Mattia Savardi1, Alessandro Ferrari2, Alberto Signoroni3. 1. Information Engineering Dept., University of Brescia, Brescia, Italy. 2. Futura Science Park, Copan Group S.p.A., Brescia, Italy. 3. Information Engineering Dept., University of Brescia, Brescia, Italy. Electronic address: alberto.signoroni@unibs.it.
Abstract
BACKGROUND AND OBJECTIVE: The recent introduction of Full Laboratory Automation systems in clinical microbiology opens to the availability of streams of high definition images representing bacteria culturing plates. This creates new opportunities to support diagnostic decisions through image analysis and interpretation solutions, with an expected high impact on the efficiency of the laboratory workflow and related quality implications. Starting from images acquired under different illumination settings (top-light and back-light), the objective of this work is to design and evaluate a method for the detection and classification of diagnostically relevant hemolysis effects associated with specific bacteria growing on blood agar plates. The presence of hemolysis is an important factor to assess the virulence of pathogens, and is a fundamental sign of the presence of certain types of bacteria. METHODS: We introduce a two-stage approach. Firstly, the implementation of a highly accurate alignment of same-plate image scans, acquired using top-light and back-light illumination, enables the joint spatially coherent exploitation of the available data. Secondly, from each segmented portion of the image containing at least one bacterial colony, specifically designed image features are extracted to feed a SVM classification system, allowing detection and discrimination among different types of hemolysis. RESULTS: The fine alignment solution aligns more than 98.1% images with a residual error of less than 0.13 mm. The hemolysis classification block achieves a 88.3% precision with a recall of 98.6%. CONCLUSIONS: The results collected from different clinical scenarios (urinary infections and throat swab screening) together with accurate error analysis demonstrate the suitability of our system for robust hemolysis detection and classification, which remains feasible even in challenging conditions (low contrast or illumination changes).
BACKGROUND AND OBJECTIVE: The recent introduction of Full Laboratory Automation systems in clinical microbiology opens to the availability of streams of high definition images representing bacteria culturing plates. This creates new opportunities to support diagnostic decisions through image analysis and interpretation solutions, with an expected high impact on the efficiency of the laboratory workflow and related quality implications. Starting from images acquired under different illumination settings (top-light and back-light), the objective of this work is to design and evaluate a method for the detection and classification of diagnostically relevant hemolysis effects associated with specific bacteria growing on blood agar plates. The presence of hemolysis is an important factor to assess the virulence of pathogens, and is a fundamental sign of the presence of certain types of bacteria. METHODS: We introduce a two-stage approach. Firstly, the implementation of a highly accurate alignment of same-plate image scans, acquired using top-light and back-light illumination, enables the joint spatially coherent exploitation of the available data. Secondly, from each segmented portion of the image containing at least one bacterial colony, specifically designed image features are extracted to feed a SVM classification system, allowing detection and discrimination among different types of hemolysis. RESULTS: The fine alignment solution aligns more than 98.1% images with a residual error of less than 0.13 mm. The hemolysis classification block achieves a 88.3% precision with a recall of 98.6%. CONCLUSIONS: The results collected from different clinical scenarios (urinary infections and throat swab screening) together with accurate error analysis demonstrate the suitability of our system for robust hemolysis detection and classification, which remains feasible even in challenging conditions (low contrast or illumination changes).