Search results for "Texture analysis"

showing 10 items of 25 documents

Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art

2020

Breast cancer represents the most common malignancy in women, being one of the most frequent cause of cancer-related mortality. Ultrasound, mammography, and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of breast lesions, with different levels of accuracy. Particularly, dynamic contrast-enhanced MRI has shown high diagnostic value in detecting multifocal, multicentric, or contralateral breast cancers. Radiomics is emerging as a promising tool for quantitative tumor evaluation, allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities. Radiomics analysis may provide novel information through the quantification…

Dynamic contrastNuclear magnetic resonancemedicine.diagnostic_testRadiomicsbusiness.industrymedicineState of artMagnetic resonance imagingRadiomics Texture analysis Magnetic resonance imaging Dynamic contrast-enhanced-magnetic resonance imaging Breast CancerGeneral MedicinebusinessSettore MED/36 - Diagnostica Per Immagini E Radioterapia
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Radiomics Analyses of Schwannomas in the Head and Neck: A Preliminary Analysis

2022

The purpose of this preliminary study was to evaluate the differences in Magnetic Resonance Imaging (MRI)-based radiomics analysis between cerebellopontine angle neurinomas and schwannomas originating from other locations in the neck spaces. Twenty-six patients with available MRI exams and head and neck schwannomas were included. Lesions were manually segmented on the precontrast and postcontrast T1 sequences. The radiomics features were extracted by using PyRadiomics software, and a total of 120 radiomics features were obtained from each segmented tumor volume. An operator-independent hybrid descriptive‐inferential method was adopted for the selection and reduction of the features, while d…

Head and neck cancer Magnetic resonance imaging Radiomics Texture analysis
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Etude et modélisation du comportement des gouttelettes de produits phytosanitaires sur les feuilles de vignes par imagerie ultra-rapide et analyse de…

2013

In the domain of vineyard precision spraying research, one of the most importantobjectives is to minimize the volume of phytosanitary products ejected bya sprayer in order to be more environmentally respectful with more effectivevine leaf treatments. Unfortunaltely, even if lot of works have been carriedout at a parcel scale, mainly on losses caused by drift, less works have beencarried out at the leaf scale in order to understand which parameters influencethe spray quality. Since few years, recent improvements in image processing,sensitivity of imaging systems and cost reduction have increased the interestof high-speed imaging techniques. Analyzing the behavior of droplets afterimpact with…

High-speed imaging[SPI.OTHER]Engineering Sciences [physics]/Other[SDV.SA]Life Sciences [q-bio]/Agricultural sciences[SDV.SA] Life Sciences [q-bio]/Agricultural sciences[ SPI.OTHER ] Engineering Sciences [physics]/Other[SPI.OTHER] Engineering Sciences [physics]/OtherTrackingImagerie rapideAnalyse de texturesTexture analysisPulvérisationSprayingSuivi d’objets[ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences
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Development of an ultrasound-based muscle texture analysis as a potential imaging biomarker for frailty phenotype

2018

Las herramientas habituales para evaluar la fragilidad muestran, entre otras características, una baja sensibilidad y un bajo valor predictivo positivo. Es por eso que, en este estudio prospectivo-retrospectivo, nos preguntamos si es posible identificar y desarrollar biomarcadores cuantitativos a partir de imágenes de ultrasonido muscular, para la identificación de sujetos con riesgo de fragilidad. Para ello utlilizamos el análisis de textura de ecointensidad con ayuda del aprendizaje automático (machine learning, en inglés) como enfoque experimental para responder a esta pregunta. El proyecto se desarrolló en consulta externa, donde se realizó la ecografía muscular. Al final de la adquisic…

Machine LearningFrailtyUltrasoundDiagnóstico por imagenTexture AnalysisUltrasonidosInteligencia ArtificialEnvejecimiento de la poblaciónBiomarkers
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Differentiation between acute and chronic myocardial infarction by means of texture analysis of late gadolinium enhancement and cine cardiac magnetic…

2017

[EN] The purpose of this study was to differentiate acute from chronic myocardial infarction using machine learning techniques and texture features extracted from cardiac magnetic resonance imaging (MRI). The study group comprised 22 cases with acute myocardial infarction (AMI) and 22 cases with chronic myocardial infarction (CMI). Cine and late gadolinium enhancement (LGE) MRI were analyzed independently to differentiate AMI from CMI. A total of 279 texture features were extracted from predefined regions of interest (ROIs): the infarcted area on LGE MRI, and the entire myocardium on cine MRI. Classification performance was evaluated by a nested cross-validation approach combining a feature…

Malemedicine.medical_specialtySupport Vector MachineMyocardial InfarctionContrast MediaMagnetic Resonance Imaging CineInfarctionGadolinium030204 cardiovascular system & hematologySensitivity and Specificity030218 nuclear medicine & medical imagingDiagnosis DifferentialTECNOLOGIA ELECTRONICA03 medical and health sciences0302 clinical medicinePolynomial kernelCardiac magnetic resonance imagingmedicineHumansLate gadolinium enhancementRadiology Nuclear Medicine and imagingMyocardial infarctioncardiovascular diseasesCardiac MRIChronic myocardial infarctionReceiver operating characteristicmedicine.diagnostic_testbusiness.industryMyocardiumReproducibility of ResultsGeneral MedicineMiddle Agedmedicine.diseaseSupport vector machineClassification Myocardial infarctionROC CurveTexture analysisArea Under CurveAcute DiseaseChronic Diseasecardiovascular systemFemaleRadiologyNuclear medicinebusinessAlgorithmsMagnetic Resonance Angiography
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Everything matters: Molar microwear texture in goats (Capra aegagrus hircus) fed diets of different abrasiveness

2020

There is an ongoing discourse about whether or not external abrasives influence the microscopic wear in herbivore teeth, including a statement that “dust does not matter”. We submitted the maxillary and mandibular second molar of 28 goats (Capra aegagrus hircus) to dental microwear texture analysis (DMTA). The study animals were divided into four groups, which received diets of increasing phytolith-based abrasiveness (L: lucerne based pellets, very low phytolith abrasion diet, acting as control; G: grass-based pellets, medium abrasive phytolith diet; GR: grass and rice husk pellets, high abrasion phytolith diet), or a diet with added external abrasives (GRS: the GR diet with add…

Molar010506 paleontology10253 Department of Small AnimalsEvolutionPhytolith1904 Earth-Surface ProcessesGrazerGrit010502 geochemistry & geophysicsOceanography01 natural sciencesMesowearMesowearAnimal scienceBehavior and SystematicsGrazing1910 Oceanographymedia_common.cataloged_instanceEcology Evolution Behavior and Systematics0105 earth and related environmental sciencesEarth-Surface Processesmedia_common2. Zero hungerEnamel paintbiology630 AgricultureEcologyPalaeontologyTooth wearPaleontologyEarthbiology.organism_classification1911 Paleontology1105 Ecology Evolution Behavior and SystematicsTexture analysisConnochaetes taurinusSurface ProcessesTooth wearPhytolithvisual_artvisual_art.visual_art_medium570 Life sciences; biologyGeologyGiraffa camelopardalis
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Automatic recognition of tree species from 3D point clouds of forest plots

2014

The objective of the thesis is the automatic recognition of tree species from Terrestrial LiDAR data. This information is essential for forest inventory. As an answer, we propose different recognition methods based on the 3D geometric texture of the bark.These methods use the following processing steps: a preprocessing step, a segmentation step, a feature extraction step and a final classification step. They are based on the 3D data or on depth images built from 3D point clouds of tree trunks using a reference surface.We have investigated and tested several segmentation approaches on depth images representing the geometric texture of the bark. These approaches have the disadvantages of over…

Reconnaissance de formes 3DInventaire forestierAnalyse de texture 3DTree species recognitionIdentification des espèces d’arbres3D geometric texture analysisForest inventory3D pattern recognition[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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Using Temporal Texture for Content-Based Video Retrieval

2000

Textures evolving over time are called temporal textures and are very common in everyday life. Examples are the smoke flowing or the wavy water of a river. The idea explored in this paper is that image features based on temporal texture could allow a better performance of current content-based video retrieval systems that are mainly based on static characteristics of representative frames, like color and texture. To this aim we analyze the spatio-temporal nature of texture and its application in content-based access to video databases. In particular, we represent temporal texture using the spatio-temporal autoregressive (STAR) model and a variation of self-organizing maps (SOM) where each n…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniComputer sciencebusiness.industryNode (networking)ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONVariation (game tree)Star (graph theory)CBIR texture analysisTexture (geology)Language and LinguisticsComputer Science ApplicationsHuman-Computer InteractionAutoregressive modelImage textureComputer visionQuery by ExampleArtificial intelligencebusinessRepresentation (mathematics)computerComputingMethodologies_COMPUTERGRAPHICScomputer.programming_languageJournal of Visual Languages & Computing
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Texture classification for content-based image retrieval

2002

An original approach to texture-based classification of regions, for image indexing and retrieval, is presented. The system addresses automatic macro-textured ROI detection, and classification: we focus our attention on those objects that can be characterized by a texture as a whole, like trees, flowers, walls, clouds, and so on. The proposed architecture is based on the computation of the /spl lambda/ vector from each selected region, and classification of this feature by means of a pool of suitably trained support vector machines (SVM). This approach is an extension of the one previously developed by some of the authors to classify image regions on the basis of the geometrical shape of th…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniContextual image classificationComputer sciencebusiness.industryFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognitionImage segmentationContent-based image retrievalCBIR texture analysisObject detectionImage textureFeature (computer vision)Computer visionArtificial intelligencebusinessImage retrievalProceedings 11th International Conference on Image Analysis and Processing
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Automatic Volumetric Liver Segmentation Using Texture Based Region Growing

2010

In this paper an automatic texture based volumetric region growing method for liver segmentation is proposed. 3D seeded region growing is based on texture features with the automatic selection of the seed voxel inside the liver organ and the automatic threshold value computation for the region growing stop condition. Co-occurrence 3D texture features are extracted from CT abdominal volumes and the seeded region growing algorithm is based on statistics in the features space. Each CT volume is composed by 230 slices, having 512 x 512 pixels as spatial resolution, and 12-bit gray level resolution. In this initial feasible study, 5 healthy volunteer acquisitions has been used. Tests have been p…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioniliver texture analysis CT image segmentationPixelComputer sciencebusiness.industryFeature extractionImage segmentationcomputer.software_genreImage textureRegion growingVoxelSegmentationComputer visionArtificial intelligencebusinessSettore MED/36 - Diagnostica Per Immagini E RadioterapiaImage resolutioncomputer
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