An approach for tissue density classification in mammographic images using artificial neural network based on wavelet and curvelet transforms

Basit Öğe Kaydı
dc.contributor.authorYasar, Huseyin
dc.contributor.authorCeylan, Murat
dc.date.accessioned2020-03-26T19:00:43Z
dc.date.available2020-03-26T19:00:43Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description6th International Conference on Graphic and Image Processing (ICGIP) -- OCT 24-26, 2014 -- Beijing, PEOPLES R CHINAen_US
dc.description.abstractBreast cancer is one of the types of cancer which is most commonly seen in women. Density of breast is an important indicator for the risk of cancer. In addition, densities of tissue may harden the diagnosis by hiding the abnormalities occurring on the breast. For this reason, during the process of diagnosis, the process of automatic classification of breast density has a significant importance. In this study, a new system with the base of Artificial Neural Network (ANN) and multiple resolution analysis is suggested. Wavelet and curvelet analyses having the most common use have been used as multi resolution analysis. 4 pieces of statistics which are minimum value, maximum value, mean value and standard deviation have been extracted from the images which have been eluted to their sub-bands via multi resolution analysis. For the purpose of testing the success of the system, 322 pieces of images which are in MIAS database have been used. The obtained results for different backgrounds are so satisfying; and the highest classification values have been obtained as 97.16 % with Wavelet transform and ANN for fatty background and 79.80 % with Wavelet transform and ANN for fatty-glanduar background. The same results have been obtained using Wavelet transform and ANN and Curvelet transform and ANN for dense background and accuracy rate of 84.82 % have been reached. The results of mean classification have been obtained, for three pieces of tissue types (fatty, fatty-glanduar, dense), in sequence as 84.47 % with the use of ANN, 85.71 % with the use of curvelet analysis and ANN; and 87.26 % with the use of wavelet analysis and ANN.en_US
dc.description.sponsorshipInt Assoc Comp Sci & Informat Technol, Wuhan Univen_US
dc.identifier.doi10.1117/12.2178829en_US
dc.identifier.isbn978-1-62841-558-2
dc.identifier.issn0277-786Xen_US
dc.identifier.issn1996-756Xen_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://dx.doi.org/10.1117/12.2178829
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31822
dc.identifier.volume9443en_US
dc.identifier.wosWOS:000354613300023en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPIE-INT SOC OPTICAL ENGINEERINGen_US
dc.relation.ispartofSIXTH INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING (ICGIP 2014)en_US
dc.relation.ispartofseriesProceedings of SPIE
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectBreast densityen_US
dc.subjectwavelet transformen_US
dc.subjectcurvelet transformen_US
dc.subjectartificial neural network (ANN)en_US
dc.subjectMIAS databaseen_US
dc.titleAn approach for tissue density classification in mammographic images using artificial neural network based on wavelet and curvelet transformsen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu