Search results for "C-mean"

showing 10 items of 27 documents

Fuzzy technique for microcalcifications clustering in digital mammograms

2012

Abstract Background Mammography has established itself as the most efficient technique for the identification of the pathological breast lesions. Among the various types of lesions, microcalcifications are the most difficult to identify since they are quite small (0.1-1.0 mm) and often poorly contrasted against an images background. Within this context, the Computer Aided Detection (CAD) systems could turn out to be very useful in breast cancer control. Methods In this paper we present a potentially powerful microcalcifications cluster enhancement method applicable to digital mammograms. The segmentation phase employs a form filter, obtained from LoG filter, to overcome the dependence from …

Databases FactualMicrocalcificationsBreast NeoplasmsContext (language use)CADcomputer.software_genreSensitivity and SpecificityFuzzy logicClusteringBreast cancerSegmentationBreast cancerC-meansImage Processing Computer-AssistedmedicineCluster AnalysisHumansMammographyRadiology Nuclear Medicine and imagingSegmentationCluster analysisSpatial filtersmedicine.diagnostic_testMultimediabusiness.industryCalcinosisPattern recognitionmedicine.diseaseSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Computer aided detectionFuzzy logicRadiology Nuclear Medicine and imagingFemaleArtificial intelligencebusinesscomputerAlgorithmsMammographyResearch ArticleBreast cancer Microcalcifications Spatial filters Clustering Fuzzy logic C-means Mammography SegmentationBMC Medical Imaging
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An Heuristic Approach for the Training Dataset Selection in Fingerprint Classification Tasks

2015

Fingerprint classification is a key issue in automatic fingerprint identification systems. It aims to reduce the item search time within the fingerprint database without affecting the accuracy rate. In this paper an heuristic approach using only the directional image information for the training dataset selection in fingerprint classification tasks is described. The method combines a Fuzzy C-Means clustering method and a Naive Bayes Classifier and it is composed of three modules: the first module builds the working datasets, the second module extracts the training images dataset and, finally, the third module classifies fingerprint images in four classes. Unlike literature approaches using …

Directional imageFingerprint classificationComputer sciencebusiness.industryHeuristicNaive bayes classifierTraining dataset optimizationPattern recognitionBayes classifiercomputer.software_genreClass (biology)Fuzzy logicNaive Bayes classifierComputingMethodologies_PATTERNRECOGNITIONFingerprintArtificial intelligenceData miningCluster analysisbusinesscomputerSelection (genetic algorithm)Fuzzy C-Mean
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A Novel Technique for Fingerprint Classification based on Fuzzy C-Means and Naive Bayes Classifier

2014

Fingerprint classification is a key issue in automatic fingerprint identification systems. One of the main goals is to reduce the item search time within the fingerprint database without affecting the accuracy rate. In this paper, a novel technique, based on topological information, for efficient fingerprint classification is described. The proposed system is composed of two independent modules: the former module, based on Fuzzy C-Means, extracts the best set of training images, the latter module, based on Fuzzy C-Means and Naive Bayes classifier, assigns a class to each processed fingerprint using only directional image information. The proposed approach does not require any image enhancem…

Novel techniqueSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniComputer sciencebusiness.industryPattern recognitioncomputer.software_genreClass (biology)Fuzzy logicImage (mathematics)Set (abstract data type)Naive Bayes classifierFingerprintKey (cryptography)Artificial intelligenceData miningbusinessFingerprint Classification Directional Images Fuzzy C-Means Naive Bayes Classifiercomputer
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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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A multiscale approach to automatic and unsupervised retinal vessel segmentation using Self-Organizing Maps

2016

In this paper an automatic unsupervised method for retinal vessel segmentation is described. Self-Organizing Map, modified Fuzzy C-Means, STAPLE algorithms and majority voting strategy were adopted to identify a segmentation of the retinal vessels. The performance of the proposed method was evaluated on the DRIVE database.

Self-organizing mapMajority ruleComputer science0206 medical engineeringComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologySelf-organizing mapFuzzy logicCLAHE030218 nuclear medicine & medical imagingRetinal vessel03 medical and health scienceschemistry.chemical_compound0302 clinical medicineMajority votingSegmentationComputer visionComputingMethodologies_COMPUTERGRAPHICSFuzzy C-Mean1707Settore INF/01 - Informaticabusiness.industrySTAPLERetinal020601 biomedical engineeringRetinal vesselHuman-Computer InteractionComputer Networks and CommunicationchemistryAdaptive histogram equalizationArtificial intelligencebusinessSoftware
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A hybrid approach to semantic web services matchmaking

2008

AbstractDeploying the semantics embedded in web services is a mandatory step in the automation of discovery, invocation and composition activities. The semantic annotation is the “add-on” to cope with the actual interoperability limitations and to assure a valid support to the interpretation of services capabilities. Nevertheless many issues have to be reached to support semantics in the web services and to guarantee accurate functionality descriptions. Early efforts address automatic matchmaking tasks, in order to find eligible advertised services which appropriately meet the consumer’s demand. In the most of approaches, this activity is often entrusted to software agents, able to drive re…

Service (business)Computer scienceWS-I Basic Profilebusiness.industryApplied MathematicsServices computingSemantic web servicesOntology web language service (OWLS)Agentscomputer.software_genreSocial Semantic WebTheoretical Computer ScienceWorld Wide WebHybrid systemFuzzy C-mean (FCM) clusteringArtificial IntelligenceFuzzy multisetSemantic Web StackWeb serviceWS-PolicybusinessSemantic WebcomputerSoftwareInternational Journal of Approximate Reasoning
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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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