Automatic image analysis and classification for urinary bacteria infection screening

Abstract

In this paper, we present an automatic system for the screening of urinary tract infections. It is estimated that about 150 million infections of this kind occur world wide yearly, giving rise to roughly five billion health–care expenditures. Currently, Petri plates seeded with infected samples are analyzed by human experts, an error prone and lengthy process. Nevertheless, based on image processing techniques and machine learning tools, the recognition of the bacterium type and the colony count can be automatically carried out. The proposed system captures a digital image of the plate and, after a preprocessing stage to isolate the colonies from the culture ground, accurately identifies the infection type and severity. Moreover, it contributes to the standardization of the analysis process, also avoiding the continuous transition between sterile and external environments, which is typical in the classical laboratory procedure.