0000000000677121

AUTHOR

Sc Cheran

showing 3 related works from this author

Distributed medical images analysis on a Grid infrastructure

2007

In this paper medical applications on a Grid infrastructure, the MAGIC-5 Project, are presented and discussed. MAGIC-5 aims at developing Computer Aided Detection (CADe) software for the analysis of medical images on distributed databases by means of GRID Services. The use of automated systems for analyzing medical images improves radiologists’ performance; in addition, it could be of paramount importance in screening programs, due to the huge amount of data to check and the cost of related manpower. The need for acquiring and analyzing data stored in different locations requires the use of Grid Services for the management of distributed computing resources and data. Grid technologies allow…

GRID; Virtual Organization; Medical ApplicationsComputer Networks and CommunicationsComputer scienceVirtual organizationmammographyComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONcomputer.software_genreGRID; virtual organization; CAD; mammography; medical applicationsSoftwareComputer aided diagnosimedicineMammographyCADComputer visionGridLung tumorDistributed databasemedicine.diagnostic_testmedical applicationsbusiness.industryDigital imagingGridDigital imagingHardware and ArchitectureImage analysiArtificial intelligenceData miningAlzheimer diseasevirtual organizationGRIDbusinesscomputerSoftwareMammography
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A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model

2007

A computer-aided detection (CAD) system for the selection of lung nodules in computer tomography (CT) images is presented. The system is based on region growing (RG) algorithms and a new active contour model (ACM), implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: (1) the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; (2) a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; (3) a double-threshold cut and a neural network are applied to reduce…

medicine.medical_specialtyLung NeoplasmsRadiation DosageModels BiologicalEdge detectionImage processingMedical imagingmedicineHumansDiagnosis Computer-AssistedComputed radiographycomputer-aided diagnosis (CAD)Lungimage segmentationComputed tomographyActive contour modelImage segmentationbusiness.industrycomputed tomographyGeneral MedicineImage segmentationComputer-aided diagnosis (CAD)image processingROC CurveRegion growingComputer-aided diagnosisRadiologyTomographyNeural Networks Computercomputer-aided diagnosis (CAD)image processingcomputed tomographyimage segmentationNuclear medicinebusinessTomography X-Ray ComputedAlgorithms
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A completely automated CAD system for mass detection in a large mammographic database.

2006

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing secon…

Databases FactualInformation Storage and RetrievalReproducibility of ResultsBreast NeoplasmsSensitivity and SpecificityNeural networkPattern Recognition AutomatedRadiographic Image EnhancementBreast cancerTextural featuresRadiology Information SystemsImage processingComputer-aided detection (CAD)Artificial IntelligenceCluster AnalysisDatabase Management SystemsHumansRadiographic Image Interpretation Computer-AssistedFemaleBreast cancer; Computer-aided detection (CAD); Image processing; Mammographic mass detection; Neural network; Textural featuresMammographic mass detectionAlgorithmsMammographyMedical physics
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