A Novel Approach for Reduction of Breast Tissue Density Effects on Normal and Abnormal Masses Classification

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dc.contributor.authorYasar, Huseyin
dc.contributor.authorCeylan, Murat
dc.date.accessioned2020-03-26T19:22:57Z
dc.date.available2020-03-26T19:22:57Z
dc.date.issued2016
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractBreast tissue density prevents the separation of the abnormal tissue from normal tissue in mammography images due to the negative effects on diagnostic success often hiding abnormalities. In this study, significant reduction of these adverse effects is provided by proposing a complete system including also breast tissue density classification. The breast tissue density type of image, which will first be subjected to normal and abnormal tissue classification, is classified with the proposed system. For the breast tissue density classification, artificial neural network (ANN) and the multiresolution analysis, which were previously proposed in the literature, were used. At the second stage, mammography image was subjected to the classification of normal and abnormal tissue by using trained ANN with the other images which have the same type of breast tissue density according to breast tissue density classification results. Wavelet transform, ridgelet transform and contourlet transform were used at this stage in obtaining image features of mammography. In order to test the success of the proposed system, 265 pieces of region of interests belonging to MIAS database were used. At the end of the study, the highest accuracy is 95.472%, sensitivity is 0.8514, specificity is 1 and A(z) is 0.960. These results can be further increased with semi-automatic operation of the system by performing the classification of breast tissue density by the radiologist. The highest accuracy is 97.736%, sensitivity is 0.9324, specificity is 0.9948 and A(z) is 0.974 for semi-automatic system.en_US
dc.identifier.doi10.1166/jmihi.2016.1737en_US
dc.identifier.endpage717en_US
dc.identifier.issn2156-7018en_US
dc.identifier.issn2156-7026en_US
dc.identifier.issue3en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage710en_US
dc.identifier.urihttps://dx.doi.org/10.1166/jmihi.2016.1737
dc.identifier.urihttps://hdl.handle.net/20.500.12395/33191
dc.identifier.volume6en_US
dc.identifier.wosWOS:000378798400017en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAMER SCIENTIFIC PUBLISHERSen_US
dc.relation.ispartofJOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectBreast Mass Classificationen_US
dc.subjectBreast Tissue Density Classificationen_US
dc.subjectWavelet Transformen_US
dc.subjectRidgelet Transformen_US
dc.subjectContourlet Transformen_US
dc.subjectArtificial Neural Network (ANN)en_US
dc.subjectMIAS Databaseen_US
dc.titleA Novel Approach for Reduction of Breast Tissue Density Effects on Normal and Abnormal Masses Classificationen_US
dc.typeArticleen_US

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