Fast Background Elimination in Fluorescence Microbiology Images: Comparison of Four Algorithms

Abstract : In this work, we investigate a fast background elimination front-end of an automatic bacilli detection system. This background eliminating system consists of a feature descriptor followed by a linear-SVMs classifier. Four state-of-the-art feature extraction algorithms are analyzed and modified. Extensive experiments have been made on real sputum fluorescence images and the results reveal that 96.92% of the background content can be correctly removed from one image with an acceptable computational complexity.
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Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-364 (Part II), pp.285-290, 2011, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-23960-1_34〉
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Shan Gong, Antonio Artés-Rodríguez. Fast Background Elimination in Fluorescence Microbiology Images: Comparison of Four Algorithms. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-364 (Part II), pp.285-290, 2011, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-23960-1_34〉. 〈hal-01571482〉

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