Search results for "Cancer segmentation"

showing 3 items of 3 documents

K-nearest neighbor driving active contours to delineate biological tumor volumes

2019

Abstract An algorithm for tumor delineation in positron emission tomography (PET) is presented. Segmentation is achieved by a local active contour algorithm, integrated and optimized with the k-nearest neighbor (KNN) classification method, which takes advantage of the stratified k-fold cross-validation strategy. The proposed approach is evaluated considering the delineation of cancers located in different body districts (i.e. brain, head and neck, and lung), and considering different PET radioactive tracers. Data are pre-processed in order to be expressed in terms of standardized uptake value, the most widely used PET quantification index. The algorithm uses an initial, operator selected re…

0209 industrial biotechnologyK-nearest neighborComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONFDG and MET PET imagingStandardized uptake value02 engineering and technologyImaging phantomk-nearest neighbors algorithmActive contour algorithm020901 industrial engineering & automationArtificial IntelligenceRegion of interest0202 electrical engineering electronic engineering information engineeringSegmentationElectrical and Electronic EngineeringActive contour modelbusiness.industryProcess (computing)Pattern recognitionCancer segmentationBiological target volumeControl and Systems Engineering020201 artificial intelligence & image processingArtificial intelligencebusinessEnergy (signal processing)Engineering Applications of Artificial Intelligence
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A smart and operator independent system to delineate tumours in Positron Emission Tomography scans

2018

Abstract Positron Emission Tomography (PET) imaging has an enormous potential to improve radiation therapy treatment planning offering complementary functional information with respect to other anatomical imaging approaches. The aim of this study is to develop an operator independent, reliable, and clinically feasible system for biological tumour volume delineation from PET images. Under this design hypothesis, we combine several known approaches in an original way to deploy a system with a high level of automation. The proposed system automatically identifies the optimal region of interest around the tumour and performs a slice-by-slice marching local active contour segmentation. It automa…

Lung NeoplasmsComputer sciencemedicine.medical_treatmentPET imagingPattern Recognition Automated030218 nuclear medicine & medical imaging0302 clinical medicineNeoplasmsImage Processing Computer-AssistedSegmentationDiagnosis Computer-AssistedNeoplasm MetastasisRadiation treatment planningSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniObserver VariationActive contour modelmedicine.diagnostic_testBrain NeoplasmsPhantoms ImagingComputer Science ApplicationsHead and Neck NeoplasmsPositron emission tomography030220 oncology & carcinogenesis18F-fluoro-2-deoxy-d-glucoseAlgorithms18F-fluoro-2-deoxy-d-glucose and 11C-labeled methionine PET imagingSimilarity (geometry)Health InformaticsSensitivity and SpecificityNOActive contour algorithm03 medical and health sciencesFluorodeoxyglucose F18Predictive Value of TestsRegion of interestmedicineHumansFalse Positive ReactionsRetrospective Studies18F-fluoro-2-deoxy-d-glucose 11C-labeled methionine PET imaging Active contour algorithm Biological target volume Cancer segmentationbusiness.industryRadiotherapy Planning Computer-Assisted11C-labeled methionineReproducibility of ResultsPattern recognitionGold standard (test)Cancer segmentationRadiation therapyBiological target volumePositron-Emission TomographyArtificial intelligenceTomography X-Ray ComputedbusinessSoftwareComputers in Biology and Medicine
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An enhanced random walk algorithm for delineation of head and neck cancers in PET studies

2017

An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies:…

Similarity (geometry)Computer sciencePET imagingBiomedical EngineeringRandom walk030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicinemedicineImage Processing Computer-AssistedHumansSegmentationComputer visionCluster analysisEvent (probability theory)Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionimedicine.diagnostic_testbusiness.industryPhantoms ImagingBiological target volume; Head and neck cancer segmentation; PET imaging; Random walksComputer Science ApplicationPattern recognitionRandom walkComputer Science ApplicationsBiological target volumeHausdorff distancePositron emission tomographyHead and Neck Neoplasms030220 oncology & carcinogenesisPositron-Emission TomographyArtificial intelligenceHead and neck cancer segmentationComputer Vision and Pattern RecognitionbusinessAlgorithmsBiological target volume Head and neck cancer segmentation PET imaging Random walks Algorithms Head and Neck Neoplasms Humans Image Processing Computer-Assisted Phantoms Imaging Positron-Emission TomographyVolume (compression)
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