Hyperspectral image analysis for rapid and accurate discrimination of bacterial infections: A benchmark study. (1st September 2017)

• Record Type: Journal Article
• Authors: Arrigoni, Simone; Turra, Giovanni; Signoroni, Alberto
• Is Part Of: Computers in biology and medicine. Volume 88(2017)
• Page Start: 60
• Page End: 71
• Publication Date: 2017-09-01
• Subjects: Hyperspectral imaging -- Pattern recognition -- Image segmentation -- Spectral signature extraction -- Feature reduction -- Digital microbiology imaging; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.06.018
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: With the rapid diffusion of Full Laboratory Automation systems, Clinical Microbiology is currently experiencing a new digital revolution. The ability to capture and process large amounts of visual data from microbiological specimen processing enables the definition of completely new objectives. These include the direct identification of pathogens growing on culturing plates, with expected improvements in rapid definition of the right treatment for patients affected by bacterial infections. In this framework, the synergies between light spectroscopy and image analysis, offered by hyperspectral imaging, are of prominent interest. This leads us to assess the feasibility of a reliable and rapid discrimination of pathogens through the classification of their spectral signatures extracted from hyperspectral image acquisitions of bacteria colonies growing on blood agar plates. We designed and implemented the whole data acquisition and processing pipeline and performed a comprehensive comparison among 40 combinations of different data preprocessing and classification techniques. High discrimination performance has been achieved also thanks to improved colony segmentation and spectral signature extraction. Experimental results reveal the high accuracy and suitability of the proposed approach, driving the selection of most suitable and scalable classification pipelines and stimulating clinical validations.