Search results for "C-mean"

showing 7 items of 27 documents

Normal and Abnormal Tissue Classification in Positron Emission Tomography Oncological Studies

2018

Positron Emission Tomography (PET) imaging is increasingly used in radiotherapy environment as well as for staging and assessing treatment response. The ability to classify PET tissues, as normal versus abnormal tissues, is crucial for medical analysis and interpretation. For this reason, a system for classifying PET area is implemented and validated. The proposed classification is carried out using k-nearest neighbor (KNN) method with the stratified K-Fold Cross-Validation strategy to enhance the classifier reliability. A dataset of eighty oncological patients are collected for system training and validation. For every patient, lesion (abnormal tissue) and background (normal tissue around …

Treatment responsepositron emission tomographyK-nearest neighborKernel support vector machineComputer scienceNormal tissueK-Fold cross-validation030218 nuclear medicine & medical imagingk-nearest neighbors algorithmLesion03 medical and health sciences0302 clinical medicinetissue classificationmedicineRadiation treatment planningFuzzy C-Mean1707Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionimedicine.diagnostic_testbusiness.industryPattern recognitionComputer Graphics and Computer-Aided DesignPredictive valueSupport vector machineFuzzy C-MeansPositron emission tomography030220 oncology & carcinogenesisComputer Vision and Pattern RecognitionArtificial intelligencemedicine.symptombusinessPattern Recognition and Image Analysis
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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 2D segmentation method for uterine fibroid in MRgFUS treatment evaluation

2015

PurposeMagnetic Resonance guided Focused UltraSound (MRgFUS) represents a non-invasive surgical approach that uses thermal ablation to treat uterine fibroids. After the MRgFUS treatment, an operator must manually segment the treated fibroid areas to evaluate the NonPerfused Volume (NPV). This manual approach is operator-dependent, introducing issues of result reproducibility, which could lead to errors in the subsequent follow-up phase. Moreover, manual segmentation is time-consuming, and can have a negative impact on the optimization of both machine-time and operator-time. MethodTo address these issues, in this paper a novel fully automatic method based on the unsupervised Fuzzy C-Means cl…

medicine.medical_specialtyDatabases FactualUterine fibroidsComputer scienceAdaptive thresholdingImage ProcessingAdaptive thresholding; Automatic segmentation; Fuzzy C-Means clustering; MRgFUS treatment; Uterine fibroids; Female; Humans; Image Processing Computer-Assisted; Leiomyoma; Radiography; Algorithms; Databases Factual; Magnetic Resonance Imaging; Ultrasonography InterventionalHealth InformaticsFuzzy logicDatabasesComputer-AssistedImage Processing Computer-AssistedmedicineHumansSegmentationCluster analysisFactualUltrasonography InterventionalUltrasonographySettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniInterventionalLeiomyomaPixelbusiness.industryPattern recognitionmedicine.diseaseMagnetic Resonance ImagingFuzzy C-Means clusteringComputer Science ApplicationsSurgeryRadiographyTreatment evaluationMRgFUS treatmentFully automaticFemaleManual segmentationArtificial intelligenceAutomatic segmentationAdaptive thresholding Automatic segmentation Fuzzy C-Means clustering MRgFUS treatment Uterine fibroidsbusinessAlgorithmsUterine fibroids
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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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NeXt for neuro-radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique

2017

Stereotactic neuro-radiosurgery is a well-established therapy for intracranial diseases, especially brain metastases and highly invasive cancers that are difficult to treat with conventional surgery or radiotherapy. Nowadays, magnetic resonance imaging (MRI) is the most used modality in radiation therapy for soft-tissue anatomical districts, allowing for an accurate gross tumor volume (GTV) segmentation. Investigating also necrotic material within the whole tumor has significant clinical value in treatment planning and cancer progression assessment. These pathological necrotic regions are generally characterized by hypoxia, which is implicated in several aspects of tumor development and gro…

medicine.medical_specialtyPathologyING-INF/06 - BIOINGEGNERIA ELETTRONICA E INFORMATICAmedicine.medical_treatmentunsupervisedFuzzy C-Means clusteringBrain tumorRadiosurgeryING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI030218 nuclear medicine & medical imaging03 medical and health sciencesnecrosis extraction0302 clinical medicineMagnetic resonance imagingmedicineSegmentationElectrical and Electronic EngineeringRadiation treatment planningmedicine.diagnostic_testSettore INF/01 - Informaticabusiness.industryneuro-radiosurgery treatmentsNeuro-radiosurgery treatmentbrain tumors; magnetic resonance imaging; necrosis extraction; neuro-radiosurgery treatments; unsupervisedFuzzy C-Means clustering;brain tumors; magnetic resonance imaging; necrosis extraction; neuro-radiosurgery treatments; unsupervised Fuzzy C-Means clusteringCancerINF/01 - INFORMATICAMagnetic resonance imagingmedicine.diseaseElectronic Optical and Magnetic MaterialsRadiation therapyunsupervised Fuzzy C-Means clusteringBrain tumorUnsupervised learningbrain tumorsComputer Vision and Pattern RecognitionRadiologybusiness030217 neurology & neurosurgerySoftware
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Cluster-Based RF Fingerprint Positioning Using LTE and WLAN Outdoor Signals

2015

In this paper we evaluate user-equipment (UE) positioning performance of three cluster-based RF fingerprinting methods using LTE and WLAN signals. Real-life LTE and WLAN data were collected for the evaluation purpose using consumer cellular-mobile handset utilizing ‘Nemo Handy’ drive test software tool. Test results of cluster-based methods were compared to the conventional grid-based RF fingerprinting. The cluster-based methods do not require grid-cell layout and training signature formation as compared to the gridbased method. They utilize LTE cell-ID searching technique to reduce the search space for clustering operation. Thus UE position estimation is done in short time with less comput…

ta113PercentileK-nearest neighborComputer sciencebusiness.industrycell-IDFingerprint (computing)Real-time computingFingerprint recognitionGridHandsetlaw.inventionminimization of drive testsEuclidean distanceLTElawEmbedded systemgrid-based RF fingerprintingRadio frequencyCluster analysisbusinessfuzzy C-meanshierarchical clustering
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