Search results for "Fuzzy C-Means"

showing 10 items of 19 documents

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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Compaction of Open-Graded HMAs Evaluated by a Fuzzy Clustering Technique

2015

The aim of this paper is the proposal of an expeditious procedure to be used during the execution of an asphalt layer for improving the compaction task. This procedure, based on a fuzzy clustering technique, starts from the knowledge of some information recorded by ordinary measuring instruments and provides an aid to the decision-maker on the number of roller passes needed to achieve a specific density at a certain temperature. This result can be deduced with great rapidity during the paving operations on site without waiting for the time spent in the core extraction and in the subsequent laboratory analysis. In this way it is possible to identify more precisely which aspects of the execut…

Compaction Density Fuzzy C-means Hot mix asphaltFuzzy clusteringComputer scienceCompactionCompactionDensitycomputer.software_genreHot mix asphaltSpecific densityTask (project management)Asphalt pavementMeasuring instrumentSettore ICAR/04 - Strade Ferrovie Ed AeroportiData miningLayer (object-oriented design)Fuzzy C-meanscomputer
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Cluster-based RF fingerprint positioning using LTE and WLAN signal strengths

2017

Wireless Local Area Network (WLAN) positioning has become a popular localization system due to its low-cost installation and widespread availability of WLAN access points. Traditional grid-based radio frequency (RF) fingerprinting (GRFF) suffers from two drawbacks. First it requires costly and non-efficient data collection and updating procedure; secondly the method goes through time-consuming data pre-processing before it outputs user position. This paper proposes Cluster-based RF Fingerprinting (CRFF) to overcome these limitations by using modified Minimization of Drive Tests data which can be autonomously collected by cellular operators from their subscribers. The effect of environmental…

Computer Networks and CommunicationsComputer scienceReal-time computingK-means clustering02 engineering and technologySignallaw.inventionK-nearest neighbors0203 mechanical engineeringlaw0202 electrical engineering electronic engineering information engineeringfuzzy C-means clusteringWi-FiElectrical and Electronic EngineeringData collectionbusiness.industryFingerprint (computing)k-means clusteringRF fingerprint positioning020206 networking & telecommunications020302 automobile design & engineeringGridHardware and ArchitectureEmbedded systemMinificationRadio frequencybusinesshierarchical clustering
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Automated prostate gland segmentation based on an unsupervised fuzzy C-means clustering technique using multispectral T1w and T2w MR imaging

2017

Prostate imaging analysis is difficult in diagnosis, therapy, and staging of prostate cancer. In clinical practice, Magnetic Resonance Imaging (MRI) is increasingly used thanks to its morphologic and functional capabilities. However, manual detection and delineation of prostate gland on multispectral MRI data is currently a time-expensive and operator-dependent procedure. Efficient computer-assisted segmentation approaches are not yet able to address these issues, but rather have the potential to do so. In this paper, a novel automatic prostate MR image segmentation method based on the Fuzzy C-Means (FCM) clustering algorithm, which enables multispectral T1-weighted (T1w) and T2-weighted (T…

Computer scienceAutomated segmentation; Fuzzy C-Means clustering; Multispectral MR imaging; Prostate cancer; Prostate gland; Unsupervised machine learningMultispectral image02 engineering and technologyautomated segmentation; multispectral MR imaging; prostate gland; prostate cancer; unsupervised Machine Learning; Fuzzy C-Means clustering030218 nuclear medicine & medical imaging03 medical and health sciencesProstate cancer0302 clinical medicineProstate0202 electrical engineering electronic engineering information engineeringmedicineComputer visionSegmentationautomated segmentationunsupervised Machine LearningCluster analysisSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionimedicine.diagnostic_testbusiness.industryINF/01 - INFORMATICAMagnetic resonance imagingmedicine.diseaseprostate cancerFuzzy C-Means clusteringmultispectral MR imagingmedicine.anatomical_structureUnsupervised learning020201 artificial intelligence & image processingArtificial intelligencebusinessprostate glandInformation SystemsMultispectral segmentation
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Automatic skull stripping in MRI based on morphological filters and fuzzy c-means segmentation

2012

In this paper a new automatic skull stripping method for T1-weighted MR image of human brain is presented. Skull stripping is a process that allows to separate the brain from the rest of tissues. The proposed method is based on a 2D brain extraction making use of fuzzy c-means segmentation and morphological operators applied on transversal slices. The approach is extended to the 3D case, taking into account the result obtained from the preceding slice to solve the organ splitting problem. The proposed approach is compared with BET (Brain Extraction Tool) implemented in MRIcro software.

Computer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONSensitivity and SpecificityFuzzy logicPattern Recognition AutomatedFuzzy LogicImage Interpretation Computer-AssistedmedicineHumansSegmentationComputer visionSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionimedicine.diagnostic_testSkull Stripping Fuzzy C-means Morphological Filters.business.industrySkullProcess (computing)BrainReproducibility of ResultsMagnetic resonance imagingImage segmentationImage EnhancementMagnetic Resonance ImagingSubtraction TechniquePattern recognition (psychology)Skull strippingArtificial intelligenceMr imagesbusinessAlgorithms2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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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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Fully automatic multispectral MR image segmentation of prostate gland based on the fuzzy C-means clustering algorithm

2017

Prostate imaging is a very critical issue in the clinical practice, especially for diagnosis, therapy, and staging of prostate cancer. Magnetic Resonance Imaging (MRI) can provide both morphologic and complementary functional information of tumor region. Manual detection and segmentation of prostate gland and carcinoma on multispectral MRI data is not easily practicable in the clinical routine because of the long times required by experienced radiologists to analyze several types of imaging data. In this paper, a fully automatic image segmentation method, exploiting an unsupervised Fuzzy C-Means (FCM) clustering technique for multispectral T1-weighted and T2-weighted MRI data processing, is…

Computer scienceMultispectral imageFully automatic segmentation; Multispectral MR imaging; Prostate cancer; Prostate gland; Unsupervised fuzzy C-means clusteringFuzzy logic030218 nuclear medicine & medical imaging03 medical and health sciencesProstate cancer0302 clinical medicineProstatemedicineSegmentationComputer visionCluster analysismedicine.diagnostic_testbusiness.industryINF/01 - INFORMATICAMagnetic resonance imagingfully automatic segmentationImage segmentationmedicine.diseaseprostate cancermultispectral MR imagingunsupervised Fuzzy C-Means clusteringmedicine.anatomical_structureArtificial intelligencebusinessprostate gland030217 neurology & neurosurgery
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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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