Search results for " microcalcifications"

showing 4 items of 4 documents

Mammographic images segmentation based on chaotic map clustering algorithm

2013

Background: This work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images. The chaotic map clustering algorithm is used to group together similar subsets of image pixels resulting in a medically meaningful partition of the mammography. Methods: The image is divided into pixels subsets characterized by a set of conveniently chosen features and each of the corresponding points in the feature space is associated to a map. A mutual coupling strength between the maps depending on the associated distance between feature space points is subsequently introduced. On the system of maps, the simulated evolution through chaotic dynamics leads…

Cooperative behaviorClustering algorithmsComputer scienceFeature vectorCorrelation clusteringPhysics::Medical PhysicsMass lesionsMicrocalcificationsImage processingBreast NeoplasmsDigital imageSegmentationBreast cancerImage Processing Computer-AssistedCluster AnalysisHumansRadiology Nuclear Medicine and imagingSegmentationComputer visionCluster analysisFeaturesPixelChaotic maps Clustering algorithms Cooperative behavior Segmentation Mammography Features Mass lesions Microcalcifications Breast cancerbusiness.industrySegmentation-based object categorizationCalcinosisSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Radiographic Image EnhancementChaotic mapsRadiology Nuclear Medicine and imagingComputer Science::Computer Vision and Pattern RecognitionFemaleArtificial intelligencebusinessAlgorithmsMammographyResearch Article
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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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Computer-aided diagnosis in digital mammography: comparison of two commercial systems

2014

Aim: Within this work, a comparative analysis of two commercial computer-aided detection or diagnosis (CAD) systems, CyclopusCAD® mammo (v. 6.0) produced by CyclopusCAD Ltd (Palermo, Italy) and SecondLook® (v. 6.1C) produced by iCAD Inc. (OH, USA) is performed by evaluating the results of both systems application on an unique set of mammographic digital images routinely acquired in a hospital structure. Materials & methods: The two CAD systems have been separately applied on a sample set of 126 mammographic digital cases, having been independently diagnosed by two senior radiologists. According to the human diagnosis, the cases in the sample reference set are divided into 61 negatives and 6…

Pathologymedicine.medical_specialtyDigital mammographyRadiological and Ultrasound Technologymedicine.diagnostic_testbusiness.industryCad systemSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Digital imageComputer-aided diagnosisBreast cancer clusters computer-assisted diagnosis FFDM FROC curve mammografy mass lesions microcalcifications performancemedicineMammographyRadiology Nuclear Medicine and imagingRadiologybusiness
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Fuzzy technique for microcalcifications clustering in digital mammograms

2012

Abstract Background Mammography has established itself as the most efficient technique for the identification of the pathological breast lesions. Among the various types of lesions, microcalcifications are the most difficult to identify since they are quite small (0.1-1.0 mm) and often poorly contrasted against an images background. Within this context, the Computer Aided Detection (CAD) systems could turn out to be very useful in breast cancer control. Methods In this paper we present a potentially powerful microcalcifications cluster enhancement method applicable to digital mammograms. The segmentation phase employs a form filter, obtained from LoG filter, to overcome the dependence from …

Databases FactualMicrocalcificationsBreast NeoplasmsContext (language use)CADcomputer.software_genreSensitivity and SpecificityFuzzy logicClusteringBreast cancerSegmentationBreast cancerC-meansImage Processing Computer-AssistedmedicineCluster AnalysisHumansMammographyRadiology Nuclear Medicine and imagingSegmentationCluster analysisSpatial filtersmedicine.diagnostic_testMultimediabusiness.industryCalcinosisPattern recognitionmedicine.diseaseSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Computer aided detectionFuzzy logicRadiology Nuclear Medicine and imagingFemaleArtificial intelligencebusinesscomputerAlgorithmsMammographyResearch ArticleBreast cancer Microcalcifications Spatial filters Clustering Fuzzy logic C-means Mammography SegmentationBMC Medical Imaging
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