEye MovementsRotationHead tiltParabolic flightPhase (waves)Aerospace EngineeringHypergravityOtolithic MembraneOpticsHumansPhysicsbusiness.industryWeightlessnessHealthy subjectsSpace FlightVestibular Function TestsProprioceptionRetinal imageTilt (optics)Head MovementsLine (geometry)Visual Perceptionsense organsbusinessActa astronautica
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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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Time response of the vision binocularity by use of dynamic suppression of retinal images

1999

We present a novel technique for determination the stereopsis dynamic response, using as stimulus a random dot stereopair. Stereopsis can be evoked or depressed by continuous or flash illumination of the stimulus with simultaneous control of a special light scattering obstacle build in the visual path of one eye. The obstacle--a thin plate of electrooptical PLZT ceramics-- exposes (by applying of the voltage to semitransparent gold electrodes deposited on both surfaces of the plate) light scattering so blurring the retinal image, similar as for an eye with a cataract, depressing stereopsis. The random dot stereopair contains contours of images with a different stereodisparity. The PLZT plat…

Novel techniqueMaterials sciencegenetic structuresbusiness.industryRetinalStimulus (physiology)eye diseasesLight scatteringRetinal imagechemistry.chemical_compoundStereopsisOpticschemistryTime responseComputer visionsense organsArtificial intelligencebusinessBinocular visionSPIE Proceedings
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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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A visual framework to create photorealistic retinal vessels for diagnosis purposes

2020

The methods developed in recent years for synthesising an ocular fundus can be been divided into two main categories. The first category of methods involves the development of an anatomical model of the eye, where artificial images are generated using appropriate parameters for modelling the vascular networks and fundus. The second type of method has been made possible by the development of deep learning techniques and improvements in the performance of hardware (especially graphics cards equipped with a large number of cores). The methodology proposed here to produce high-resolution synthetic fundus images is intended to be an alternative to the increasingly widespread use of generative ad…

PLUS DISEASEData augmentationFundus OculiComputer scienceCOMPUTER-AIDED DIAGNOSISIMAGESSEGMENTATIONComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONHealth InformaticsSynthetic retinal imageFundus (eye)Fundus image analysisStatistical featuresTORTUOSITY03 medical and health sciences0302 clinical medicineImage Processing Computer-AssistedComputer vision030212 general & internal medicineGraphics030304 developmental biologyGraphical user interfaceSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni0303 health sciencesSettore INF/01 - Informaticabusiness.industryDeep learningRetinal VesselsReal imageComputer Science ApplicationsPredictive evaluation diseasesFILTERA priori and a posterioriArtificial intelligencebusinessSYSTEMJournal of Biomedical Informatics
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