Search results for "C-means"

showing 10 items of 23 documents

Radio frequency fingerprinting for outdoor user equipment localization

2017

The recent advancements in cellular mobile technology and smart phone usage have opened opportunities for researchers and commercial companies to develop ubiquitous low cost localization systems. Radio frequency (RF) fingerprinting is a popular positioning technique which uses radio signal strength (RSS) values from already existing infrastructures to provide satisfactory user positioning accuracy in indoor and densely built outdoor urban areas where Global Navigation Satellite System (GNSS) signal is poor and hard to reach. However a major requirement for the RF fingerprinting to maintain good localization accuracy is the collection and updating of large training database. The Minimization…

langattomat lähiverkotKullback-Leibler divergenceK-Nearest NeighborpaikannusK-means clusteringRF fingerprintingmatkaviestinverkotradioaallotLTEWLANkoneoppiminenmobiililaitteetFuzzy C-means ClusteringklusterianalyysiMahalanobis distancehierarchical clustering
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Spatiotemporal hotspots analysis for exploring the evolution of diseases: An application to oto-laryngopharyngeal diseases

2013

Abstract View references (14) This paper presents a spatiotemporal analysis of hotspot areas based on the Extended Fuzzy C-Means method implemented in a geographic information system. This method has been adapted for detecting spatial areas with high concentrations of events and tested to study their temporal evolution. The data consist of georeferenced patterns corresponding to the residence of patients in the district of Naples (Italy) to whom a surgical intervention to the oto-laryngopharyngeal apparatus was carried out between the years 2008 and 2012

lcsh:Computer softwareGeographic information systemControl and OptimizationArticle Subjectbusiness.industrySpatiotemporal AnalysisComputational Mathematics Fuzzy C-Means disease analysis.disease analysisComputational Mathematicslcsh:QA76.75-76.765GeographyFuzzy C-MeansControl and Systems EngineeringGeoreferenceHotspot (geology)lcsh:Electrical engineering. Electronics. Nuclear engineeringbusinesslcsh:TK1-9971CartographyDemography
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A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning

2017

The aim of this study is to combine Biological Target Volume (BTV) segmentation and Gross Target Volume (GTV) segmentation in stereotactic neurosurgery.Our goal is to enhance Clinical Target Volume (CTV) definition, including metabolic and morphologic information, for treatment planning and patient follow-up.We propose a fully automatic approach for multimodal PET and MR image segmentation. This method is based on the Random Walker (RW) and Fuzzy C-Means clustering (FCM) algorithms. A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is presented, considering volume…

Radiotherapy PlanningBrain tumorHealth Informatics02 engineering and technologyFuzzy C-means clusteringRadiosurgeryBrain tumorsMultimodal ImagingING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI030218 nuclear medicine & medical imaging03 medical and health sciencesComputer-Assisted0302 clinical medicineRandom walker algorithm0202 electrical engineering electronic engineering information engineeringHumansMedicineSegmentationComputer visionRadiation treatment planningCluster analysisImage resolutionPET/MR imagingModality (human–computer interaction)Brain Neoplasmsbusiness.industryRadiotherapy Planning Computer-AssistedINF/01 - INFORMATICAMultimodal therapymedicine.diseaseRandom Walker algorithmMagnetic Resonance ImagingComputer Science ApplicationsBrain tumorGamma knife treatmentPositron-Emission Tomography020201 artificial intelligence & image processingMultimodal image segmentationBrain tumors; Fuzzy C-means clustering; Gamma knife treatments; Multimodal image segmentation; PET/MR imaging; Random Walker algorithm; Brain Neoplasms; Humans; Radiosurgery; Magnetic Resonance Imaging; Multimodal Imaging; Positron-Emission Tomography; Radiotherapy Planning Computer-AssistedArtificial intelligencebusinessGamma knife treatmentsSoftware
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Stable Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and a Modified Fuzzy C-Means Clustering

2011

In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. Three features are extracted from the tested image. The features are scaled down by a factor of 2 and mapped into a Self-Organizing Map. A modified Fuzzy C-Means clustering algorithm is used to divide the neuron units of the map in 2 classes. The entire image is again input for the Self-Organizing Map and the class of each pixel will be the class of its best matching unit in the Self-Organizing Map. Finally, the vessel network is post-processed using a hill climbing strategy on the connected components of the segmented image. The experimental evaluation on the DRIVE database shows accurate ex…

Self-organizing mapGround truthPixelSettore INF/01 - Informaticabusiness.industryComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationPattern recognitionFuzzy logicComputer visionSegmentationArtificial intelligenceCluster analysisbusinessHill climbingRetinal Vessels Self-Organizing Map Fuzzy C-Means.
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Fuzzy C-Means Segmentation on Brain MR Slices Corrupted by RF-Inhomogeneity

2007

Brain MR Images corrupted by RF-Inhomogeneity exhibit brightness variations in such a way that a standard Fuzzy C-Means (fcm) segmentation algorithm fails. As a consequence, modified versions of the algorithm can be found in literature, which take into account the artifact. In this work we show that the application of a suitable pre-processing algorithm, already presented by the authors, followed by a standard fcm segmentation achieves good results also. The experimental results ones are compared with those obtained using SPM5, which can be considered the state of the art algorithm oriented to brain segmentation and bias removal.

Artifact (error)BrightnessComputer sciencebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationPattern recognitionFuzzy logicBrain segmentationSegmentationComputer visionArtificial intelligenceMr imagesbusinessrf-inhomogeneity bias artifact mri fuzzy c-means segmentationHistogram equalization
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Exudates as Landmarks Identified through FCM Clustering in Retinal Images

2020

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

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 processingApplied Sciences
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A Fuzzy Logic C-Means Clustering Algorithm to Enhance Microcalcifications Clusters in Digital Mammograms

2011

The detection of microcalcifications is a hard task, since they are quite small and often poorly contrasted against the background of images. The Computer Aided Detection (CAD) systems could be very useful for breast cancer control. In this paper, we report a method to enhance microcalcifications cluster in digital mammograms. A Fuzzy Logic clustering algorithm with a set of features is used for clustering microcalcifications. The method described was tested on simulated clusters of microcalcifications, so that the location of the cluster within the breast and the exact number of microcalcifications is known.

C-meanCOMPUTER-AIDED DETECTIONComputer scienceCADFuzzy logicSet (abstract data type)Cluster (physics)medicineMammographycancerComputer visionCLASSIFICATION.Cluster analysisbreastmedicine.diagnostic_testbusiness.industryPattern recognitionImage enhancementComputer aided detectionSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)microcalcificationComputingMethodologies_PATTERNRECOGNITIONbreast; cancer; microcalcifications; clustering; fuzzy logic; C-means; COMPUTER-AIDED DETECTION; CLASSIFICATION.Artificial intelligencefuzzy logicbusinessclustering
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Rings for Privacy: an Architecture for Large Scale Privacy-Preserving Data Mining

2021

This article proposes a new architecture for privacy-preserving data mining based on Multi Party Computation (MPC) and secure sums. While traditional MPC approaches rely on a small number of aggregation peers replacing a centralized trusted entity, the current study puts forth a distributed solution that involves all data sources in the aggregation process, with the help of a single server for storing intermediate results. A large-scale scenario is examined and the possibility that data become inaccessible during the aggregation process is considered, a possibility that traditional schemes often neglect. Here, it is explicitly examined, as it might be provoked by intermittent network connec…

020203 distributed computingInformation privacyDistributed databasesDistributed databaseSettore ING-INF/03 - TelecomunicazioniComputer scienceReliability (computer networking)Secure Multi-Party Computation02 engineering and technologycomputer.software_genreSecret sharingData Mining; Data privacy; Distributed databases; Peer-to-peer computing; Secret sharing; Secure Multi-Party ComputationComputational Theory and MathematicsHardware and ArchitectureServerSignal Processing0202 electrical engineering electronic engineering information engineeringSecure multi-party computationData MiningData miningPeer-to-peer computingC-means data mining Privacy secret sharing secure multi-party computationSecret sharingcomputerData privacy
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Clinical support in radiation therapy scenarios: MR brain tumor segmentation using an unsupervised fuzzy C-Means clustering technique

2016

medicine.medical_specialtyMR segmentationComputer sciencemedicine.medical_treatmentBiophysicsGeneral Physics and AstronomyFuzzy logicradiation therapy030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicineClinical supportmedicineRadiology Nuclear Medicine and imagingCluster analysisSemi-automatic segmentationNeuro-radiosurgery treatmentbusiness.industryPattern recognitionGeneral MedicineFuzzy C-Means clusteringRadiation therapy030220 oncology & carcinogenesisArtificial intelligenceRadiologybusinessBrain tumor segmentationbrain tumorMR imaging
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Fast Fingerprints Classification only using the Directional Image

2007

The classification phase is an important step of an automatic fingerprint identification system, where the goal is to restrict only to a subset of the whole database the search time. The proposed system classifies fingerprint images in four classes using only directional image information. This approach, unlike the literature approaches, uses the acquired fingerprint image without enhancement phases application. The system extracts only directional image and uses three concurrent decisional modules to classify the fingerprint. The proposed system has a high classification speed and a very low computational cost. The experimental results show a classification rate of 87.27%.

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniBayesian networkc-means algorithmDecision networkFingerprint classificationNeural network
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