A two-step procedure for automatic and accurate segmentation of volumetric CLSM biofilm images

This paper presents a robust two-step segmentation procedure for the study of biofilm structure. Without user intervention, the procedure segments volumetric biofilm images generated by a confocal laser scanning microscopy (CLSM). This automated procedure implements an anisotropic diffusion filter as a preprocessing step and a 3D extension of the Otsu method for thresholding. Applying the anisotropic diffusion filter to even low-contrast CLSM images significantly improves the segmentation obtained with the 3D Otsu method. A comparison of the results for several CLSM data sets demonstrated that the accuracy of this procedure, unlike that of the objective threshold selection algorithm (OTS), is not affected by biofilm coverage levels and thus fills an important gap in developing a robust and objective segmenting procedure. The effectiveness of the present segmentation procedure is shown for CLSM images containing different bacterial strains. The image saturation handling capability of this procedure relaxes the constraints on user-selected gain and intensity settings of a CLSM. Therefore, this two-step procedure provides an automatic and accurate segmentation of biofilms that is independent of biofilm coverage levels and, in turn, lays a solid foundation for achieving objective analysis of biofilm structural parameters.     

Microscope-Based Imaging Platform for Large-Scale Analysis of Oral Biofilms

A microscopic method for noninvasively monitoring oral biofilms at the macroscale was developed to describe the spatial distribution of biofilms of different bacterial composition on bovine enamel surfaces (BES). For this purpose, oral biofilm was grown in situ on BES that were fixed at approximal sites of individual upper jaw acrylic devices worn by a volunteer for 3 or 5 days. Eubacteria, Streptococcus spp., and Fusobacterium nucleatum were stained using specific fluorescence in situ hybridization (FISH) probes. The resulting fluorescence signals were subsequently tested by confocal laser scanning microscopy (CLSM) and monitored by an automated wide-field microscope-based imaging platform (Scan∧R). Automated image processing and data analysis were conducted by microscope-associated software and followed by statistical evaluation of the results. The full segmentation of biofilm images revealed a random distribution of bacteria across the entire area of the enamel surfaces examined. Significant differences in the composition of the microflora were recorded across individual as well as between different enamel surfaces varying from sparsely colonized (47.26%) after 3 days to almost full surface coverage (84.45%) after 5 days. The enamel plates that were positioned at the back or in the middle of the oral cavity were found to be more suitable for the examination of biofilms up to 3 days old. In conclusion, automated microscopy combined with the use of FISH can enable the efficient visualization and meaningful quantification of bacterial composition over the entire sample surface. Due to the possibility of automation, Scan∧R overcomes the technical limitations of conventional CLSM.     

Three-dimensional biofilm image reconstruction for assessing structural parameters

Parameters representing three-dimensional (3D) biofilm structure are quantified from confocal laser-scanning microscope (CLSM) images. These 3D parameters describe the distribution of biomass pixels within the space occupied by a biofilm; however, they lack a direct connection to biofilm activity. As a result, researchers choose a handful of parameters without there being a consensus on a standard set of parameters. We hypothesized that a select 3D parameter set could be used to reconstruct a biofilm image and that the reconstructed and original biofilm images would have similar activities. To test this hypothesis, an algorithm was developed to reconstruct a biofilm image with parameters identical to those of the original CLSM image. We introduced an objective method to assess the reconstruction algorithm by comparing the activities of the original and reconstructed biofilm images. We found that biofilm images with identical structural parameters showed nearly identical activities and substrate concentration profiles. This implies that the set containing all common structural parameters can successfully describe biofilm structure. This finding is significant, as it opens the door to the next step, of finding a smaller standard set of biofilm structural parameters that can be used to compare biofilm structure.     

The use of microscopy and three-dimensional visualization to evaluate the structure of microbial biofilms cultivated in the calgary biofilm device

Microbes frequently live within multicellular, solid surface-attached assemblages termed biofilms. These microbial communities have architectural features that contribute to population heterogeneity and consequently to emergent cell functions. Therefore, three-dimensional (3D) features of biofilm structure are important for understanding the physiology and ecology of these microbial systems. This paper details several protocols for scanning electron microscopy and confocal laser scanning microscopy (CLSM) of biofilms grown on polystyrene pegs in the Calgary Biofilm Device (CBD). Furthermore, a procedure is described for image processing of CLSM data stacks using amira™, a virtual reality tool, to create surface and/or volume rendered 3D visualizations of biofilm microorganisms. The combination of microscopy with microbial cultivation in the CBD — an apparatus that was designed for highthroughput susceptibility testing — allows for structure-function analysis of biofilms under multivariate growth and exposure conditions.     

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.     

Volumetric measurements of bacterial cells and extracellular polymeric substance glycoconjugates in biofilms

In this study an enrichment culture developed from activated sludge was used to investigate the architecture of fully hydrated multispecies biofilms. The assessment of biofilm structure and volume was carried out using confocal laser scanning microscopy (CLSM). Bacterial cell distribution was determined with the nucleic acid-specific stain SYTO 60, whereas glycoconjugates of extracellular polymeric substances (EPS) were stained with the Alexa-488-labeled lectin of Aleuria aurantia. Digital image analysis was employed for visualization and quantification of three-dimensional CLSM data sets. The specific volumes of the polymeric and cellular biofilm constituents were quantified. In addition, gravimetric measurements were done to determine dry mass and thickness of the biofilms. The data recorded by the CLSM technique and the gravimetric data were then compared. It was shown that the biofilm thicknesses determined with both methods agree well for slow-growing heterotrophic and chemoautotrophic biofilms. In addition, for slow-growing biofilms, the volumes and masses calculated from CLSM and the biomass calculated from gravimetric measurements were also comparable. For fast-growing heterotrophic biofilms cultivated with high glucose concentrations the data sets fit to a lesser degree, but still showed the same common trend. Compared with traditional gravimetric measurements, CLSM allowed differential recording of multiple biofilm parameters with subsequent three-dimensional visualization and quantification. The quantitative three-dimensional results recorded by CLSM are an important basis for understanding, controlling, exploiting, and modeling of biofilms.     

Fully automated segmentation and morphometrical analysis of muscle fiber images.

BACKGROUND: Measurement of muscle fiber size and determination of size distribution is important in the assessment of neuromuscular disease. Fiber size estimation by simple inspection is inaccurate and subjective. Manual segmentation and measurement are time-consuming and tedious. We therefore propose an automated image analysis method for objective, reproducible, and time-saving measurement of muscle fibers in routinely hematoxylin-eosin stained cryostat sections. METHODS: The proposed segmentation technique makes use of recent advances in level set based segmentation, where classical edge based active contours are extended by region based cues, such as color and texture. Segmentation and measurement are performed fully automatically. Multiple morphometric parameters, i.e., cross sectional area, lesser diameter, and perimeter are assessed in a single pass. The performance of the computed method was compared to results obtained by manual measurement by experts. RESULTS: The correct classification rate of the computed method was high (98%). Segmentation and measurement results obtained manually or automatically did not reveal any significant differences. CONCLUSIONS: The presented region based active contour approach has been proven to accurately segment and measure muscle fibers. Complete automation minimizes user interaction, thus, batch processing, as well as objective and reproducible muscle fiber morphometry are provided.     

Automated identification of diploid reference cells in cervical smears using image analysis

BACKGROUND: Acquisition of DNA ploidy histograms by image analysis may yield important information regarding the behavior of premalignant cervical lesions. Accurate selection of nuclei for DNA measurement is an important prerequisite for obtaining reliable data. Traditionally, manual selection of nuclei of diagnostic and reference cells is performed by an experienced cytotechnologist. In the present study, a method for the fully automated identification of nuclei of diploid epithelial reference cells in Feulgen- restained Papanicolaou (PAP) smears is described. METHODS: The automated procedure consists of a decision tree implemented on the measurement device, containing nodes with feature threshold values and multivariate discriminant functions. Nodes were constructed to recognize debris and inflammatory cells, as well as diploid and nondiploid epithelial cells of the uterine cervix. Evaluation of the classifier was performed by comparing resulting diploid integrated optical densities with those from manually selected reference cells. RESULTS AND CONCLUSION: On average, automatically acquired values deviated 2.4% from manually acquired values, indicating that the method described in this paper may be useful in cytometric practice.     

Confocal DNA cytometry: a contour-based segmentation algorithm for automated three-dimensional image segmentation

BACKGROUND: Confocal laser scanning microscopy (CLSM) presents the opportunity to perform three-dimensional (3D) DNA content measurements on intact cells in thick histological sections. So far, these measurements have been performed manually, which is quite time-consuming. METHODS: In this study, an intuitive contour-based segmentation algorithm for automatic 3D CLSM image cytometry of nuclei in thick histological sections is presented. To evaluate the segmentation algorithm, we measured the DNA content and volume of human liver and breast cancer nuclei in 3D CLSM images. RESULTS: A high percentage of nuclei could be segmented fully automatically (e.g., human liver, 92%). Comparison with (time-consuming) interactive measurements on the same CLSM images showed that the results were well correlated (liver, r = 1.00; breast, r = 0.92). CONCLUSIONS: Automatic 3D CLSM image cytometry enables measurement of volume and DNA content of large numbers of nuclei in thick histological sections within an acceptable time. This makes large-scale studies feasible, whereby the advantages of CLSM can be exploited fully. The intuitive modular segmentation algorithm presented in this study detects and separates overlapping objects, also in two-dimensional (2D) space. Therefore, this algorithm may also be suitable for other applications.     

Evaluation of automated threshold selection methods for accurately sizing microscopic fluorescent cells by image analysis

The accurate measurement of bacterial and protistan cell biomass is necessary for understanding their population and trophic dynamics in nature. Direct measurement of fluorescently stained cells is often the method of choice. The tedium of making such measurements visually on the large numbers of cells required has prompted the use of automatic image analysis for this purpose. Accurate measurements by image analysis require an accurate, reliable method of segmenting the image, that is, distinguishing the brightly fluorescing cells from a dark background. This is commonly done by visually choosing a threshold intensity value which most closely coincides with the outline of the cells as perceived by the operator. Ideally, an automated method based on the cell image characteristics should be used. Since the optical nature of edges in images of light-emitting, microscopic fluorescent objects is different from that of images generated by transmitted or reflected light, it seemed that automatic segmentation of such images may require special considerations. We tested nine automated threshold selection methods using standard fluorescent microspheres ranging in size and fluorescence intensity and fluorochrome-stained samples of cells from cultures of cyanobacteria, flagellates, and ciliates. The methods included several variations based on the maximum intensity gradient of the sphere profile (first derivative), the minimum in the second derivative of the sphere profile, the minimum of the image histogram, and the midpoint intensity. Our results indicated that thresholds determined visually and by first-derivative methods tended to overestimate the threshold, causing an underestimation of microsphere size. The method based on the minimum of the second derivative of the profile yielded the most accurate area estimates for spheres of different sizes and brightnesses and for four of the five cell types tested. A simple model of the optical properties of fluorescing objects and the video acquisition system is described which explains how the second derivative best approximates the position of the edge.     

Evaluation of automated threshold selection methods for accurately sizing microscopic fluorescent cells by image analysis.

The accurate measurement of bacterial and protistan cell biomass is necessary for understanding their population and trophic dynamics in nature. Direct measurement of fluorescently stained cells is often the method of choice. The tedium of making such measurements visually on the large numbers of cells required has prompted the use of automatic image analysis for this purpose. Accurate measurements by image analysis require an accurate, reliable method of segmenting the image, that is, distinguishing the brightly fluorescing cells from a dark background. This is commonly done by visually choosing a threshold intensity value which most closely coincides with the outline of the cells as perceived by the operator. Ideally, an automated method based on the cell image characteristics should be used. Since the optical nature of edges in images of light-emitting, microscopic fluorescent objects is different from that of images generated by transmitted or reflected light, it seemed that automatic segmentation of such images may require special considerations. We tested nine automated threshold selection methods using standard fluorescent microspheres ranging in size and fluorescence intensity and fluorochrome-stained samples of cells from cultures of cyanobacteria, flagellates, and ciliates. The methods included several variations based on the maximum intensity gradient of the sphere profile (first derivative), the minimum in the second derivative of the sphere profile, the minimum of the image histogram, and the midpoint intensity. Our results indicated that thresholds determined visually and by first-derivative methods tended to overestimate the threshold, causing an underestimation of microsphere size. The method based on the minimum of the second derivative of the profile yielded the most accurate area estimates for spheres of different sizes and brightnesses and for four of the five cell types tested. A simple model of the optical properties of fluorescing objects and the video acquisition system is described which explains how the second derivative best approximates the position of the edge.     

Mathematical morphologic segmentation dedicated to quantitative immunohistochemistry

OBJECTIVE: To develop automatic segmentation sequences for fully automated quantitative immunohistochemistry of cancer cell nuclei by image analysis. STUDY DESIGN: The study focused on the automated delineation of cancer cell lobules and nuclei, taking breast carcinoma as an example. A hierarchic segmentation was developed, employing mainly the chaining of mathematical morphology operators. The proposed sequence was tested on 22 images of various situations, collected from 18 different cases of breast carcinoma. A quality control procedure was applied, comparing the automated method with manual outlining of cancer cell foci and with manual pricking of cancer cell nuclei. RESULTS: Good concordance was found between automated and manual segmentation procedures (90% for cancer cell clumps, 97% for cancer cell nuclei on average), but the rate of false positive nuclei (small regions labeled as nuclei by the segmentation procedure) could be relatively high (11% on average, with a maximum of 35%) and can result in underestimation of the immunostaining ratio. CONCLUSION: This study examined a preliminary approach to automated immunoquantification, limited to automated segmentation without any color characterization. The automated hierarchic segmentation presented here leads to good discrimination of cancer cell nuclei at the chosen magnification.     

Quantification of epithelial volume by image processing applied to ovarian tumors

This paper describes an image analysis technique for automated estimation of the percentages of epithelium and stroma in (tumor) tissue. The program is evaluated on ovarian tumors of the serous, mucinous, and endometrioid type. From standard paraffin sections, stained with pararosanilin Feulgen and naphthol yellow, a blue-yellow image pair was recorded. The blue image was used for the determination of the total tissue area and the yellow image for the epithelial area. For the latter the image processing method is based on the fact that epithelial nuclei are generally more tightly packed than stromal nuclei. A structural approach was applied, in which the segmentation of the nuclei was based on the image contrast range in the density domain. The method has been tested with 78 image pairs from 19 ovarian tumors with varying degrees of malignancy. The area percentages, as assessed with image processing, were strongly correlated to control percentages, established by interactive morphometry (r = 0.98).     

An edge relocation segmentation algorithm

An automated procedure that refines the nuclear contour of a previously segmented nucleus is described. The algorithm makes use of intensity information, edge magnitude information and both object and edge connectivity information. This automated procedure generates a closed contour precisely along the edge of the nucleus. The procedure was tested on a database of 3,680 red-green-blue images of thionin-SO2 and orange II-stained cervical cells obtained from normal and dysplastic samples. When used in conjunction with a simple threshold selection algorithm and an artifact removal routine, this edge relocation algorithm resulted in the correct segmentation of over 98% of the nuclei. Only 63 (1.7%) of all nuclei were incorrectly segmented.     

An Objective Method to Optimize the MR Sequence Set for Plaque Classification in Carotid Vessel Wall Images Using Automated Image Segmentation

A typical MR imaging protocol to study the status of atherosclerosis in the carotid artery consists of the application of multiple MR sequences. Since scanner time is limited, a balance has to be reached between the duration of the applied MR protocol and the quantity and quality of the resulting images which are needed to assess the disease. In this study an objective method to optimize the MR sequence set for classification of soft plaque in vessel wall images of the carotid artery using automated image segmentation was developed. The automated method employs statistical pattern recognition techniques and was developed based on an extensive set of MR contrast weightings and corresponding manual segmentations of the vessel wall and soft plaque components, which were validated by histological sections. Evaluation of the results from nine contrast weightings showed the tradeoff between scan duration and automated image segmentation performance. For our dataset the best segmentation performance was achieved by selecting five contrast weightings. Similar performance was achieved with a set of three contrast weightings, which resulted in a reduction of scan time by more than 60%. The presented approach can help others to optimize MR imaging protocols by investigating the tradeoff between scan duration and automated image segmentation performance possibly leading to shorter scanning times and better image interpretation. This approach can potentially also be applied to other research fields focusing on different diseases and anatomical regions.     

Biofilm structure differentiation based on multi-resolution analysis

Quantitative parameters for describing the morphology of biofilms are crucial towards establishing the influence of growing conditions on biofilm structure. Parameters used in earlier studies generally fail to differentiate complex three-dimensional structures. This article presents a novel approach of defining a parameter vector based on the energy signature of multi-resolution analysis, which was applied to differentiating biofilm structures from confocal laser scanning microscopy (CLSM) biofilm images. The parameter vector distinguished differences in the spatial arrangements of synthetic images. For real CLSM images, this parameter vector detected subtle differences in biofilm structure for three sample cases: (1) two adjacent images of a CLSM stack; (2) two partial stacks from the same CLSM stack with equal numbers of images but spatially offset by one image; and (3) three complete CLSM stacks from different bacterial strains. It was also observed that filtering the noise in CLSM images enhanced the sensitivity of the differentiation using our parameter vector.     

Biofilm structure differentiation based on multi-resolution analysis

Quantitative parameters for describing the morphology of biofilms are crucial towards establishing the influence of growing conditions on biofilm structure. Parameters used in earlier studies generally fail to differentiate complex three-dimensional structures. This article presents a novel approach of defining a parameter vector based on the energy signature of multi-resolution analysis, which was applied to differentiating biofilm structures from confocal laser scanning microscopy (CLSM) biofilm images. The parameter vector distinguished differences in the spatial arrangements of synthetic images. For real CLSM images, this parameter vector detected subtle differences in biofilm structure for three sample cases: (1) two adjacent images of a CLSM stack; (2) two partial stacks from the same CLSM stack with equal numbers of images but spatially offset by one image; and (3) three complete CLSM stacks from different bacterial strains. It was also observed that filtering the noise in CLSM images enhanced the sensitivity of the differentiation using our parameter vector.     

Histometric features for the grading of prostatic carcinoma

Histometric features for the objective grading of prostatic adenocarcinoma in histologic specimens were analyzed in five cases each of well, moderately and poorly differentiated lesions. Tissue sections from the selected cases were stained by the Feulgen method and digitized by a video-based microphotometer. Twenty total fields were recorded for each grade: ten at high resolution (an image sampling of 0.5 micron per pixel) and ten at low resolution (0.8 micron per pixel), with two fields per case recorded at each resolution. The images were segmented by an automated expert system-guided scene segmentation procedure. The performance of that procedure was measured by comparing the automated counts of nuclei in the segmented fields to the visual counts made by a pathologist in the same fields. For well, moderately and poorly differentiated cases, respectively, the nuclear counts made by the expert system at high resolution were 2.7%, 4.2% and 4.7% higher than the visual counts (as estimated from a total of 6,628 nuclei), but 1.2%, 2.5% and 1.1% lower at low resolution (10,329 nuclei). High-resolution features and tissue textural features were computed for each case. The high-resolution features showed good separation between the three groups of cases. The tissue textural features showed consistent separation between well and moderately differentiated cases. The relaxation of the spatial resolution (to 0.8 micron/pixel spacing) did not affect the selection of features, but led to less separation between the data from different grades. In conclusion, the automated system performed satisfactorily in distinguishing sections of prostatic tumors of varying degrees of differentiation.     

Investigation of the mesoscale structure and volumetric features of biofilms using optical coherence tomography

Optical coherence tomography (OCT) was successfully applied to visualize the mesoscale structure of three different heterotrophic biofilms. For this purpose, biofilm volumes of 4 × 4 × 1.6 mm³ were scanned with spatial resolutions lower than 20 µm within an acquisition time of 2 min. A heterogeneous structure was detected for biofilms cultivated in laminar as well as transient flow conditions. The structure was found to be more homogeneous for the biofilm grown in turbulent flow. This biofilm structure was characterized by a volumetric porosity of 0.36, whereas the porosity calculated for biofilms grown in laminar and transient conditions was 0.65. These results were directly generated from the distribution of porosity calculated from the OCT images acquired and can be linked to structural properties. Up to now, the mesoscale biofilm structure was only observable with time‐consuming and expensive studies, for example, magnetic resonance microscopy. OCT will most certainly be helpful for improved understanding and prediction of biofilm physics with respect to macroscale processes, for example, mass transfer and detachment as the information about mesoscale is easily accessible using this method. In the context of this study, we show that CLSM images do not necessarily provide an accurate representation of the biofilm structure at the mesoscale. Additionally, the typical characteristic parameters obtained from CLSM image stacks differ largely from those calculated from OCT images. Nevertheless, to determine the local distribution of biofilm constituents, microscopic methods such as confocal laser scanning microscopy are required. Biotechnol. Bioeng. 2010;107: 844–853. © 2010 Wiley Periodicals, Inc.