Image analysis of particle dispersions in microscopy images of cryo-sectioned sausages

Two feature extraction methods, the three-dimensional (3-D) local box-counting method and the area distribution method, are presented to describe the fat dispersion pattern on digital microscopy images of cryo-sectioned sausages. Both methods calculate whole arrays of variables for each microscopy image. The 3-D box-counting method calculates scale dependent (local) dimensions. This is in contrast to common fractal methods, which are univariate. Principal component analysis (PCA) was used to show that different sausages yield different fat dispersion patterns. Partial least square regression (PLS) shows that there is a correlation between the variables gained with both methods and the fat content.     

Automated classification and visualization of fluorescent live cell microscopy images

Robotic, high‐throughput microscopy is a powerful tool for small molecule screening and classifying cell phenotype, proteomic and genomic data. An important hurdle in the field is the automated classification and visualization of results collected from a data set of tens of thousands of images. We present a method that approaches these problems from the perspective of flow cytometry with supporting open‐source code. Image analysis software was created that allowed high‐throughput microscopy data to be analysed in a similar manner as flow cytometry. Each cell on an image is considered an object and a series of gates similar to flow cytometry is used to classify and quantify the properties of cells including size and level of fluorescent intensity. This method is released with open‐source software and code that demonstrates the method's implementation. Accuracy of the software was determined by measuring the levels of apoptosis in a primary murine myoblast cell line after exposure to staurosporine and comparing these results to flow cytometry.     

Micromorphology analysis of TiO2 thin films by atomic force microscopy images: The influence of postannealing

This work describes an analysis of titanium dioxide (TiO₂) thin films prepared on silicon substrates by direct current (DC) planar magnetron sputtering system in O₂/Ar atmosphere in correlation with three‐dimensional (3D) surface characterization using atomic force microscopy (AFM). The samples were grown at temperatures 200, 300, and 400°C on silicon substrate using the same deposition time (30 min) and were distributed into four groups: Group I (as‐deposited samples), Group II (samples annealed at 200°C), Group III (samples annealed at 300°C), and Group IV (samples annealed at 400°C). AFM images with a size of 0.95 μm × 0.95 μm were recorded with a scanning resolution of 256 × 256 pixels. Stereometric analysis was carried out on the basis of AFM data, and the surface topography was described according to ISO 25178‐2:2012 and American Society of Mechanical Engineers (ASME) B46.1‐2009 standards. The maximum and minimum root mean square roughnesses were observed in surfaces of Group II (Sq = 7.96 ± 0.1 nm) and Group IV (Sq = 3.87 ± 0.1 nm), respectively.     

Quantitative analysis of keratin filament networks in scanning electron microscopy images of cancer cells

The keratin filament network is an important part of the cytoskeleton. It is involved in the regulation of shape and viscoelasticity of epithelial cells. The morphology of keratin networks depends on post-translational modifications of keratin monomers. In-vitro studies indicated that network characteristics, such as filament crosslink density, determines the biophysical properties of the filament network. This report presents a quantitative method for the morphological analysis of keratin filament networks. Visualization of filaments was based on prefixation extraction of epithelial cells and scanning electron microscopy (SEM). SEM images were processed by a skeletonization algorithm to obtain a graph structure that represents individual filaments as well as their connections. This method was applied to investigate the effects of transforming growth factor α (TGFα) on the morphology of keratin networks in pancreatic cancer cells. TGFα contributes to pancreatic cancer progression and activates signalling pathways phosphorylating keratin monomers. Using this new method, a significant alteration to the keratin network morphology could be detected in response to TGFα.     

Restoration of confocal images for quantitative image analysis

Quantitative studies of three-dimensional (3-D) structure of microscopic objects have been made possible through the introduction of microscopic volume imaging techniques, most notably the confocal fluorescence microscope (CFM). Although the CFM is a true volume imager, its specific imaging properties give rise to distortions in the images and hamper subsequent quantitative analysis. Therefore, it is a prerequisite that confocal images are restored prior to analysis. The distortions can be divided into several categories: attenuation of areas in the image due to self-absorption, bleaching effects, geometrical effects and distortions due to diffraction effects. Of these, absorption and diffraction effects are the most important. This paper describes a method aimed at the correction of diffraction-induced distortions. All the steps necessary in restoring confocal images are discussed, including a novel method to measure instrumental properties on a routine basis. To test the restoration procedure an image of a fluorescent planar object was restored. The results show a considerable improvement in the z-resolution and no ringing artefacts. The relevance of the method for image analysis is demonstrated by a comparison of results of applying 3-D texture analysis to restored and unrestored images of a synthetic object. Furthermore, the method can be successfully applied to noisy fluorescence images of biological objects, such as interphase cell nucei.     

Evaluation of scanning electron microscopy images of a model dressing using image feature extraction techniques and principal component analysis

Twelve dressing systems made by varying protein type, oil level, CaCl₂, NaCl, and sucrose, were examined using scanning electron microscopy. Images from the 12 systems were quantitatively analysed using methods of feature extraction. These methods were based on vectorisations of the images followed by principal component analysis on the extracted vectors. These techniques were used to examine the reproducibility of the acquired images as well as to relate the images to rheologic and sensory texture parameters. Two feature extraction methods were used: the angle measure technique (AMT) and the absolute difference method (ABDF). The ABDF method used fewer principal components to extract information from images relevant to the complex modulus/sensory viscosity of the system, but the information seemed equally well preserved by the two-feature extraction methods. The AMT was more efficient in classifying the images with respect to protein type. A fair correlation between images and complex modulus was obtained (R=0.73). It is suggested that a better correlation might be obtained by adding more systems, increasing the number of areas imaged for each system as well as avoiding systems of low viscosity.     

Classification of images of biomolecular assemblies: a study of ribosomes and ribosomal subunits of Escherichia coli

Images of macromolecules obtained in the electron microscope are subjected to correspondence analysis. The structure inherent in the data in the resulting low-dimensional factor space is characterized by a mixed classification method which combines the dynamic clouds clustering technique with hierarchical ascendant classification (HAC). For our data, the rejection of marginal clusters obtained by dynamic clouds clustering appears as a crucial prerequisite for a stable performance of HAC. The method is applied to two sets of 204 and 177 images that show the 70S ribosome of Escherichia coli, in the range of overlap views as defined by A. Verschoor and co-workers, and to two sets of 480 and 496 images of the 50S subunit of E. coli depleted of L7/L12 proteins in the well-defined crown view. Reproducible classes are obtained, which are characterized by images reconstituted from factorial coordinates. These classes appear to be related to different orientations on the specimen grid (in the case of the 70S particle) and to different conformational states (50S subunit).     

A guided tour into subcellular colocalization analysis in light microscopy

It is generally accepted that the functional compartmentalization of eukaryotic cells is reflected by the differential occurrence of proteins in their compartments. The location and physiological function of a protein are closely related; local information of a protein is thus crucial to understanding its role in biological processes. The visualization of proteins residing on intracellular structures by fluorescence microscopy has become a routine approach in cell biology and is increasingly used to assess their colocalization with well‐characterized markers. However, image‐analysis methods for colocalization studies are a field of contention and enigma. We have therefore undertaken to review the most currently used colocalization analysis methods, introducing the basic optical concepts important for image acquisition and subsequent analysis. We provide a summary of practical tips for image acquisition and treatment that should precede proper colocalization analysis. Furthermore, we discuss the application and feasibility of colocalization tools for various biological colocalization situations and discuss their respective strengths and weaknesses. We have created a novel toolbox for subcellular colocalization analysis under ImageJ, named JACoP, that integrates current global statistic methods and a novel object‐based approach.     

Enhancement of confocal microscopy images using Mueller-matrix polarimetry

A simplified procedure based on Mueller-matrix polarimetry has recently been reported as a method of retinal image improvement in a confocal ophthalmoscope [J. M. Bueno et al ., J. Opt. Soc. Am. A 24, 1337 (2007)]. Here, we have applied the technique to imaging static samples providing well-defined reflection properties. The method uses a generator of polarization states in the illumination pathway of a confocal scanning laser system. From the calculated four elements of the Mueller matrix of any sample and instrument combination, the best images defined by different metrics were constructed. For samples with specular, diffuse and mixed reflections, the best-constructed images showed an enhancement in both objective and subjective image quality compared to the original images and those obtained from frame averaging. This technique could improve microscopic imaging in many diverse fields, particularly in biomedical imaging.     

A quantitative method for analysing AFM images of the outer surfaces of human hair

Cuticle step height is an important parameter for the quantitative assessment of human hair. This paper describes a novel, computational method for the rapid calculation of step heights from atomic force microscope images obtained from large numbers of specimens. Such an approach is necessary to allow a statistical analysis of the inherently wide distribution of cuticle step heights characteristic of a single hair sample. The method described will be of use to cosmetic formulation chemists and forensic scientists and also to dermatologists in the field of disease diagnosis.     

Algorithms for accurate 3D registration of neuronal images acquired by confocal scanning laser microscopy

This paper presents automated and accurate algorithms based on high‐order transformation models for registering three‐dimensional (3D) confocal images of dye‐injected neurons. The algorithms improve upon prior methods in several ways, and meet the more stringent image registration needs of applications such as two‐view attenuation correction recently developed by us. First, they achieve high accuracy (≈ 1.2 voxels, equivalent to 0.4 µm) by using landmarks, rather than intensity correlations, and by using a high‐dimensional affine and quadratic transformation model that accounts for 3D translation, rotation, non‐isotropic scaling, modest curvature of field, distortions and mechanical inconsistencies introduced by the imaging system. Second, they use a hierarchy of models and iterative algorithms to eliminate potential instabilities. Third, they incorporate robust statistical methods to achieve accurate registration in the face of inaccurate and missing landmarks. Fourth, they are fully automated, even estimating the initial registration from the extracted landmarks. Finally, they are computationally efficient, taking less than a minute on a 900‐MHz Pentium III computer for registering two images roughly 70 MB in size. The registration errors represent a combination of modelling, estimation, discretization and neuron tracing errors. Accurate 3D montaging is described; the algorithms have broader applicability to images of vasculature, and other structures with distinctive point, line and surface landmarks.     

Localizing and extracting filament distributions from microscopy images

Detailed quantitative measurements of biological filament networks represent a crucial step in understanding architecture and structure of cells and tissues, which in turn explain important biological events such as wound healing and cancer metastases. Microscopic images of biological specimens marked for different structural proteins constitute an important source for observing and measuring meaningful parameters of biological networks. Unfortunately, current efforts at quantitative estimation of architecture and orientation of biological filament networks from microscopy images are predominantly limited to visual estimation and indirect experimental inference. Here, we describe a new method for localizing and extracting filament distributions from 2D microscopy images of different modalities. The method combines a filter‐based detection of pixels likely to contain a filament with a constrained reverse diffusion‐based approach for localizing the filaments centrelines. We show with qualitative and quantitative experiments, using both simulated and real data, that the new method can provide more accurate centreline estimates of filament in comparison to other approaches currently available. In addition, we show the algorithm is more robust with respect to variations in the initial filter‐based filament detection step often used. We demonstrate the application of the method in extracting quantitative parameters from confocal microscopy images of actin filaments and atomic force microscopy images of DNA fragments.     

Application of regularized Richardson-Lucy algorithm for deconvolution of confocal microscopy images

Although confocal microscopes have considerably smaller contribution of out-of-focus light than widefield microscopes, the confocal images can still be enhanced mathematically if the optical and data acquisition effects are accounted for. For that, several deconvolution algorithms have been proposed. As a practical solution, maximum-likelihood algorithms with regularization have been used. However, the choice of regularization parameters is often unknown although it has considerable effect on the result of deconvolution process. The aims of this work were: to find good estimates of deconvolution parameters; and to develop an open source software package that would allow testing different deconvolution algorithms and that would be easy to use in practice. Here, Richardson-Lucy algorithm has been implemented together with the total variation regularization in an open source software package IOCBio Microscope. The influence of total variation regularization on deconvolution process is determined by one parameter. We derived a formula to estimate this regularization parameter automatically from the images as the algorithm progresses. To assess the effectiveness of this algorithm, synthetic images were composed on the basis of confocal images of rat cardiomyocytes. From the analysis of deconvolved results, we have determined under which conditions our estimation of total variation regularization parameter gives good results. The estimated total variation regularization parameter can be monitored during deconvolution process and used as a stopping criterion. An inverse relation between the optimal regularization parameter and the peak signal-to-noise ratio of an image is shown. Finally, we demonstrate the use of the developed software by deconvolving images of rat cardiomyocytes with stained mitochondria and sarcolemma obtained by confocal and widefield microscopes.     

Multicolour analysis and local image correlation in confocal microscopy

Multiparameter fluorescence microscopy is often used to identify cell types and subcellular organelles according to their differential labelling. For thick objects, the quantitative comparison of different multiply labelled specimens requires the three-dimensional (3-D) sampling capacity of confocal laser scanning microscopy, which can be used to generate pseudocolour images. To analyse such 3-D data sets, we have created pixel fluorogram representations, which are estimates of the joint probability densities linking multiple fluorescence distributions. Such pixel fluorograms also provide a powerful means of analysing image acquisition noise, fluorescence cross-talk, fluorescence photobleaching and cell movements. To identify true fluorescence co-localization, we have developed a novel approach based on local image correlation maps. These maps discriminate the coincident fluorescence distributions from the superimposition of noncorrelated fluorescence profiles on a local basis, by correcting for contrast and local variations in background intensity in each fluorescence channel. We believe that the pixel fluorograms are best suited to the quality control of multifluorescence image acquisition. The local image correlation methods are more appropriate for identifying co-localized structures at the cellular or subcellular level. The thresholding of these correlation maps can further be used to recognize and classify biological structures according to multifluorescence attributes.     

Explorative statistical analysis of planar point processes in microscopy

Basic methods of explorative statistical analysis for stationary and isotropic planar point processes are briefly and informally reviewed. At the explorative level, planar point patterns may be characterized in terms of the intensity, the K-function and the pair correlation function. These second-order functions enable one to classify a given point process as completely random, clustering or repulsive. The repulsive behaviour may be quantified by an estimate of the hard-core distance. In the exploratory approach, the statistics are essentially free from model assumptions. Second-order spatial functions have been estimated to characterize genuine planar point processes in the macroscopic domain, for example in forestry, geography and epidemiology. For light microscopy and transmission electron microscopy, two situations are distinguished, which may be summarized as the genuine planar case and the stereological case. In the genuine planar case, a direct interpretation of the results of spatial statistics is feasible. Here, monolayers in cell culture, intramembranous particles on freeze fracture specimens and amacrine cells of the retina are mentioned as examples. In the stereological case, point patterns are generated by sections through 3D structures. Here the observed point patterns may arise as the centres of sectional profiles of particles, or as centres of sectional profiles of spatial fibre processes. In both situations, exploratory spatial point process statistics allow a quantitative characterization of sectional images for the purposes of group comparisons and classification. Moreover, for spatial fibre processes it has recently been shown that the observed pair correlation function of the centres of the fibre profiles is an estimate of the reduced pair correlation function of the fibre process in 3D. Hence for fibre processes a stereological interpretation of point process statistics obtained from sections is an additional option.     

Pipeline for illumination correction of images for high‐throughput microscopy

The presence of systematic noise in images in high‐throughput microscopy experiments can significantly impact the accuracy of downstream results. Among the most common sources of systematic noise is non‐homogeneous illumination across the image field. This often adds an unacceptable level of noise, obscures true quantitative differences and precludes biological experiments that rely on accurate fluorescence intensity measurements. In this paper, we seek to quantify the improvement in the quality of high‐content screen readouts due to software‐based illumination correction. We present a straightforward illumination correction pipeline that has been used by our group across many experiments. We test the pipeline on real‐world high‐throughput image sets and evaluate the performance of the pipeline at two levels: (a) Z′‐factor to evaluate the effect of the image correction on a univariate readout, representative of a typical high‐content screen, and (b) classification accuracy on phenotypic signatures derived from the images, representative of an experiment involving more complex data mining. We find that applying the proposed post‐hoc correction method improves performance in both experiments, even when illumination correction has already been applied using software associated with the instrument. To facilitate the ready application and future development of illumination correction methods, we have made our complete test data sets as well as open‐source image analysis pipelines publicly available. This software‐based solution has the potential to improve outcomes for a wide‐variety of image‐based HTS experiments.     

Artefacts in restored images due to intensity loss in three-dimensional fluorescence microscopy

Computational algorithms for three-dimensional deconvolution have proven successful in reducing blurring and improving the resolution of fluorescence microscopic images. However, discrepancies between the imaging conditions and the models on which such deconvolution algorithms are based may lead to artefacts and/or distortions in the images restored by application of the algorithms. In this paper, artefacts associated with a decrease of fluorescence intensity with time or slice in three-dimensional wide-field images are demonstrated using simulated images. Loss of intensity, whether due to photobleaching or other factors, leads to artefacts in the form of bands or stripes in the restored images. An empirical method for correcting the intensity losses in wide-field images has been implemented and used to correct biological images. This method is based on fitting a decreasing function to the slice intensity curve computed by summing all pixel values in each slice. The fitted curve is then used for the calculation of correction factors for each slice.     

Scanning luminescence X-ray microscopy: Imaging fluorescence dyes at suboptical resolution

Scanning luminescence X-ray microscopy is based on the use of the very small focused probe of a scanning X-ray microscope to stimulate visible light emission from phosphors and dyes. Using an undulator X-ray source and a Fresnel zone plate to produce a focused X-ray probe, images of P31 phosphor grains with a resolution of 50–75 nm have been obtained, and luminescence from polystyrene spheres loaded with 50–100 μmol/g of fluorescent dye has been imaged. The resolution was not limited by the focused X-ray probe (the microscope has imaged features at 36-nm spacing in transmission mode) but by dark noise and the low net efficiency of the luminescence detection system used for this investigation. This technique may make it possible to image dye-tagged sites of biochemical activity at the resolution of the X-ray microscope in wet, unsectioned, and unfixed cells, especially with soft X-ray optimized dyes. Because the image is formed from the detection of signal against a dark background, calculations suggest that the radiation dose for luminescence imaging of dye-tagged features should be 2–22 times lower than it is in transmission X-ray microscopy. A possible extension of the technique for three-dimensional imaging at the transverse resolution of the X-ray microscope is described, where visible light collection optics might be used to obtain submicrometre axial resolution.