Hyperspectral imaging for presumptive identification of bacterial colonies on solid chromogenic culture media

Guillemot, Mathilde; Midahuen, Rony; Archeny, Delpine; Fulchiron, Corine; Montvernay, Regis; Perrin, Guillaume; Leroux, Denis

doi:10.1117/12.2229761

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Traditionally, HSI has been widely employed in the remote sensing field, so the majority of algorithms developed to classify HS images are related to this field [40]. However, more recently, HSI is progressively being used in other fields, such as drug analysis [41,42], food quality inspection [17,18,19,43], automated clinical microbiology for pathogen identification directly from bacterial colonies [44,45,46], or defense and security [13,47], among many others. That is why the algorithms that were developed targeting remote sensing application have been adapted to classify different types of scenes.…”

Section: Hyperspectral Imagingmentioning

confidence: 99%

In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer

Halicek

Fabelo

Ortega

et al. 2019

Cancers

178

124

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In contrast to conventional optical imaging modalities, hyperspectral imaging (HSI) is able to capture much more information from a certain scene, both within and beyond the visual spectral range (from 400 to 700 nm). This imaging modality is based on the principle that each material provides different responses to light reflection, absorption, and scattering across the electromagnetic spectrum. Due to these properties, it is possible to differentiate and identify the different materials/substances presented in a certain scene by their spectral signature. Over the last two decades, HSI has demonstrated potential to become a powerful tool to study and identify several diseases in the medical field, being a non-contact, non-ionizing, and a label-free imaging modality. In this review, the use of HSI as an imaging tool for the analysis and detection of cancer is presented. The basic concepts related to this technology are detailed. The most relevant, state-of-the-art studies that can be found in the literature using HSI for cancer analysis are presented and summarized, both in-vivo and ex-vivo. Lastly, we discuss the current limitations of this technology in the field of cancer detection, together with some insights into possible future steps in the improvement of this technology.

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Section: Hyperspectral Imagingmentioning

confidence: 99%

In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer

Halicek

Fabelo

Ortega

et al. 2019

Cancers

178

124

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“…The potential of hyperspectral imaging in the region of 400 -900 nm of colonies for identifying microbial cultures of 19 bacterial strains of 10 genera was evaluated [44]. Microbial colonies were subcategorized in 6 chromogenic classes after growth on a chromogenic culture medium (chromID® CPS Elite, bioMérieux, France).…”

Section: Development Of a Visible And Near-infrared Hyperspectral Imamentioning

confidence: 99%

Image Analysis in Microbiology: A Review

Pushchino¹

2016

JCC

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This review is focused on using computer image analysis as a means of objective and quantitative characterizing optical images of the macroscopic (e.g. microbial colonies) and the microscopic (e.g. single cell) objects in the microbiological research. This is the way of making many visual inspection assays more objective and less time and labor consuming. Also, it can provide new visually inaccessible information on relation between some optical parameters and various biological features of the microbial cultures. Of special interest is application of image analysis in fluorescence microscopy as it opens new ways of using fluorescence based methodology for single microbial cell studies. Examples of using image analysis in the studies of both the macroscopic and the microscopic microbiological objects obtained by various imaging techniques are presented and discussed.

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“…Using feature selection techniques (Forward Selection SVM), we have previously shown 2,3,4,5 via parsimonious analysis of fully spectrally resolved diffuse reflectance spectra, the possibility to move from hyper-to multi-spectral imaging with a dramatic dimension reduction from 240 channels to 6 channels (i.e., narrow spectral bands). We discovered that only 6 dichroic bandpass filters with a 10 nm bandwidth were sufficient to identify, with an accuracy higher han 90%, the five MPU responsible for 70% to 90% of UTI infections depending on the local epidemiology.…”

Section: Introductionmentioning

confidence: 99%

Uropathogens identification by multispectral imaging of urine clinical samples using a low-cost device: an intermediate-technology solution

Leroux,

Montvernay,

Davenas

et al. 2024

Biomedical Spectroscopy, Microscopy, and Imaging III

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Identification of after-culture microbial species using a low-cost fully automated system would strongly benefit Low Income Countries, as traditional culture plate reading for the purpose of species identification currently requires trained professionals and remains mostly manual, while more recent automated identification methods are costly. Our application is the label-freeidentification of uropathogens from bacterial colonies images directly on a non-chromogenic culture medium. With a frugal innovation mindset, we are developing a simple diagnostic system based on a filter wheel and a smartphone-driven CMOS camera. We are reporting performance of identification for true clinical samples and compare it to samples issued from a strain collection. Also, the capability to classify the five most-prevalent uropathogens (MPU) in presence of low-prevalent uropathogens (LPU) is evaluated. Two machine learning classification approaches are compared: classical or probabilistic support vector machine SVM (Platt method) using solely a 1-D vector of intensities without any morphological predictors at this stage. Some apparent shortcomings of the logistic regression approach are highlighted for the probabilistic SVM approach.

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Hyperspectral imaging for presumptive identification of bacterial colonies on solid chromogenic culture media

Cited by 5 publications

References 8 publications

In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer

In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer

Image Analysis in Microbiology: A Review

Uropathogens identification by multispectral imaging of urine clinical samples using a low-cost device: an intermediate-technology solution

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