Search results for "Anisotropic filtering"

showing 2 items of 2 documents

Isolation of the left atrial surface from cardiac multi-detector CT images based on marker controlled watershed segmentation

2006

The delineation of left atrium (LA) and pulmonary veins (PVs) anatomy from high resolution images holds importance for atrial fibrillation (AF) investigation and treatment. In this study, a semiautomatic segmentation procedure for LA and PVs inner surface from contrast enhanced CT data was developed. The procedure consists of a three dimensional marker controlled watershed segmentation applied to the external morphological gradient, followed by variable threshold surface extraction from the original intensity image. A preliminary anisotropic non-linear filtering was implemented to improve the S/N ratio of CT images. The performance of segmentation was evaluated on cardiac CT scans of 12 AF …

Morphological gradientPsychology (all)Computer scienceBiomedical EngineeringBiophysicsContrast MediaMathematical morphologycomputer.software_genreSensitivity and SpecificityPattern Recognition AutomatedImaging Three-DimensionalVoxelAtrial FibrillationHumansSegmentationOrthopedics and Sports MedicineHeart AtriaComputed tomographyAnisotropic filteringImage segmentationAnisotropic filteringModels CardiovascularReproducibility of ResultsGold standard (test)Image segmentationAnatomy RegionalWatershedIntensity (physics)Pulmonary VeinsMathematical morphologySubtraction TechniqueLeft atriumSettore ING-INF/06 - Bioingegneria Elettronica E Informaticacardiovascular systemTomography X-Ray ComputedcomputerBiomedical engineering
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Vector anisotropic filter for multispectral image denoising

2015

In this paper, we propose an approach to extend the application of anisotropic Gaussian filtering for multi- spectral image denoising. We study the case of images corrupted with additive Gaussian noise and use sparse matrix transform for noise covariance matrix estimation. Specifically we show that if an image has a low local variability, we can make the assumption that in the noisy image, the local variability originates from the noise variance only. We apply the proposed approach for the denoising of multispectral images corrupted by noise and compare the proposed method with some existing methods. Results demonstrate an improvement in the denoising performance.

Covariance matrixbusiness.industryNoise reductionMultispectral imageComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognitionNon-local meansNoisesymbols.namesakeGaussian noiseComputer Science::Computer Vision and Pattern RecognitionsymbolsComputer visionVideo denoisingArtificial intelligencebusinessMathematicsAnisotropic filteringTwelfth International Conference on Quality Control by Artificial Vision 2015
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