Search results for "Texture Analysis"

showing 10 items of 25 documents

Why should traceology learn from dental microwear, and vice-versa?

2019

Dental and artifact microwear analyses have a lot in common regarding the questions they address, their developmental history and their issues. However, few paleontologists and archeologists are aware of this, and even those who are, do not take into account most of the methodological insights from the other field. In this focus article, we briefly review the main developmental steps of both methods, highlight how similar their histories are and how combining methodological developments can improve both research fields. In both cases, the traditional analyses have been strongly criticized mainly because of their subjectivity and their lack of repeatability and reproducibility. Quantitative …

010506 paleontologyArtifact (archaeology)ArcheologyTeeth060102 archaeologyPaleontology06 humanities and the arts01 natural sciencesData scienceField (computer science)Confocal microscopyDental microwear texture analysisQuantitative surface texture analysis0601 history and archaeologyPsychologyArtifacts0105 earth and related environmental sciences
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Protocol for the Definition of a Multi-Spectral Sensor for Specific Foliar Disease Detection: Case of “Flavescence Dorée”

2018

Flavescence Doree (FD) is a contagious and incurable grapevine disease that can be perceived on leaves. In order to contain its spread, the regulations obligate winegrowers to control each plant and to remove the suspected ones. Nevertheless, this monitoring is performed during the harvest and mobilizes many people during a strategic period for viticulture. To solve this problem, we aim to develop a Multi-Spectral (MS) imaging device ensuring an automated grapevine disease detection solution. If embedded on a UAV, the tool can provide disease outbreaks locations in a geographical information system allowing localized and direct treatment of infected vines. The high-resolution MS camera aims…

0106 biological sciences[SDE] Environmental SciencesDisease detectionComputer science[SDV]Life Sciences [q-bio]Multispectral imageradiometric/geometric correctionsFeature selectionMulti spectral01 natural sciencesfeature selection[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biologytexture analysisProtocol (science)Artificial neural networkbusiness.industrymultispectral sensorOutbreakPattern recognition04 agricultural and veterinary sciencesFlavescence Dorée3. Good health[SDV] Life Sciences [q-bio]Identification (information)classification[SDE]Environmental Sciences040103 agronomy & agriculture0401 agriculture forestry and fisheriesFlavescence doréeArtificial intelligencebusiness010606 plant biology & botany
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Radiomics predicts survival of patients with advanced non-small cell lung cancer undergoing PD-1 blockade using Nivolumab

2019

Immune checkpoint blockade is an emerging anticancer strategy, and Nivolumab is a human mAb to PD-1 that is used in the treatment of a number of different malignancies, including non-small cell lung cancer (NSCLC), kidney cancer, urothelial carcinoma and melanoma. Although the use of Nivolumab prolongs survival in a number of patients, this treatment is hampered by high cost. Therefore, the identification of predictive markers of response to treatment in patients is required. In this context, PD-1/PDL1 blockade antitumor effects occur through the reactivation of a pre-existing immune response, and the efficacy of these effects is strictly associated with the presence of necrosis, hypoxia an…

0301 basic medicineOncologyCancer Researchmedicine.medical_specialtySurvivalImmunology03 medical and health sciences0302 clinical medicineNon-small cell lung cancerInternal medicinemedicineProgression-free survivalLung cancerPathologicalProgrammed cell death protein 1business.industryMelanomaRetrospective cohort studyArticlesmedicine.diseaseBlockade030104 developmental biologyNivolumabOncologyTexture analysis030220 oncology & carcinogenesisNivolumabRadiomicbusinessKidney cancer
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Characterization of bread breakdown during mastication by image texture analysis

2012

National audience; Reducing sodium intake in Western diet is advised for reducing hypertension and risk of developing cardiovascular disease. Sodium is mainly consumed via processed foods and among them breads are one of the most important sources of salt [1]. The present study investigated the changes in mastication and salivation for bread samples with different composition and texture, their respective contribution to food bolus formation, and the impact on salt release. The study set-up included five subjects presenting different chewing efficiency and four different breads from different composition and structure (two French baguettes (bakery (BB) and supermarket (BS)), a toast bread (…

2. Zero hunger0303 health sciences030309 nutrition & dietetics[ SDV.AEN ] Life Sciences [q-bio]/Food and Nutritiondigestive oral and skin physiologymasticationbreadfood and beverages04 agricultural and veterinary sciences040401 food science03 medical and health sciences[SDV.AEN] Life Sciences [q-bio]/Food and Nutrition0404 agricultural biotechnologyImage texturestomatognathic systemimage texture analysisGrey levelFood scienceglcmMastication[SDV.AEN]Life Sciences [q-bio]/Food and NutritionFood ScienceMathematics
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Archetypal analysis: an alternative to clustering for unsupervised texture segmentation

2019

Texture segmentation is one of the main tasks in image applications, specifically in remote sensing, where the objective is to segment high-resolution images of natural landscapes into different cover types. Often the focus is on the selection of discriminant textural features, and although these are really fundamental, there is another part of the process that is also influential, partitioning different homogeneous textures into groups. A methodology based on archetype analysis (AA) of the local textural measurements is proposed. AA seeks the purest textures in the image and it can find the borders between pure textures, as those regions composed of mixtures of several archetypes. The prop…

Acoustics and UltrasonicsComputer scienceMaterials Science (miscellaneous)General MathematicsComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologylocal granulometriesMathematical morphology01 natural sciencesTexture (geology)archetypeImage (mathematics)010104 statistics & probability0202 electrical engineering electronic engineering information engineeringRadiology Nuclear Medicine and imagingSegmentationmathematical morphology0101 mathematicsCluster analysisInstrumentationimage segmentationtexture analysislcsh:R5-920business.industrylcsh:MathematicsPattern recognitionImage segmentationlcsh:QA1-939DiscriminantSignal Processing020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligenceFocus (optics)businesslcsh:Medicine (General)Biotechnology
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MRI radiomics-based machine-learning classification of bone chondrosarcoma.

2019

Abstract Purpose To evaluate the diagnostic performance of machine learning for discrimination between low-grade and high-grade cartilaginous bone tumors based on radiomic parameters extracted from unenhanced magnetic resonance imaging (MRI). Methods We retrospectively enrolled 58 patients with histologically-proven low-grade/atypical cartilaginous tumor of the appendicular skeleton (n = 26) or higher-grade chondrosarcoma (n = 32, including 16 appendicular and 16 axial lesions). They were randomly divided into training (n = 42) and test (n = 16) groups for model tuning and testing, respectively. All tumors were manually segmented on T1-weighted and T2-weighted images by drawing bidimensiona…

AdultMalemedicine.medical_specialtyArtificial intelligenceAppendicular skeletonChondrosarcomaFeature selectionBone NeoplasmsBone and BonesMachine LearningImage Interpretation Computer-AssistedmedicineHumansRadiology Nuclear Medicine and imagingRetrospective StudiesLearning classifier systemReceiver operating characteristicmedicine.diagnostic_testbusiness.industryReproducibility of ResultsMagnetic resonance imagingGeneral MedicineMiddle Agedmedicine.diseaseMagnetic Resonance ImagingRandom forestStatistical classificationmedicine.anatomical_structureTexture analysisROC CurveCartilaginous tumorFemaleRadiologyChondrosarcomaRadiomicNeoplasm GradingbusinessEuropean journal of radiology
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Whole-body MRI radiomics model to predict relapsed/refractory Hodgkin Lymphoma: A preliminary study.

2022

Purpose A strong prognostic score that enables a stratification of newly diagnosed Hodgkin Lymphoma (HL) to identify patients at high risk of refractory/relapsed disease is still needed. Our aim was to investigate the potential value of a radiomics analysis pipeline from whole-body MRI (WB-MRI) exams for clinical outcome prediction in patients with Hodgkin Lymphoma (HL). Materials and methods Index lesions from baseline WB-MRIs of 40 patients (22 females; mean age 31.7 ± 11.4 years) with newly diagnosed HL treated by ABVD chemotherapy regimen were manually segmented on T1-weighted, STIR, and DWI images for texture analysis feature extraction. A machine learning approach based on the Extra T…

AdultPositron emission tomographymedicine.medical_specialtyWhole body mriBiomedical EngineeringBiophysicsVinblastineBleomycinYoung AdultRefractoryRadiomicsPositron Emission Tomography Computed TomographyMachine learningAntineoplastic Combined Chemotherapy ProtocolsMedicineHumansRadiology Nuclear Medicine and imagingMagnetic resonance imaging Positron emission tomography Machine learning Texture analysis Hodgkin Lymphomamedicine.diagnostic_testHodgkin Lymphomabusiness.industryMagnetic resonance imagingMetabolic tumor volumeHodgkin DiseaseMagnetic Resonance ImagingDacarbazineTexture analysisPositron emission tomographyDoxorubicinRelapsed refractoryHodgkin lymphomaFemaleRadiologySettore MED/36 - Diagnostica Per Immagini E RadioterapiabusinessMagnetic resonance imaging
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Effects of Interobserver Variability on 2D and 3D CT- and MRI-Based Texture Feature Reproducibility of Cartilaginous Bone Tumors

2021

AbstractThis study aims to investigate the influence of interobserver manual segmentation variability on the reproducibility of 2D and 3D unenhanced computed tomography (CT)- and magnetic resonance imaging (MRI)-based texture analysis. Thirty patients with cartilaginous bone tumors (10 enchondromas, 10 atypical cartilaginous tumors, 10 chondrosarcomas) were retrospectively included. Three radiologists independently performed manual contour-focused segmentation on unenhanced CT and T1-weighted and T2-weighted MRI by drawing both a 2D region of interest (ROI) on the slice showing the largest tumor area and a 3D ROI including the whole tumor volume. Additionally, a marginal erosion was applied…

Artificial intelligenceFuture studiesIntraclass correlationChondrosarcomaBone NeoplasmsArticleRegion of interestNeoplasmsArtificial intelligence Chondroma Chondrosarcoma Neoplasms Radiomics Texture analysisHumansMedicineRadiology Nuclear Medicine and imagingSegmentationTexture featureRetrospective StudiesObserver VariationReproducibilityRadiomicsRadiological and Ultrasound Technologymedicine.diagnostic_testbusiness.industryReproducibility of ResultsMagnetic resonance imagingmedicine.diseaseMagnetic Resonance ImagingComputer Science ApplicationsTexture analysisFeature (computer vision)ChondrosarcomaTomography X-Ray ComputedbusinessNuclear medicineChondromaChondromaJournal of Digital Imaging
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A Predictive System to Classify Preoperative Grading of Rectal Cancer Using Radiomics Features

2022

Although preoperative biopsy of rectal cancer (RC) is an essential step for confirmation of diagnosis, it currently fails to provide prognostic information to the clinician beyond a rough estimation of tumour grade. In this study we used a risk classification to stratified patient in low-risk and high-risk patients in relation to the disease free survival and the overall survival using histopathological post-operative features. The purpose of this study was to evaluate if low-risk and high-risk RC can be distinguished using a CT-based radiomics model. We retrospectively reviewed the preoperative abdominal contrast-enhanced CT of 40 patients with RC. CT portal-venous phase was used for manua…

Computed tomography Radiomics Rectal cancer Texture analysis
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Complex networks : application for texture characterization and classification

2008

This article describes a new method and approch of texture characterization. Using complex network representation of an image, classical and derived (hierarchical) measurements, we presente how to have good performance in texture classification. Image is represented by a complex networks : one pixel as a node. Node degree and clustering coefficient, using with traditionnal and extended hierarchical measurements, are used to characterize ”organisation” of textures.

Computer engineering. Computer hardwareTexture compressionComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONComplex networksImage processingTexture (geology)TK7885-7895Image textureImage processingAnàlisi de texturaProcesamiento de imágenestexture analysisClustering coefficientAnálisis de texturaRedes complejasPixelbusiness.industryNode (networking)Pattern recognitionProcessament d'imatgescomplex networksQA75.5-76.95Xarxes complexesComplex networkTexture analysisElectronic computers. Computer scienceComputer Science::Computer Vision and Pattern RecognitionComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftwareELCVIA: electronic letters on computer vision and image analysis
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