Search results for "Active contours model"

showing 4 items of 4 documents

Automatic Segmentation of HEp-2 Cells Based on Active Contours Model

2018

In the past years, a great deal of effort was put into research regarding Indirect Immunofluorescence techniques with the aim of development of CAD systems. In this work a method for segmenting HEp-2 cells in IIF images is presented. Such task is one of the most challenging of automated IIF analysis, because the segmentation algorithm has to cope with a large heterogeneity of shapes and textures. In order to address this problem, numerous techniques and their combinations were evaluated, in a process aimed at maximizing the figure of merit. The proposed method, for a greater definition of cellular contours, uses the active contours in the last phase of the process. The initial conditions, c…

Active contour modelComputer sciencebusiness.industryHEp-2 cellComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONProcess (computing)Pattern recognitionEllipseDice indexSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Hough transformlaw.inventionRandomized Hough transformHough transformlawPosition (vector)Convergence (routing)SegmentationArtificial intelligencebusinessActive contours modelCells segmentationIIF imagesProceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing
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An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

2019

The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. The most common methods to determine ANA are indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic system able to characterize IIF images. The fluorescence intensity classification was obtained by performing an image preprocessing phase and implementing a Support Vector Machines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentati…

Computer scienceSVMKNN02 engineering and technologylcsh:TechnologyIIF imageHough transformlaw.inventionlcsh:Chemistry03 medical and health scienceslawClassifier (linguistics)0202 electrical engineering electronic engineering information engineeringPreprocessorGeneral Materials ScienceSegmentationcell segmentationlcsh:QH301-705.5InstrumentationIIF images030304 developmental biologyFluid Flow and Transfer Processes0303 health sciencesIndirect immunofluorescencelcsh:Tbusiness.industryProcess Chemistry and TechnologyGeneral EngineeringPattern recognitionSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)ROC curvelcsh:QC1-999Computer Science ApplicationsSupport vector machineParameter identification problemFluorescence intensityHough transformlcsh:Biology (General)lcsh:QD1-999lcsh:TA1-2040020201 artificial intelligence & image processingArtificial intelligencelcsh:Engineering (General). Civil engineering (General)businesslcsh:Physicsactive contours modelApplied Sciences
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Automatic detection of lung nodules in CT datasets based on stable 3D mass–spring models

2012

We propose a computer-aided detection (CAD) system which can detect small-sized (from 3 mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung cancer and their early stage detection arguably improves the patient survival rate. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the full analysis difficult, leading to omission of nodules by the radiologist. We…

medicine.medical_specialtyLung NeoplasmsDatabases FactualHealth InformaticsCADModels BiologicalSensitivity and SpecificityImaging Three-DimensionalSegmentationLung nodulemedicineFalse positive paradoxSegmentation; Lung nodules; Active contours models;Computer tomography (CT); Mass–spring models; Spline curves; Image featuresHumansSegmentationDiagnosis Computer-AssistedStage (cooking)Lung cancerComputer tomography (CT)business.industryNodule (medicine)Image featuresSpline curvemedicine.diseaseSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Computer Science ApplicationsRegion growingMass–spring modelRadiologyTomographymedicine.symptombusinessTomography Spiral ComputedAlgorithmsActive contours model
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DEVELOPMENT AND IMPLEMENTATION OF MACHINE LEARNING METHODS FOR THE IIF IMAGES ANALYSIS

2021

FEATURES EXTRACTIONSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniACTIVE CONTOURS MODELFINE-TUNINGDEEP LEARNINGSettore ING-INF/03 - TelecomunicazioniSVMHOUGH TRANSFORMMULTI-CLASS CLASSIFICATIONHEP-2 CELLSIMAGE PREPROCESSINGAUTOIMMUNE DISEASESMACHINE LEARNINGCELLS SEGMENTATIONROC CURVECNNIIF TEST
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