Microscopic image classification using sparsity in a transform domain and Bayesian learning
Author
Suhre, Alexander
Erşahin, Tülin
Çetin-Atalay, Rengül
Çetin, A. Enis
Date
2011Source Title
2011 19th European Signal Processing Conference
Print ISSN
2076-1465
Publisher
IEEE
Pages
1005 - 1009
Language
English
Type
Conference PaperItem Usage Stats
81
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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.
Keywords
A-transformBayesian frameworks
Bayesian learning
Biomedical image data
Biomedical images
Cancer cell lines
Classification parameters
Covariance matrices
Image blocks
Region covariance
Sharp corners
Cell culture
Covariance matrix
Signal processing
Support vector machines