Suhre, AlexanderErşahin, TülinÇetin-Atalay, RengülÇetin, A. Enis2016-02-082016-02-0820112076-1465http://hdl.handle.net/11693/28294Date of Conference: 29 Aug.-2 Sept. 2011Some 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.EnglishA-transformBayesian frameworksBayesian learningBiomedical image dataBiomedical imagesCancer cell linesClassification parametersCovariance matricesImage blocksRegion covarianceSharp cornersCell cultureCovariance matrixSignal processingSupport vector machinesConference Paper