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dc.creatorMilošević, Marina
dc.creatorJanković, Dragan
dc.creatorPeulić, Aleksandar
dc.date.accessioned2021-09-24T15:32:57Z
dc.date.available2021-09-24T15:32:57Z
dc.date.issued2015
dc.identifier.issn0013-5585
dc.identifier.urihttps://gery.gef.bg.ac.rs/handle/123456789/718
dc.description.abstractIn this paper, we present a system based on feature extraction techniques for detecting abnormal patterns in digital mammograms and thermograms. A comparative study of texture-analysis methods is performed for three image groups: mammograms from the Mammographic Image Analysis Society mammographic database; digital mammograms from the local database; and thermography images of the breast. Also, we present a procedure for the automatic separation of the breast region from the mammograms. Computed features based on gray-level co-occurrence matrices are used to evaluate the effectiveness of textural information possessed by mass regions. A total of 20 texture features are extracted from the region of interest. The ability of feature set in differentiating abnormal from normal tissue is investigated using a support vector machine classifier, Naive Bayes classifier and K-Nearest Neighbor classifier. To evaluate the classification performance, five-fold cross-validation method and receiver operating characteristic analysis was performed.en
dc.publisherWalter De Gruyter Gmbh, Berlin
dc.rightsrestrictedAccess
dc.sourceBiomedical Engineering-Biomedizinische Technik
dc.subjectbreast canceren
dc.subjectmammographyen
dc.subjectregion of interesten
dc.subjecttexture analysisen
dc.subjectthermographyen
dc.titleComparative analysis of breast cancer detection in mammograms and thermogramsen
dc.typearticle
dc.rights.licenseARR
dcterms.abstractМилошевић, Марина; Пеулић, Aлександар; Јанковић, Драган;
dc.citation.volume60
dc.citation.issue1
dc.citation.spage49
dc.citation.epage56
dc.citation.other60(1): 49-56
dc.citation.rankM22
dc.identifier.wos000350406100006
dc.identifier.doi10.1515/bmt-2014-0047
dc.identifier.pmid25720034
dc.identifier.scopus2-s2.0-84925344935
dc.identifier.rcubhttps://hdl.handle.net/21.15107/rcub_gery_718
dc.type.versionpublishedVersion


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