Search results for "Brain segmentation"

showing 3 items of 3 documents

Segmentation of MR brain images with bias artifact

2009

Brain MR Images corrupted by RF- Inhomogeneity (bias artifact) exhibit brightness variations across the image. As a consequence, a standard Fuzzy C-Means (fern) segmentation algorithm may fail. In this work we show a new general-purpose bias removing algorithm, which can be used as a pre-processing step for a fern segmentation. We also compare our experimental results with the ones achieved by using E2 D - H U M filter, showing an improvement in brain segmentation and bias removal.

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniArtifact (error)Computer sciencebusiness.industryLow-pass filterScale-space segmentationPattern recognitionFilter (signal processing)Image segmentationMedical imagingBrain segmentationRF-Inhomogeneity bias segmentation Halo MRlComputer visionSegmentationArtificial intelligencebusiness
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Unsupervised tissue classification of brain MR images for voxel-based morphometry analysis

2016

In this article, a fully unsupervised method for brain tissue segmentation of T1-weighted MRI 3D volumes is proposed. The method uses the Fuzzy C-Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro-radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial-and-error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro-Spinal Fluid in …

Fuzzy clusteringComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONcomputer.software_genreFuzzy logicImaging phantom030218 nuclear medicine & medical imaging03 medical and health sciencesbrain images segmentation0302 clinical medicinevoxel-based morphometryBrain segmentationSegmentationElectrical and Electronic EngineeringCluster analysisSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniArtificial neural networkbusiness.industryUsabilityneural networksElectronic Optical and Magnetic MaterialsComputingMethodologies_PATTERNRECOGNITIONfuzzy clusteringunsupervised tissues classificationComputer Vision and Pattern RecognitionData miningbusinesscomputer030217 neurology & neurosurgerySoftwareInternational Journal of Imaging Systems and Technology
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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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