Deep learning approach to bacterial colony classification

In microbiology it is diagnostically useful to recognize various genera and species of bacteria. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for the classification. Moreover, in case of diagnostic uncertainty (the misleading similarity in shape or structure of bacterial cells), such methods can minimize the risk of incorrect recognition. In this article, we apply the state of the art method for texture analysis to classify genera and species of bacteria. This method uses deep Convolutional Neural Networks to obtain image descriptors, which are then encoded and classified with Support Vector Machine or Random Forest. To evaluate this approach and to make it comparable with other approaches, we provide a new dataset of images. DIBaS dataset (Digital Image of Bacterial Species) contains 660 images with 33 different genera and species of bacteria

Computer vision approach for the determination of microbial concentration and growth kinetics using a low cost sensor system

The measurement of microbial contamination is of primary importance in different fields, from environmental monitoring to food safety and clinical analysis. Today, almost all microbiology laboratories make microbial concentration measurements using the standard Plate Count Technique (PCT), a manual method that must be performed by trained personnel. Since manual PCT analysis can result in eye fatigue and errors, in particular when hundreds of samples are processed every day, automatic colony counters have been built and are commercially available. While quick and reliable, these instruments are generally expensive, thus, portable colony counters based on smartphones have been developed and are of low cost but also not accurate as the commercial benchtop instruments. In this paper, a novel computer vision sensor system is presented that can measure the microbial concentration of a sample under test and also estimate the microbial growth kinetics by monitoring the colonies grown on a Petri dish at regular time intervals. The proposed method has been in-house validated by performing PCT analysis in parallel under the same conditions and using these results as a reference. All the measurements have been carried out in a laboratory using benchtop instruments, however, such a system can also be realized as an embedded sensor system to be deployed for microbial analysis outside a laboratory environment

Methods of Classification of the Genera and Species of Bacteria Using Decision Tree, Journal of Telecommunications and Information Technology, 2019, nr 4

This paper presents a computer-based method for recognizing digital images of bacterial cells. It covers automatic recognition of twenty genera and species of bacteria chosen by the author whose original contribution to the work consisted in the decision to conduct the process of recognizing bacteria using the simultaneous analysis of the following physical features of bacterial cells: color, size, shape, number of clusters, cluster shape, as well as density and distribution of the cells. The proposed method may be also used to recognize the microorganisms other than bacteria. In addition, it does not require the use of any specialized equipment. The lack of demand for high infrastructural standards and complementarity with the hardware and software widens the scope of the method’s application in diagnostics, including microbiological diagnostics. The proposed method may be used to identify new genera and species of bacteria, but also other microorganisms that exhibit similar morphological characteristic

TAMMiCol: Tool for analysis of the morphology of microbial colonies.

Many microbes are studied by examining colony morphology via two-dimensional top-down images. The quantification of such images typically requires each pixel to be labelled as belonging to either the colony or background, producing a binary image. While this may be achieved manually for a single colony, this process is infeasible for large datasets containing thousands of images. The software Tool for Analysis of the Morphology of Microbial Colonies (TAMMiCol) has been developed to efficiently and automatically convert colony images to binary. TAMMiCol exploits the structure of the images to choose a thresholding tolerance and produce a binary image of the colony. The images produced are shown to compare favourably with images processed manually, while TAMMiCol is shown to outperform standard segmentation methods. Multiple images may be imported together for batch processing, while the binary data may be exported as a CSV or MATLAB MAT file for quantification, or analysed using statistics built into the software. Using the in-built statistics, it is found that images produced by TAMMiCol yield values close to those computed from binary images processed manually. Analysis of a new large dataset using TAMMiCol shows that colonies of Saccharomyces cerevisiae reach a maximum level of filamentous growth once the concentration of ammonium sulfate is reduced to 200 μM. TAMMiCol is accessed through a graphical user interface, making it easy to use for those without specialist knowledge of image processing, statistical methods or coding

Estado da arte das técnicas de contagem de elementos específicos em imagens digitais.

Contagem de células. Contagem de bactérias e/ou colônias de bactérias. Contagem de árvores. Contagem de pessoas. Contagem de frutas. Contagem de estruturas específicas em amostras de solo. Contagem de colônias de fungos. Contagem de pólen. Contagem de espigas. Contagem de cromossomos. Contagem de ovos de Aedes Aegypti. Contagem de defeitos em madeira. Contagem detos. Contagem de peixes. Contagem de grãos. Contagem de esperma. Contagem de parasitas de malária. Contagem de plâncton. Contagem de larvas. Contagem de lesões causadas por cisticercose. Contagens em ovários. Contagem de pontos fluorescentes em células. Contagem de biscoitos com defeito. Contagem de elementos geológicos extraplanetários. Contagem de sedimentos na urina. Contagem de partículas de amianto. Contagem de trilhas de radição. Contagem de pintas na pele. Contagem de tarugos de aço. Contagem de circuitos impressos. Contagem de fontes de raios gama. Contagem de automóveis. Contagem de rubis em relógios. Contagem de tramas em quadros de pinturas. Contagem de objetos multicoloridos. Contagens gerais. Avaliação de desempenho dos algoritmos.bitstream/item/63197/1/documento120.pd

Metodologias analíticas para avaliar a biodegradabilidade do diesel, biodiesel e blendas B10

Neste trabalho foram desenvolvidas metodologias de análise para avaliar a biodegradabilidade do diesel, biodiesel e blendas B10, mediante as técnicas de espectroscopia no infravermelho (FTIR) associada com quimiometria e o emprego de análise de imagem digitais. O objetivo do trabalho foi desenvolver métodos analíticos capazes de avaliar biodegradação em diesel, quantificar biomassa formada na biodegradação de misturas biodiesel/diesel e por fim quantificar o número de colônias de microrganismos deteriogênicos de biodiesel. Para isso, foram utilizados métodos de análise multivariada como análise por componentes principais (PCA) e regressão multivariada por mínimos quadrados (PLS) e a análise de imagens digitais. Para o estudo de biodegradação de diesel foi investigado diferentes combinações de métodos de pré-processamentos aplicados em espectros de infravermelho médio para analisar amostras de diesel com diferentes teores de enxofre que foram submetidas a estocagem simulada com contaminação microbiana por um período de 40 dias. Para quantificação de biomassa em blendas B10 (10% de biodiesel puro e 90% de óleo diesel) foram utilizadas amostras submetidas a biodegradação por microorganismos e analisadas através de espectroscopia no infravermelho médio e regressão multivariada. Para a quantificação do número de colônias, foram utilizadas imagens digitais de placas de petri contendo colônias da bactéria Bacillus pumilus e da levedura Meyerozyma guilliermondii e um código Python foi desenvolvido para contar automaticamente as unidades formadoras de colônias a partir das imagens digitais. Os principais resultados obtidos demonstraram que o FTIR-ATR pode ser utilizado para avaliação da degradação de diesel e que a região de 1528 - 1318 cm-1 é a mais indicada e uma possível combinação de pré-processamentos com normalização/segunda derivada/centrado na média. Para a quantificação utilizando espectros FTIR-ATR foram avaliadas as figuras de mérito da metodologia, sendo o método considerado promissor para o seu objetivo. Na contagem de unidades formadoras de colônias, por um método semi-automático, foram avaliadas a calibração e validação em relação à contagem manual clássica, os resultados mostraram curvas de calibração e validação com coeficiente de determinação (R2) de 0,99 e 0,98, respectivamente.In this work, analysis methodologies were developed to evaluate the biodegradability of diesel, biodiesel and B10 blends, using infrared spectroscopy (FTIR) technique associated with chemometry and the use of the digital image analysis. The aim of this work was to develop analytical methods capable of evaluating biodegradation in diesel, quantifying biomass formed in biodegradation of biodiesel/diesel mixtures and finally quantifying the number of colonies of deteriogenic microorganisms in biodiesel. For this, multivariate analysis methods were used such as principal component analysis (PCA) and multivariate least squares regression (PLS) and the digital image analysis. For the study of diesel biodegradation was investigated different combinations of pre-processing methods applied in mid-infrared spectra to analyze diesel samples with different sulfur contents that were subjected to simulated storage with microbial contamination for a period of 40 days. For quantification of biomass in B10 blends (10% pure biodiesel and 90% diesel oil), samples subjected to biodegradation by microorganisms were used and analyzed using medium infrared spectroscopy and multivariate regression. To quantify the number of colonies, digital images of petri dishes containing colonies of the genus Bacillus pumilus and the yeast Meyerozyma guilliermondii were used and a Python code was developed to automatically count the colony forming units from the digital images. The main results obtained demonstrated that the FTIR-ATR can be used to assess diesel degradation and that the region of 1528 - 1318 cm-1 is the most suitable and a possible combination of pre-processing with normalization/second derivative/centered on average. For the quantification using FTIR-ATR spectra, the figures of merit of the proposed method were evaluated, being considered appropriate for its objective. The counting of colony forming units, by a semi-automatic method, the calibration and validation were compared with the classic manual counting. The results showed calibration and validation curves with coefficient of determination (R2) of 0.99 and 0 .98, respectively

Segmentação, seguimento e avaliação automática de bactérias em imagens de microscópio

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaAo longo de várias décadas, os diversos trabalhos realizados no campo da microscopia caracterizaram-se por um vasto conjunto de procedimentos de análise em imagens microscópicas. A quantificação celular das imagens em estudo é normalmente um procedimento lento, podendo apresentar uma percentagem de erro significativa, devido essencialmente à elevada quantidade de observações a serem efetuadas. Neste sentido, o desenvolvimento de algoritmos de processamento de imagem e de sistemas de reconhecimento, permite a criação de processos de quantificação automática de muitas das imagens microscópicas em estudo. A dissertação de mestrado aqui apresentada descreve e avalia o desenvolvimento de um algoritmo que tem como objetivo efetuar a segmentação e avaliação de um conjunto de imagens microscópicas. O protótipo projetado constitui um sistema de contabilização automática do número de bactérias E.coli visualizado em cada uma das imagens consideradas. As imagens foram disponibilizadas pelo Laboratory of Biosystem Dynamics da Tampere University of Technology tendo sido adquiridas através de microscópios confocais. O algoritmo desenvolvido pode dividir-se em três passos distintos. Inicialmente é aplicado um pré-processamento sobre as imagens em estudo constituído por um conjunto de transformações que retiram alguma da informação desnecessária da imagem e ao mesmo tempo melhoram os contornos dos segmentos constituintes. Seguidamente é implementado um processo de Template matching, que efetua a detecção da localização de cada uma das bactérias. Neste passo, as bactérias são povoadas individualmente por um conjunto de marcas que são posteriormente utilizadas no terceiro e último passo. Neste último passo é aplicada uma técnica de segmentação baseada no método Watershed, um dos mais estudados métodos de segmentação, inseridos na área do processamento de imagem. O protótipo foi testado nas imagens disponibilizadas, tendo obtido um grau de eficiência bastante satisfatório. De forma a avaliar a versatilidade do software desenvolvido, este foi também aplicado a imagens fornecidas por um dos mais eficientes softwares existentes no mercado e os seus resultados comparados

Image Processing and Simulation Toolboxes of Microscopy Images of Bacterial Cells

Recent advances in microscopy imaging technology have allowed the characterization of the dynamics of cellular processes at the single-cell and single-molecule level. Particularly in bacterial cell studies, and using the E. coli as a case study, these techniques have been used to detect and track internal cell structures such as the Nucleoid and the Cell Wall and fluorescently tagged molecular aggregates such as FtsZ proteins, Min system proteins, inclusion bodies and all the different types of RNA molecules. These studies have been performed with using multi-modal, multi-process, time-lapse microscopy, producing both morphological and functional images. To facilitate the finding of relationships between cellular processes, from small-scale, such as gene expression, to large-scale, such as cell division, an image processing toolbox was implemented with several automatic and/or manual features such as, cell segmentation and tracking, intra-modal and intra-modal image registration, as well as the detection, counting and characterization of several cellular components. Two segmentation algorithms of cellular component were implemented, the first one based on the Gaussian Distribution and the second based on Thresholding and morphological structuring functions. These algorithms were used to perform the segmentation of Nucleoids and to identify the different stages of FtsZ Ring formation (allied with the use of machine learning algorithms), which allowed to understand how the temperature influences the physical properties of the Nucleoid and correlated those properties with the exclusion of protein aggregates from the center of the cell. Another study used the segmentation algorithms to study how the temperature affects the formation of the FtsZ Ring. The validation of the developed image processing methods and techniques has been based on benchmark databases manually produced and curated by experts. When dealing with thousands of cells and hundreds of images, these manually generated datasets can become the biggest cost in a research project. To expedite these studies in terms of time and lower the cost of the manual labour, an image simulation was implemented to generate realistic artificial images. The proposed image simulation toolbox can generate biologically inspired objects that mimic the spatial and temporal organization of bacterial cells and their processes, such as cell growth and division and cell motility, and cell morphology (shape, size and cluster organization). The image simulation toolbox was shown to be useful in the validation of three cell tracking algorithms: Simple Nearest-Neighbour, Nearest-Neighbour with Morphology and DBSCAN cluster identification algorithm. It was shown that the Simple Nearest-Neighbour still performed with great reliability when simulating objects with small velocities, while the other algorithms performed better for higher velocities and when there were larger clusters present