Authors:
Paolo Andreini
;
Simone Bonechi
;
Monica Bianchini
;
Andrea Garzelli
and
Alessandro Mecocci
Affiliation:
University of Siena, Italy
Keyword(s):
Image Classification, Automatic Urinoculture Screening, Urinary Tract Infections.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image Understanding
;
Medical Imaging
;
Pattern Recognition
;
Software Engineering
Abstract:
Urinary Tract Infections (UTIs) are very common in women, babies and the elderly. The most frequent cause is a bacterium, called Escherichia Coli, which usually lives in the digestive system and in the bowel. Infections can target the urethra, bladder or kidneys. Traditional analysis methods, based on human experts’ evaluation, are typically used to diagnose UTIs, an error prone and lengthy process, whereas an early treatment of common pathologies is fundamental to prevent the infection spreading to kidneys. This paper presents an image based Automated Bacterial Load Estimator (ABLE) system for the urinoculture screening, that provides quick and traceable results for UTIs. Infections are accurately detected and the bacterial load is evaluated through image processing techniques. First, digital color images of the Petri dishes are automatically captured, and cleaned from noisily elements due to laboratory procedures, then specific spatial clustering algorithms are applied to isolate t
he colonies from the culture ground and, finally, an accurate evaluation of the infection severity is performed. A dataset of 499 urine samples has been used during the experiments and the obtained results are fully discussed. The ABLE system speeds up the analysis, grants repeatable results, contributes to the process standardization, and guarantees a significant cost reduction.
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