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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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Shan Gong, Antonio Artés-Rodríguez. Fast Background Elimination in Fluorescence Microbiology Images: Comparison of Four Algorithms. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. pp.285-290, ⟨10.1007/978-3-642-23960-1_34⟩. ⟨hal-01571482⟩

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