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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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