Semi-automated Acanthamoeba polyphaga detection and computation of Salmonella typhimurium concentration in spatio-temporal images.
Interaction between bacteria and protozoa is an increasing area of interest, however there are a few systems that allow extensive observation of the interactions. A semi-automated approach is proposed to analyse a large amount of experimental data and avoid a time demanding manual object classification. We examined a surface system consisting of non nutrient agar with a uniform bacterial lawn that extended over the agar surface, and a spatially localised central population of amoebae. Location and identification of protozoa and quantification of bacteria population are performed by the employment of image analysis techniques in a series of spatial images. The quantitative tools are based on intensity thresholding, or on probabilistic models. To accelerate organism identification, correct classification errors and attain quantitative details of all objects a custom written Graphical User Interfaces has also been developed.
This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.
Vol. 42, pp. 911 - 920
Place of publication