Search results for "computer-aided detection"

showing 4 items of 4 documents

A Fuzzy Logic C-Means Clustering Algorithm to Enhance Microcalcifications Clusters in Digital Mammograms

2011

The detection of microcalcifications is a hard task, since they are quite small and often poorly contrasted against the background of images. The Computer Aided Detection (CAD) systems could be very useful for breast cancer control. In this paper, we report a method to enhance microcalcifications cluster in digital mammograms. A Fuzzy Logic clustering algorithm with a set of features is used for clustering microcalcifications. The method described was tested on simulated clusters of microcalcifications, so that the location of the cluster within the breast and the exact number of microcalcifications is known.

C-meanCOMPUTER-AIDED DETECTIONComputer scienceCADFuzzy logicSet (abstract data type)Cluster (physics)medicineMammographycancerComputer visionCLASSIFICATION.Cluster analysisbreastmedicine.diagnostic_testbusiness.industryPattern recognitionImage enhancementComputer aided detectionSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)microcalcificationComputingMethodologies_PATTERNRECOGNITIONbreast; cancer; microcalcifications; clustering; fuzzy logic; C-means; COMPUTER-AIDED DETECTION; CLASSIFICATION.Artificial intelligencefuzzy logicbusinessclustering
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Automated detection of lung nodules in low-dose computed tomography

2007

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector computed-tomography (CT) images has been developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lung-CAD system, consisting in a 3D dot-enhancement filter for nodule detection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The database used in this study consists of 17 low…

Computer-aided detectionLow-dose computed tomography (LDCT)Computer-aided detection (CAD)thin slice CTLung cancer screeninglung cancer screeningFOS: Physical sciencesComputer-aided detection (CAD); Low-dose computed tomography (LDCT); Lung cancer screening; Thin-slice CTMedical Physics (physics.med-ph)Thin-slice CTlow-dose computed tomographyPhysics - Medical Physicsimage processing
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Computer-Aided Diagnosis System with Backpropagation Artificial Neural Network—Improving Human Readers Performance

2016

This article presents the results of a study into possibility of artificial neural networks (ANNs) to classify cancer changes in mammographic images. Today’s Computer-Aided Detection (CAD) systems cannot detect 100 % of pathological changes. One of the properties of an ANN is generalized information —it can identify not only learned data but also data that is similar to training set. The combination of CAD and ANN could give better result and help radiologists to take the right decision.

Training setArtificial neural networkComputer sciencebusiness.industryComputer Science::Neural and Evolutionary ComputationPhysics::Medical PhysicsCADMachine learningcomputer.software_genreComputer aided detectionComputingMethodologies_PATTERNRECOGNITIONComputer-aided diagnosisArtificial intelligencebusinessartificial neural networks�mammographic imagescomputercomputer-aided detectionBackpropagation artificial neural network
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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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