A New Approach for Liver Classification Using Ridgelet/Ripplet-II Transforms, Feature Groups and ANN

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dc.contributor.authorOzturk, Ayse Elif
dc.contributor.authorCeylan, Murat
dc.contributor.authorKivrak, Ali Sami
dc.date.accessioned2020-03-26T19:00:26Z
dc.date.available2020-03-26T19:00:26Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description6th European Conference of the International-Federation-for-Medical-and-Biological-Engineering (MBEC) -- SEP 07-11, 2014 -- Dubrovnik, CROATIAen_US
dc.description.abstractIn this study, 68 liver MR images (28 of them labeled as hemangioma, 40 of them labeled as cyst by specialist radiologists) were classified by using artificial neural network (ANN). Ridgelet transform and its advanced new generation form (called Ripplet-II transform) were applied on these images to compare their effects on liver image classification. Feature vectors were generated by calculating mean, standard deviation, variance, skewness, kurtosis and moment values of coefficient matrices. Firstly, all feature vectors were given as inputs to an ANN and classification process was realized. Then, vectors were seperated into three groups and classified by using three ANNs. This procedure was repeated with two ANNs and two groups of feature vectors. Outputs of the systems which used more than one ANN were evaluated by implementing AND and OR operations seperately. Accuracy, sensitivity and specifity values of obtained results were calculated and compared. The best results were achieved by evaluating the system which used three ANNs and three groups of statistical feature vectors, with AND / OR operations.en_US
dc.description.sponsorshipCroatian Med & Biol Engn Soc, Int Federat Med & Biol Engn, Minist Sci Educ & Sports Republ Croatia, Minist Hlth Republ Croatia, Univ Zagreb, Fac Elect Engn & Comp, European Alliance Med & Biol Engn & Sci, European Cooperat Sci & Technolen_US
dc.description.sponsorshipScientific and Technical Research Council of Turkey TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [113E184]en_US
dc.description.sponsorshipThis study was supported by The Scientific and Technical Research Council of Turkey (TUBITAK, Project No: 113E184).en_US
dc.identifier.doi10.1007/978-3-319-11128-5_33en_US
dc.identifier.endpage+en_US
dc.identifier.isbn978-3-319-11127-8
dc.identifier.issn1680-0737en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage130en_US
dc.identifier.urihttps://dx.doi.org/10.1007/978-3-319-11128-5_33
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31769
dc.identifier.volume45en_US
dc.identifier.wosWOS:000349454200033en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER-VERLAG BERLINen_US
dc.relation.ispartof6TH EUROPEAN CONFERENCE OF THE INTERNATIONAL FEDERATION FOR MEDICAL AND BIOLOGICAL ENGINEERINGen_US
dc.relation.ispartofseriesIFMBE Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectRidgelet transformen_US
dc.subjectRipplet-II transformen_US
dc.subjectANNen_US
dc.subjectliver classificationen_US
dc.titleA New Approach for Liver Classification Using Ridgelet/Ripplet-II Transforms, Feature Groups and ANNen_US
dc.typeConference Objecten_US

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