A review on low-cost microscopes for Open Science.

This article presents a review after an exhaustive search that yielded 23 works carried out in the last decade for the availability of optical microscopes with open hardware as a low-cost alternative to commercial systems. These works were developed with the aim of covering needs within several areas such as: Bio Sciences research in institutions with limited resources, diagnosis of diseases and health screenings in large populations in developing countries, and training in educational contexts with a need for high availability of equipment and low replacement cost. The analysis of the selected works allows us to classify the analyzed solutions into two main categories, for which their essential characteristics are enumerated: portable field microscopes and multipurpose automated microscopes. Moreover, this work includes a discussion on the degree of maturity of the solutions in terms of the adoption of practices aligned with the development of Open Science. RESEARCH HIGHLIGHTS: Concise review on low-cost microscopes for developing Open Science, exposing the role of smartphone-based microscopy. The work classifies microscopes in two main categories: (1) portable field microscopes, and (2) multipurpose automated microscopes.

Multi-exposure microscopic image fusion-based detail enhancement algorithm.

Traditional microscope imaging techniques are unable to retrieve the complete dynamic range of a diatom species with complex silica-based cell walls and multi-scale patterns. In order to extract details from the diatom, multi-exposure images are captured at variable exposure settings using microscopy techniques. A recent innovation shows that image fusion overcomes the limitations of standard digital cameras to capture details from high dynamic range scene or specimen photographed using microscopy imaging techniques. In this paper, we present a cell-region sensitive exposure fusion (CS-EF) approach to produce well-exposed fused images that can be presented directly on conventional display devices. The ambition is to preserve details in poorly and brightly illuminated regions of 3-D transparent diatom shells. The aforesaid objective is achieved by taking into account local information measures, which select well-exposed regions across input exposures. In addition, a modified histogram equalization is introduced to improve uniformity of input multi-exposure image prior to fusion. Quantitative and qualitative assessment of proposed fusion results reveal better performance than several state-of-the-art algorithms that substantiate the method's validity.

Nonsubsampled contourlet transform based tone-mapping operator to optimize the dynamic range of diatom shells.

The diatoms have intricate silica-based cell walls with multi-scale patterns. High dynamic range (HDR) imaging is widely used to examine the three-dimensional structure of diatoms for recovering the wide range of contrast and brightness. In order to construct a HDR image of a diatom, multiple images of the specimen are taken at different exposure settings with bright or dark field microscopy. In the proposed method, multi-scale decomposition based on nonsubsampled contourlet transform is adopted to separate the structured and detailed information of the HDR image. And then, by processing all layers independently, the tone-mapped image is reconstructed to retain details present in the dark and light regions. Quantitative and qualitative analysis is performed in order to assess the performance of the proposed and seven existing tone-mapping operators. In analysis, the study indicates that the proposed method enhances the diatom frustules to extract more details.

Diatom identification including life cycle stages through morphological and texture descriptors.

Diatoms are unicellular algae present almost wherever there is water. Diatom identification has many applications in different fields of study, such as ecology, forensic science, etc. In environmental studies, algae can be used as a natural water quality indicator. The diatom life cycle consists of the set of stages that pass through the successive generations of each species from the initial to the senescent cells. Life cycle modeling is a complex process since in general the distribution of the parameter vectors that represent the variations that occur in this process is non-linear and of high dimensionality. In this paper, we propose to characterize the diatom life cycle by the main features that change during the algae life cycle, mainly the contour shape and the texture. Elliptical Fourier Descriptors (EFD) are used to describe the diatom contour while phase congruency and Gabor filters describe the inner ornamentation of the algae. The proposed method has been tested with a small algae dataset (eight different classes and more than 50 samples per type) using supervised and non-supervised classification techniques obtaining accuracy results up to 99% and 98% respectively.

Low-cost oblique illumination: an image quality assessment.

We study the effectiveness of several low-cost oblique illumination filters to improve overall image quality, in comparison with standard bright field imaging. For this purpose, a dataset composed of 3360 diatom images belonging to 21 taxa was acquired. Subjective and objective image quality assessments were done. The subjective evaluation was performed by a group of diatom experts by psychophysical test where resolution, focus, and contrast were assessed. Moreover, some objective nonreference image quality metrics were applied to the same image dataset to complete the study, together with the calculation of several texture features to analyze the effect of these filters in terms of textural properties. Both image quality evaluation methods, subjective and objective, showed better results for images acquired using these illumination filters in comparison with the no filtered image. These promising results confirm that this kind of illumination filters can be a practical way to improve the image quality, thanks to the simple and low cost of the design and manufacturing process.

Oblique illumination in microscopy: A quantitative evaluation.

Many biological objects are barely distinguished with the brightfield microscope because they appear transparent, translucent and colourless. One simple way to make such specimens visible without compromising contrast and resolution is by controlling the amount and the directionality of the illumination light. Oblique illumination is an old technique described by many scientists and microscopists that however has been largely neglected in favour of other alternative methods. Oblique lighting (OL) is created by illuminating the sample by only a portion of the light coming from the condenser. If properly used it can improve the resolution and contrast of transparent specimens such as diatoms. In this paper a quantitative evaluation of OL in brigthfield microscopy is presented. Several feature descriptors were selected for characterising contrast and sharpness showing that in general OL provides better performance for distinguishing minute details compared to other lighting modalities. Oblique lighting is capable to produce directionally shadowed differential contrast images allowing to observe phase details in a similar way to differential contrast images (DIC) but at lower cost. The main advantage of OL is that the resolution of the light microscope can be increased by effectively doubling the angular aperture. OL appears as a cost-effective technique both for the amateur and professional scientist that can be used as a replacement of DIC or phase contrast when resources are scarce.

Quantitative Assessment of Emphysema Severity in Histological Lung Analysis.

Emphysema is a characteristic component of chronic obstructive pulmonary disease (COPD), which has been pointed out as one of the main causes of mortality for the next years. Animal models of emphysema are employed to study the evolution of this disease as well as the effect of treatments. In this context, measures such as the mean linear intercept [Formula: see text] and the equivalent diameter [Formula: see text] have been proposed to quantify the airspace enlargement associated with emphysematous lesions in histological sections. The parameter [Formula: see text], which relates the second and the third moments of the variable [Formula: see text], has recently shown to be a robust descriptor of airspace enlargement. However, the value of [Formula: see text] does not provide a direct evaluation of emphysema severity. In our research, we suggest a Bayesian approach to map [Formula: see text] onto a novel emphysema severity index (SI) reflecting the probability for a lung area to be emphysematous. Additionally, an image segmentation procedure was developed to compute the severity map of a lung section using the SI function. Severity maps corresponding to 54 lung sections from control mice, mice induced with mild emphysema and mice induced with severe emphysema were computed, revealing differences between the distribution of SI in the three groups. The proposed methodology could then assist in the quantification of emphysema severity in animal models of pulmonary disease.

Automated pollen identification using microscopic imaging and texture analysis.

Pollen identification is required in different scenarios such as prevention of allergic reactions, climate analysis or apiculture. However, it is a time-consuming task since experts are required to recognize each pollen grain through the microscope. In this study, we performed an exhaustive assessment on the utility of texture analysis for automated characterisation of pollen samples. A database composed of 1800 brightfield microscopy images of pollen grains from 15 different taxa was used for this purpose. A pattern recognition-based methodology was adopted to perform pollen classification. Four different methods were evaluated for texture feature extraction from the pollen image: Haralick's gray-level co-occurrence matrices (GLCM), log-Gabor filters (LGF), local binary patterns (LBP) and discrete Tchebichef moments (DTM). Fisher's discriminant analysis and k-nearest neighbour were subsequently applied to perform dimensionality reduction and multivariate classification, respectively. Our results reveal that LGF and DTM, which are based on the spectral properties of the image, outperformed GLCM and LBP in the proposed classification problem. Furthermore, we found that the combination of all the texture features resulted in the highest performance, yielding an accuracy of 95%. Therefore, thorough texture characterisation could be considered in further implementations of automatic pollen recognition systems based on image processing techniques.

Extended Gabor approach applied to classification of emphysematous patterns in computed tomography.

Chronic obstructive pulmonary disease (COPD) is a progressive and irreversible lung condition typically related to emphysema. It hinders air from passing through airpaths and causes that alveolar sacs lose their elastic quality. Findings of COPD may be manifested in a variety of computed tomography (CT) studies. Nevertheless, visual assessment of CT images is time-consuming and depends on trained observers. Hence, a reliable computer-aided diagnosis system would be useful to reduce time and inter-evaluator variability. In this paper, we propose a new emphysema classification framework based on complex Gabor filters and local binary patterns. This approach simultaneously encodes global characteristics and local information to describe emphysema morphology in CT images. Kernel Fisher analysis was used to reduce dimensionality and to find the most discriminant nonlinear boundaries among classes. Finally, classification was performed using the k-nearest neighbor classifier. The results have shown the effectiveness of our approach for quantifying lesions due to emphysema and that the combination of descriptors yields to a better classification performance.

Texture classification using discrete Tchebichef moments.

In this paper, a method to characterize texture images based on discrete Tchebichef moments is presented. A global signature vector is derived from the moment matrix by taking into account both the magnitudes of the moments and their order. The performance of our method in several texture classification problems was compared with that achieved through other standard approaches. These include Haralick's gray-level co-occurrence matrices, Gabor filters, and local binary patterns. An extensive texture classification study was carried out by selecting images with different contents from the Brodatz, Outex, and VisTex databases. The results show that the proposed method is able to capture the essential information about texture, showing comparable or even higher performance than conventional procedures. Thus, it can be considered as an effective and competitive technique for texture characterization.

No-reference image quality assessment through the von Mises distribution.

An innovative way of calculating the von Mises distribution of image entropy is introduced in this paper. The von Mises distribution's concentration parameter and some fitness parameter that will be defined later have been analyzed in the experimental part for determining their suitability as an image quality assessment measure in some particular distortions such as Gaussian blur or additive Gaussian noise. To achieve such measure, the local Rényi entropy is calculated in four equally spaced orientations and used to determine the parameters of the von Mises distribution of the image entropy. Considering contextual images, experimental results after applying this model show that the best-in-focus noise-free images are associated with the highest values for the von Mises distribution concentration parameter and the highest approximation of image data to the von Mises distribution model. Our defined von Mises fitness parameter experimentally appears also as a suitable no-reference image quality assessment indicator for no-contextual images.

Anisotropy-based robust focus measure for non-mydriatic retinal imaging.

Non-mydriatic retinal imaging is an important tool for diagnosis and progression assessment of ophthalmic diseases. Because it does not require pharmacological dilation of the patient's pupil, it is essential for screening programs performed by non-medical personnel. A typical camera is equipped with a manual focusing mechanism to compensate for the refractive errors in the eye. However, manual focusing is error prone, especially when performed by inexperienced photographers. In this work, we propose a new and robust focus measure based on a calculation of image anisotropy which, in turn, is evaluated from the directional variance of the normalized discrete cosine transform. Simulation and experimental results demonstrate the effectiveness of the proposed focus measure.

Autofocus evaluation for brightfield microscopy pathology.

An essential and indispensable component of automated microscopy framework is the automatic focusing system, which determines the in-focus position of a given field of view by searching the maximum value of a focusing function over a range of z-axis positions. The focus function and its computation time are crucial to the accuracy and efficiency of the system. Sixteen focusing algorithms were analyzed for histological and histopathological images. In terms of accuracy, results have shown an overall high performance by most of the methods. However, we included in the evaluation study other criteria such as computational cost and focusing curve shape which are crucial for real-time applications and were used to highlight the best practices.

Comparative evaluation of autofocus algorithms for a real-time system for automatic detection of Mycobacterium tuberculosis.

Microscopy images must be acquired at the optimal focal plane for the objects of interest in a scene. Although manual focusing is a standard task for a trained observer, automatic systems often fail to properly find the focal plane under different microscope imaging modalities such as bright field microscopy or phase contrast microscopy. This article assesses several autofocus algorithms applied in the study of fluorescence-labeled tuberculosis bacteria. The goal of this work was to find the optimal algorithm in order to build an automatic real-time system for diagnosing sputum smear samples, where both accuracy and computational time are important. We analyzed 13 focusing methods, ranging from well-known algorithms to the most recently proposed functions. We took into consideration criteria that are inherent to the autofocus function, such as accuracy, computational cost, and robustness to noise and to illumination changes. We also analyzed the additional benefit provided by preprocessing techniques based on morphological operators and image projection profiling.

Wavefield imaging via iterative retrieval based on phase modulation diversity.

We present a fast and robust non-interferomentric wavefield retrieval approach suitable for imaging of both amplitude and phase distributions of scalar coherent beams. It is based on the diversity of the intensity measurements obtained under controlled astigmatism and it can be easily implemented in standard imaging systems. Its application for imaging in microscopy is experimentally studied. Relevant examples illustrate the approach capabilities for image super-resolution, numerical refocusing, quantitative imaging and phase mapping.

Microparticle movements in optical funnels and pods.

Three-dimensional microparticle movements induced by laser beams with a funnel- and tubular pod-like structure, in the neighbourhood of the focal plane of an optical trapping setup, are experimentally studied. The funnel and pod beams constructed as coherent superpositions of helical Laguerre-Gaussian modes are synthesized by a computer generated hologram using a phase-only spatial light modulator. Particle tracking is achieved by in-line holography method which allows an accurate position measurement. It is experimentally demonstrated that the trapped particle follows different trajectories depending on the orbital angular momentum density of the beam. In particular applying the proposed pod beam the particle rotates in opposite directions during its movement in the optical trap. Possible applications of these single-beam traps for volumetric optical particle manipulation are discussed.

Discrimination of isotrigon textures using the Rényi entropy of Allan variances.

We present a computational algorithm for isotrigon texture discrimination. The aim of this method consists in discriminating isotrigon textures against a binary random background. The extension of the method to the problem of multitexture discrimination is considered as well. The method relies on the fact that the information content of time or space-frequency representations of signals, including images, can be readily analyzed by means of generalized entropy measures. In such a scenario, the Rényi entropy appears as an effective tool, given that Rényi measures can be used to provide information about a local neighborhood within an image. Localization is essential for comparing images on a pixel-by-pixel basis. Discrimination is performed through a local Rényi entropy measurement applied on a spatially oriented 1-D pseudo-Wigner distribution (PWD) of the test image. The PWD is normalized so that it may be interpreted as a probability distribution. Prior to the calculation of the texture's PWD, a preprocessing filtering step replaces the original texture with its localized spatially oriented Allan variances. The anisotropic structure of the textures, as revealed by the Allan variances, turns out to be crucial later to attain a high discrimination by the extraction of Rényi entropy measures. The method has been empirically evaluated with a family of isotrigon textures embedded in a binary random background. The extension to the case of multiple isotrigon mosaics has also been considered. Discrimination results are compared with other existing methods.

Blind image quality assessment through anisotropy.

We describe an innovative methodology for determining the quality of digital images. The method is based on measuring the variance of the expected entropy of a given image upon a set of predefined directions. Entropy can be calculated on a local basis by using a spatial/spatial-frequency distribution as an approximation for a probability density function. The generalized Rényi entropy and the normalized pseudo-Wigner distribution (PWD) have been selected for this purpose. As a consequence, a pixel-by-pixel entropy value can be calculated, and therefore entropy histograms can be generated as well. The variance of the expected entropy is measured as a function of the directionality, and it has been taken as an anisotropy indicator. For this purpose, directional selectivity can be attained by using an oriented 1-D PWD implementation. Our main purpose is to show how such an anisotropy measure can be used as a metric to assess both the fidelity and quality of images. Experimental results show that an index such as this presents some desirable features that resemble those from an ideal image quality function, constituting a suitable quality index for natural images. Namely, in-focus, noise-free natural images have shown a maximum of this metric in comparison with other degraded, blurred, or noisy versions. This result provides a way of identifying in-focus, noise-free images from other degraded versions, allowing an automatic and nonreference classification of images according to their relative quality. It is also shown that the new measure is well correlated with classical reference metrics such as the peak signal-to-noise ratio.

A unified approach to superresolution and multichannel blind deconvolution.

This paper presents a new approach to the blind deconvolution and superresolution problem of multiple degraded low-resolution frames of the original scene. We do not assume any prior information about the shape of degradation blurs. The proposed approach consists of building a regularized energy function and minimizing it with respect to the original image and blurs, where regularization is carried out in both the image and blur domains. The image regularization based on variational principles maintains stable performance under severe noise corruption. The blur regularization guarantees consistency of the solution by exploiting differences among the acquired low-resolution images. Several experiments on synthetic and real data illustrate the robustness and utilization of the proposed technique in real applications.

Gradient representation and perception in the early visual system--a novel account of Mach band formation.

Recent evidence suggests that object surfaces and their properties are represented at early stages in the visual system of primates. Most likely invariant surface properties are extracted to endow primates with robust object recognition capabilities. In real-world scenes, luminance gradients are often superimposed on surfaces. We argue that gradients should also be represented in the visual system, since they encode highly variable information, such as shading, focal blur, and penumbral blur. We present a neuronal architecture which was designed and optimized for segregating and representing luminance gradients in real-world images. Our architecture in addition provides a novel theory for Mach bands, whereby corresponding psychophysical data are predicted consistently.