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.
This work has been partly supported by Ministerio de Educación of Spain (projects ’DEIPRO, id. TEC2009-14504-C02-01, and ’COMONSENS’, id. CSD2008-00010).
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Gong, S., Artés-Rodríguez, A. (2011). Fast Background Elimination in Fluorescence Microbiology Images: Comparison of Four Algorithms. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds) Artificial Intelligence Applications and Innovations. EANN AIAI 2011 2011. IFIP Advances in Information and Communication Technology, vol 364. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23960-1_34
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DOI: https://doi.org/10.1007/978-3-642-23960-1_34
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