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

Date
2011
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Source Title
2011 19th European Signal Processing Conference
Print ISSN
2076-1465
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Publisher
IEEE
Volume
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Pages
1005 - 1009
Language
English
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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. 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.

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