Image processing guided analysis for estimation of bacteria colonies number by means of optical transforms

Machine learning for enumeration of cell colony forming units

As one of the most widely used assays in biological research, an enumeration of the bacterial cell colonies is an important but time-consuming and labor-intensive process. To speed up the colony counting, a machine learning method is presented for counting the colony forming units (CFUs), which is referred to as CFUCounter. This cell-counting program processes digital images and segments bacterial colonies. The algorithm combines unsupervised machine learning, iterative adaptive thresholding, and local-minima-based watershed segmentation to enable an accurate and robust cell counting. Compared to a manual counting method, CFUCounter supports color-based CFU classification, allows plates containing heterologous colonies to be counted individually, and demonstrates overall performance (slope 0.996, SD 0.013, 95%CI: 0.97–1.02, p value < 1e-11, r = 0.999) indistinguishable from the gold standard of point-and-click counting. This CFUCounter application is open-source and easy to use as a unique addition to the arsenal of colony-counting tools.

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.

Degeneration of Fraunhofer diffraction on bacterial colonies due to their light focusing properties examined in the digital holographic microscope system

The degeneration of Fraunhofer diffraction conditions in the optical system with converging spherical wave illumination for bacteria species identification based on diffraction patterns is analyzed by digital holographic methods. The obtained results have shown that the colonies of analyzed bacteria species act as biological lenses with the time-dependent light focusing properties, which are characterized and monitored by means of phase retrieval from sequentially captured digital holograms. This significantly affects the location of Fraunhofer patterns observation plane, which is continuously shifted across optical axis in time.

Bacteria species identification by the statistical analysis of bacterial colonies Fresnel patterns

It was demonstrated that statistical analysis of bacteria colonies Fresnel patterns recorded in the optical system with converging spherical wave illumination is suitable for highly effective bacteria species classification. The proposed method includes Fresnel patterns recording followed by image processing and the statistical analysis based on feature extraction, feature selection, classification and classification performance methods. Examination performed on various bacteria species (Salmonella enteritidis, Staphylococcus aureus, Staphylococcus intermedius, Escherichia coli, Proteus mirabilis, Pseudomonas aeruginosa and Citrobacter freundii) revealed that the proposed method achieved very high accuracy of over 98%.

Development of a microbial high-throughput screening instrument based on elastic light scatter patterns

A microbial high-throughput screening (HTS) system was developed that enabled high-speed combinatorial studies directly on bacterial colonies. The system consists of a forward scatterometer for elastic light scatter (ELS) detection, a plate transporter for sample handling, and a robotic incubator for automatic incubation. To minimize the ELS pattern-capturing time, a new calibration plate and correction algorithms were both designed, which dramatically reduced correction steps during acquisition of the circularly symmetric ELS patterns. Integration of three different control software programs was implemented, and the performance of the system was demonstrated with single-species detection for library generation and with time-resolved measurement for understanding ELS colony growth correlation, using Escherichia coli and Listeria. An in-house colony-tracking module enabled researchers to easily understand the time-dependent variation of the ELS from identical colony, which enabled further analysis in other biochemical experiments. The microbial HTS system provided an average scan time of 4.9 s per colony and the capability of automatically collecting more than 4000 ELS patterns within a 7-h time span.

Influence of various growth conditions on Fresnel diffraction patterns of bacteria colonies examined in the optical system with converging spherical wave illumination

The novel optical system based on converging spherical wave illumination for analysis of bacteria colonies diffraction patterns, is proposed. The complex physical model of light transformation on bacteria colonies in this system, is presented. Fresnel diffraction patterns of bacteria colonies Escherichia coli, Salmonella enteritidis, Staphylococcus aureus grown in various conditions, were examined. It was demonstrated that the proposed system enables the characterization of morphological changes of colony structures basing on the changes of theirs Fresnel diffraction patterns.