Search results for " C-means"

showing 10 items of 21 documents

Using anatomic and metabolic imaging in stereotactic radio neuro-surgery treatments

2016

PET/MR imagingmedicine.medical_specialtyNeuro-radiosurgerybusiness.industryMetabolic imagingBiophysicsGeneral Physics and AstronomyGeneral MedicineRandom Walker algorithmFuzzy C-Means clustering030218 nuclear medicine & medical imagingBrain tumor03 medical and health sciences0302 clinical medicineRandom walker algorithm030220 oncology & carcinogenesismedicineRadiology Nuclear Medicine and imagingNeurosurgeryRadiologyPet mr imagingbusinessNuclear medicine
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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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Automatic Detection of Hemangioma through a Cascade of Self-organizing Map Clustering and Morphological Operators

2016

Abstract In this paper we propose a method for the automatic detection of hemangioma regions, consisting of a cascade of algorithms: a Self Organizing Map (SOM) for clustering the image pixels in 25 classes (using a 5x5 output layer) followed by a morphological method of reducing the number of classes (MMRNC) to only two classes: hemangioma and non-hemangioma. We named this method SOM-MMRNC. To evaluate the performance of the proposed method we have used Fuzzy C-means (FCM) for comparison. The algorithms were tested on 33 images; for most images, the proposed method and FCM obtain similar overall scores, within one percent of each other. However, in about 18% of the cases, there is a signif…

Self-organizing mapComputer science050801 communication & media studies02 engineering and technologycomputer.software_genreFuzzy logicImage (mathematics)Hemangioma0508 media and communications0202 electrical engineering electronic engineering information engineeringmedicineLayer (object-oriented design)Cluster analysisFuzzy C-meansGeneral Environmental SciencePixelbusiness.industry05 social sciencesPattern recognitionmedicine.diseasehemangiomaCascadeGeneral Earth and Planetary Sciences020201 artificial intelligence & image processingArtificial intelligenceData miningbusinesscomputerSelf Organizing MapclusteringProcedia Computer Science
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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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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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