Microscopic image classification using sparsity in a transform domain and Bayesian learning

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage1009en_US
dc.citation.spage1005en_US
dc.contributor.authorSuhre, Alexanderen_US
dc.contributor.authorErşahin, Tülinen_US
dc.contributor.authorÇetin-Atalay, Rengülen_US
dc.contributor.authorÇetin, A. Enisen_US
dc.coverage.spatialBarcelona, Spainen_US
dc.date.accessioned2016-02-08T12:16:39Z
dc.date.available2016-02-08T12:16:39Z
dc.date.issued2011en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 29 Aug.-2 Sept. 2011en_US
dc.description.abstractSome biomedical images show a large quantity of different junctions and sharp corners. It is possible to classify several types of biomedical images in a region covariance approach. Cancer cell line images are divided into small blocks and covariance matrices of image blocks are computed. Eigen-values of the covariance matrices are used as classification parameters in a Bayesian framework using the sparsity of the parameters in a transform domain. The efficiency of the proposed method over classification using standard Support Vector Machines (SVM) is demonstrated on biomedical image data. © 2011 EURASIP.en_US
dc.identifier.issn2076-1465en_US
dc.identifier.urihttp://hdl.handle.net/11693/28294
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.source.title2011 19th European Signal Processing Conferenceen_US
dc.subjectA-transformen_US
dc.subjectBayesian frameworksen_US
dc.subjectBayesian learningen_US
dc.subjectBiomedical image dataen_US
dc.subjectBiomedical imagesen_US
dc.subjectCancer cell linesen_US
dc.subjectClassification parametersen_US
dc.subjectCovariance matricesen_US
dc.subjectImage blocksen_US
dc.subjectRegion covarianceen_US
dc.subjectSharp cornersen_US
dc.subjectCell cultureen_US
dc.subjectCovariance matrixen_US
dc.subjectSignal processingen_US
dc.subjectSupport vector machinesen_US
dc.titleMicroscopic image classification using sparsity in a transform domain and Bayesian learningen_US
dc.typeConference Paperen_US
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