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

Alexander Suhre, Tulin Ersahin, Rengül Çetin-Atalay, A. Enis Çetin

Abstract: Some 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. Eigenvalues 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.

External IDs:dblp:conf/eusipco/SuhreECC11