Digital Imaging Microscopy Research Articles

Dilated and soft attention‐guided convolutional neural network for breast cancer histology images classification

Breast cancer is one of the most common types of cancer in women, and histopathological imaging is considered the gold standard for its diagnosis. However, the great complexity of histopathological images and the considerable workload make this work extremely time-consuming, and the results may be affected by the subjectivity of the pathologist. Therefore, the development of an accurate, automated method for analysis of histopathological images is critical to this field. In this article, we propose a deep learning method guided by the attention mechanism for fast and effective classification of haematoxylin and eosin-stained breast biopsy images. First, this method takes advantage of DenseNet and uses the feature map's information. Second, we introduce dilated convolution to produce a larger receptive field. Finally, spatial attention and channel attention are used to guide the extraction of the most useful visual features. With the use of fivefold cross-validation, the best model obtained an accuracy of 96.47% on the BACH2018 dataset. We also evaluated our method on other datasets, and the experimental results demonstrated that our model has reliable performance. This study indicates that our histopathological image classifier with a soft attention-guided deep learning model for breast cancer shows significantly better results than the latest methods. It has great potential as an effective tool for automatic evaluation of digital histopathological microscopic images for computer-aided diagnosis.

Intelligent algorithm for detection of dengue using mobilenetv2‐based deep features with lymphocyte nucleus

AbstractDengue is a vector‐borne disease that is highly endemic in countries located in tropical regions. It can cause severe complications and can even lead to death in the case of delayed diagnosis. Detection of dengue is done by manually examining the platelets and lymphocytes in Leishman's stained peripheral blood smear (PBS) images. PBS examination is considered the gold standard for diagnosing various haematological disorders. However, manual analysis of the PBS is labour‐intensive, tedious, and time‐consuming, requiring a skilled and experienced haematologist. Today, soft computing methods and artificial intelligence have made their way into every science and technology branch. One such area which has adopted this approach is digital pathology, for automatically identifying and diagnosing various diseases. The main objective of this work was to design an intelligent algorithm to classify normal and dengue patients with the help of digital microscopic blood smear images. A total of 94 normal and dengue‐infected PBSs were acquired at a magnification of 100×. Grey‐level segmentation based on Otsu's thresholding was used for the segmentation of the nucleus of lymphocytes. Distinct features from the nucleus that differentiated infected cells from normal were extracted using a pre‐trained MobileNetV2 network and local binary pattern. Significant features were selected using the ReliefF algorithm. Subsequently, these features were fed to the support vector machine (SVM) classifier. Our proposed system gave an accuracy, sensitivity, and specificity of 95.74%, 98.14%, and 92.50%, respectively. Hence, the developed intelligent model with deep and hand‐crafted features can be valuable for dengue diagnosis.

Investigation of anisotropy effects in glass fibre reinforced polymer composites on tensile and shear properties using full field strain measurement and finite element multi-scale techniques

Designing highly stressed offshore renewable energy composite structures (e.g. wind and tidal turbine blades) necessitates characterisation of woven fabric composite under off axial loading. In this work a combined method of finite element analysis, digital image correlation and microscopy is used to assess the effect of ply orientation on the tensile/shear properties and failure modes of woven glass fibre reinforced polymer composites. Full field strain maps obtained by the digital image correlation method were used to evaluate the damage development and the inhomogeneity of strain localisation. The development of finite element models of mechanical test specimens is based on the analysis of micro-mechanical models of representative volume elements using a homogenisation technique in order to calculate the effective orthotropic properties. The agreement between numerically and experimentally calculated strains obtained in the elastic regimes indicates that stress analysis conducted by numerical methods is useful when characterising the effect of ply orientation on mechanical behaviour. Strain measurement conducted by the digital image correlation method indicated that there is a strong relationship between the strain distribution and the microstructure/ply orientation. In addition, it was found that the levels of localised tensile strain are higher than the global strain indicating the structural heterogeneity of the composite material. Finally, microstructural analysis of tension and shear test specimens showed that the main failure modes are de-bonded fibres, fibre pull out, in-plane/inter-laminar shear cracks and delamination.

Feasibility of chitosan thin film and hybrid chitosan/silica thin film for the mitigation of toxic Alexandrium minutum

Harmful Algal Bloom (HAB) has been a growing issue worldwide for the past few decades, posing harm to aquatic ecosystems and drinking water quality. Therefore, this research aims to investigate the feasibility of a chitosan thin film and hybrid chitosan/silica thin film prepared via a one-pot synthesis method to mitigate toxic algae cells, Alexandrium minutum. The film-forming solution was synthesised by stirring chitosan and sodium silicate solutions obtained from rice husk ash (RHA). The films were neutralised with NaOH and distilled water and finally dried at room temperature. The thin films were characterised using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and scanning electron microscopy (SEM). The SEM analysis indicates that the new thin film’s surface was smooth compared to the used thin films. The mitigation of A. minutum was carried out for 72 h. The chitosan film achieved 18.1 % removal of A. minutum at 72 h, whereas 23.8% of the algae was removed when the hybrid chitosan/silica thin film was used. Digital microscopic imaging showed that the algae cells were adsorbed on the chitosan film’s surface ruptured, whereas the adsorbed cells on the hybrid films were preserved. The ATR-FTIR analysis of the used film indicates the presence of the algae cells, which changes the functional group of the film. The film can be separated easily from the seawater and exhibit good biodegradable properties as it degrades after being buried in the soil for one month.

Automatic discrimination of Yamamoto-Kohama classification by machine learning approach for invasive pattern of oral squamous cell carcinoma using digital microscopic images: a retrospective study

The Yamamoto-Kohama criteria are clinically useful for determining the mode of tumor invasion, especially in Japan. However, this evaluation method is based on subjective visual findings and has led to significant differences in determinations between evaluators and facilities. In this retrospective study, we aimed to develop an automatic method of determining the mode of invasion based on the processing of digital medical images. Using 101 digitized photographic images of anonymized stained specimen slides, we created a classifier that allowed clinicians to introduce feature values and subjected the cases to machine learning using a random forest approach. We then compared the Yamamoto-Kohama grades (1, 2, 3, 4C, 4D) determined by a human oral and maxillofacial surgeon with those determined using the machine learning approach. The input of multiple test images into the newly created classifier yielded an overall F-measure value of 87% (grade 1, 93%; grade 2, 67%; grade 3, 89%; grade 4C, 83%; grade 4D, 94%). These results suggest that the output of the classifier was very similar to the judgments of the clinician. This system may be valuable for diagnostic support to provide an accurate determination of the mode of invasion.

3D-Printed Portable Robotic Mobile Microscope for Remote Diagnosis of Global Health Diseases

Microscopy plays a crucial role in the diagnosis of numerous diseases. However, the need for trained microscopists and pathologists, the complexity of pathology, and the accessibility and affordability of the technology can hinder the provision of rapid and high-quality diagnoses and healthcare. In this work, we present an affordable, 3D-printed, portable, robotic, mobile-based slide scanning microscope. The proposed device is composed of electronic, mechanical, and optical modules operated via smartphone with a control app. The device is connected and fully integrated with a telemedicine web platform, where digitized microscopy images can be remotely visualized and analyzed. The robotic scanner, which has approximately 1-µm resolution, has been evaluated in two clinical scenarios with histology and stool samples. The results showed sufficient image quality for performing a proper diagnosis in all cases under study.

Registration of Regeneration in Planarians from Photographic Images

In this study, an approach to constructing a mathematical model for quantifying the dynamics of regeneration of planarian flatworms in biological experiments is considered, based on an analysis of a series of digital microscopic images. A method is proposed to describe the body shape of a planarian using a continuous morphological model, based on the concept of a medial representation of the worm’s silhouette. The silhouette in this case is a polygon approximating the contours of the planarian’s body. The medial representation of the figure includes a medial axis and a radial function that describes the width of the figure relative to the medial axis. We propose a set of morphological criteria for assessing the dynamics of regeneration based on a continuous morphological model and present the results of computational experiments.

Malaria detection through digital microscopic imaging using Deep Greedy Network with transfer learning.

Purpose: In conventional diagnosis, the visual inspection of the malaria parasite Plasmodium falciparum in infected red blood cells under a microscope, is done manually by pathologists, which is both laborious and error-prone. Recent studies on automating this process have been conducted using artificial intelligence and feature selection of positional and morphological features from blood smear cell images using convolutional neural network (CNN). However, most deep CNN models do not perform well as per the expectation on small datasets. Approach: In this context, we propose a comprehensive computer-aided diagnosis scheme for automating the detection of malaria parasites in thin blood smear images using deep CNN, where transfer learning is used for optimizing the feature selection process. As an extra layer of security, layer embeddings are extracted from the intermediate convolutional layers using the feature matrix to cross-check the selection of features in the intermediate layers. The proposal includes the utilization of the ResNet 152 model integrated with the Deep Greedy Network for training, which produces an enhanced quality of prediction. Results: The performance of the proposed hybrid model has been evaluated concerning the evaluation metrics such as accuracy, precision, recall, specificity, and F1-score, which has been further compared with the pre-existing deep learning algorithms. Conclusions: The comparative analysis of the results reported based on the accuracy metrics demonstrates promising outcomes concerning the other models. Lastly, the embedding extraction from the intermediate hidden layers and their visual analysis also provides an opportunity for manual verification of the performance of the trained model.

Acoustic performance optimization of a cementitious composite with a porous medium

This paper presents an optimization algorithm for band-gap maximization aimed at designing the porosity of a cementitious composite. Numerical analysis revealed the relationship between the composite porosity and band structure. With the utilization of gravels as the main aggregate, microbeads and a vesicant were also mixed to produce the porous cementitious composite, where correlative experiments were conducted on the mixture proportion. Digital microscope images were applied to evaluate the micro-construction details of the composite. The acoustic performance of the porous concrete was tested by an impedance tube. The results show that the maximum sound absorption coefficient of the phononic crystal composed of 4 mm gravels and surrounding microbeads can reach 90%. The frequency corresponding to the peak value is 1490 Hz, which is consistent with the frequency range of the band gap from theoretical derivation and simulation analysis. This developed methodology is expected to be applied to the design of cementitious structures to achieve sound absorption performance enhancement.

Forensic medical evaluation of hemorrhages into the brain of traumatic and non-traumatic genesis by the Mueller matrix microscopy of circular dichroism

The aim of the work is to develop forensic criteria for differentiation of hemorrhages of traumatic and non-traumatic genesis by digital Mueller matrix (MM) polarization microscopy of histological sections of human brain substance (HBS).Material and methods. Native histological preparations of HBS from 32 corpses with ischemic stroke (1 group), 35 corpses with hemorrhages of traumatic origin (2 groups), 33 corpses with hemorrhages in HBS of non-traumatic genesis (3 group) and 30 corpses caused by acute coronary insufficiency (4 control group). The method of research is azimuthal-invariant MM microscopy of circular dichroism (CD) of histological sections of the brain.Results. The results of studies of the coordinate and statistical structure of maps of the size of values at the points of digital microscopic images of histological sections of HBS of the dead of all groups revealed a coordinate-inhomogeneous structure of all distributions of the size of the CD of microscopic images of histological sections of the dead of all groups. Probable distributions that characterize MM invariants CD maps of histological sections of brain samples from all groups have a small scatter of values of the statistical moment of the 2nd order, as well as significant values of statistical moments of the 3rd and 4th orders.Conclusion. The efficiency of using the Mueller-matrix mapping method in diagnosing of hemorrhage genesis in the brain substance in the differentiation of the set of samples of control and experimental groups reaches a satisfactory level - 77% - 78%.

On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario

Automating the analysis of digital microscopic images to identify the cell sub-types or the presence of illness has assumed a great importance since it aids the laborious manual process of review and diagnosis. In this paper, we have focused on the analysis of white blood cells. They are the body’s main defence against infections and diseases and, therefore, their reliable classification is very important. Current systems for leukocyte analysis are mainly dedicated to: counting, sub-types classification, disease detection or classification. Although these tasks seem very different, they share many steps in the analysis process, especially those dedicated to the detection of cells in blood smears. A very accurate detection step gives accurate results in the classification of white blood cells. Conversely, when detection is not accurate, it can adversely affect classification performance. However, it is very common in real-world applications that work on inaccurate or non-accurate regions. Many problems can affect detection results. They can be related to the quality of the blood smear images, e.g., colour and lighting conditions, absence of standards, or even density and presence of overlapping cells. To this end, we performed an in-depth investigation of the above scenario, simulating the regions produced by detection-based systems. We exploit various image descriptors combined with different classifiers, including CNNs, in order to evaluate which is the most suitable in such a scenario, when performing two different tasks: Classification of WBC subtypes and Leukaemia detection. Experimental results have shown that Convolutional Neural Networks are very robust in such a scenario, outperforming common machine learning techniques combined with hand-crafted descriptors. However, when exploiting appropriate images for model training, even simpler approaches can lead to accurate results in both tasks.

Construction of tissue-engineered skin with rete ridges using co-network hydrogels of gelatin methacrylated and poly(ethylene glycol) diacrylate

Tissue-engineered skin, as a promising skin substitute, can be used for in vitro skin research and skin repair. However, most of research on tissue-engineered skin tend to ignore the rete ridges (RRs) microstructure, which enhances the adhesion between dermis and epidermis and provides a growth environment for epidermal stem cells. Here, we prepared and characterized photocurable gelatin methacrylated (GelMA) and poly(ethylene glycol) diacrylate (PEGDA) co-network hydrogels with different concentrations. Using a UV curing 3D printer, resin molds were designed and fabricated to create three-dimensional micropatterns and replicated onto GelMA-PEGDA scaffolds. Human keratinocytes (HaCaTs) and human skin fibroblasts (HSFs) were co-cultured on the hydrogel scaffold to prepare tissue-engineered skin. The results showed that 10%GelMA-2%PEGDA hydrogel provides the sufficient mechanical properties and biocompatibility to prepare a human skin model with RRs microstructure, that is, it presents excellent structural support, suitable degradation rate, good bioactivity and is suitable for long-term culturing. Digital microscope image analyses showed the micropattern was well-transferred onto the scaffold surface. Both in vitro and in vivo experiments confirmed the formation of the epidermal layer with undulating microstructure. In wound healing experiments, hydrogel can significantly accelerate wound healing. This study provides a simple and powerful way to mimic the structures of human skin and can make a contribution to skin tissue engineering and wound healing.

Differential Polarization Imaging of Plant Cells. Mapping the Anisotropy of Cell Walls and Chloroplasts.

Modern light microscopy imaging techniques have substantially advanced our knowledge about the ultrastructure of plant cells and their organelles. Laser-scanning microscopy and digital light microscopy imaging techniques, in general—in addition to their high sensitivity, fast data acquisition, and great versatility of 2D–4D image analyses—also opened the technical possibilities to combine microscopy imaging with spectroscopic measurements. In this review, we focus our attention on differential polarization (DP) imaging techniques and on their applications on plant cell walls and chloroplasts, and show how these techniques provided unique and quantitative information on the anisotropic molecular organization of plant cell constituents: (i) We briefly describe how laser-scanning microscopes (LSMs) and the enhanced-resolution Re-scan Confocal Microscope (RCM of Confocal.nl Ltd. Amsterdam, Netherlands) can be equipped with DP attachments—making them capable of measuring different polarization spectroscopy parameters, parallel with the ‘conventional’ intensity imaging. (ii) We show examples of different faces of the strong anisotropic molecular organization of chloroplast thylakoid membranes. (iii) We illustrate the use of DP imaging of cell walls from a variety of wood samples and demonstrate the use of quantitative analysis. (iv) Finally, we outline the perspectives of further technical developments of micro-spectropolarimetry imaging and its use in plant cell studies.

MICROSTRUCTURE ANALYSIS OF BIO ENGINEERED CONCRETE UNDER THE CONCEPT OF SELF HEALING

The aspects of the durability of concrete that need significant attention in the current world demand the intense focus towards inventions in self-repairable materials. Bioengineered concrete using Bacillus sphaericus is one such novel trial. One needs to arrive at such an optimum combination of materials that involve the contribution of bacteria to resolve the issues of repair and rehabilitation effectively. The inherent properties of Bacillus sphaericus paved the way to arrive at the compatible combination of bacteria with nutrients in concrete to achieve self-repairable bioengineered concrete. Since the repairing agent used here performs at a micro level to precipitate stable calcite there is an indeed study carried out through SEM, XRD, EDAX, and Digital microscopic images to come out with an ideal solution for concrete repair work at the micro-level along with standard tests such as compressive strength. The results of these different tests on the bioengineered concrete using four Bacillus species of bacteria show that the best performance both in strength and durability is attained by bacteria Bacillus sphaericus with calcium lactate as a nutrient medium.

A multiscale investigation of deformation heterogeneity in additively manufactured 316L stainless steel

Additive manufacturing (AM) builds materials layer upon layer with the aid of an energy source. During AM processing, the complex thermal behaviors—directional heat flux, rapid solidification, and repeated thermal cycles—facilitate the formation of heterogeneous microstructures. Consequently, the mechanical behavior of AM parts can intrinsically differ from that of conventionally processed materials. This work focuses on characterizing deformation behavior in relation to AM-pertinent features and attempts to improve quantitative understanding of the process-structure-property relationships of metallic AM parts. A sample of as-deposited 316L stainless steel manufactured by the directed energy deposition technique is examined on different scales via microscope digital image correlation and electron backscatter diffraction. First, anisotropy in tensile properties is examined in the transverse plane perpendicular to the build direction. Then, it is revealed that the characteristic wavelength of strain bands formed due to deformation localization is strongly affected by tracks of deposits. Subsequently, strain localization at the grain scale is analyzed. The origin of plastic strains from the perspective of the activation of slip systems is examined, and the implications of the statistical distribution of local strain are discussed. Lastly, plastic strain accumulation and transmission in the vicinity of grain boundaries and substructure colonies are studied. The current observations and findings enrich the understanding of strain inhomogeneity in metallic AM parts and its relation to features at different length scales.

USING LIPASE ENZYME IN CLEANING OF ARCHAEOLOGICAL GLASS: AN EXPERIMENTAL APPLIED STUDY

The main objective of this research paper is to study the effect oflipase enzyme on the waxy materials mixed with the carbon particlesresulting from the burning of the candles, which were difficult toremove in one of the archaeological glass candle stands in the CopticMuseum, Cairo, Egypt. The paper is divided into an experimentalstudy for the use of the lipase enzyme in glass cleaning and anapplied study on cleaning an archaeological glass candle stand.The experimental samples were cleaned using lipase enzymefrom Burkholderia sp. X-Ray Fluorescence (XRF) was used toidentify the components of the archaeological glass candle standin the colorless and colored parts. X-Ray Diffraction (XRD) andRaman spectroscopy analysis were used to identify the damagedproducts in the study sample. Then, the stereo microscope and USBdigital microscope were employed to find out the efficacy of thelipase enzymes in the cleaning of experimental samples and theapplied study. The results proved that there was a high efficacy inthe dissolution of waxy fatty layers and the dissociation of carboncalcifications. USB digital microscopic images showed that lipaseenzymes from Burkholderia sp. gave good results in cleaning thesurface of the archaeological candle stand. Moreover, the enzymewas inhibited with the use of ethyl alcohol.

In-situ investigation of strain partitioning and microstructural strain path development up to and beyond necking

Multi-phase metallic materials exhibit significant levels of strain partitioning and localization when plastically deformed. Connecting these microstructural processes to macroscopic limits of uniform deformation, e.g., plastic instability and fracture, can reveal guidelines for damage-resistant microstructure design. This connection, however, is hindered due to the absence of strain partitioning and localization data from high strain levels, near and beyond necking. A parallel gap in our current understanding of the plasticity of multi-phase materials is regarding the spatial variations in strain path development at the micro-scale. Microscopic digital image correlation, the most powerful experimental method to provide such insights, is typically limited to low strain levels (< ~15%) due to crystal-plasticity- or damage-induced surface topography evolution. Here, we address these challenges by developing a practical method that relies on serial in-situ scanning electron microscope (SEM) mechanical tests of samples pre-strained to different levels. Applying this method to a dual-phase steel, we observe that ferrite and martensite on average exhibit a linear strain partitioning trend throughout the deformation. However, the highly-deformed ferrite regions (i.e., the top 4% most strained ferritic subsets) exhibit an exponential increase in the level of strain they accommodate, as well as sequential activation of the strain localization processes in different microscopic strain paths. Martensitic constituents play an important role in the strain localization processes and the resulting microscopic strain paths.

Optimization for drilling process of metal-composite aeronautical structures

Abstract Metal-composite laminates and joints are applied in aircraft manufacturing and maintenance (repairing) using aluminum alloys (AA) and glass fiber-reinforced polymer (GFRP). In these applications, drilling has a prominent place due to its vast application in aeronautical structures’ mechanical joints. Thus, this study presents the influence of uncoated carbide drills (85C, 86C, H10N), cutting speeds (v c = 20, 40, and 60 m min−1), and feed rates (f = 0.05, 0.15, and 0.25 mm rev−1) on delamination factor, thrust force ( F t {F}_{\text{t}} ), and burr formation in dry drilling metal-composite laminates and joints (AA2024/GFRP/AA2024). Experiments were performed, analyzed, and optimized using the Box–Behnken statistical design. Microscopic digital images for delamination evaluation, piezoelectric dynamometer for thrust force acquisition, and burr analysis were considered. The major finding was that the thrust force during drilling depends significantly on the feed rate. Another significant factor was the influence of the drill type (combined or not with feed rate). In fact, it was verified that the feed rate and the drill type were the most significant parameters on the delamination factor, while the feed rate was the most relevant on thrust force. The cutting speed did not affect significantly thrust force and delamination factor at exit ( F da S ) \hspace{.25em}({F}_{{\text{da}}_{\text{S}}}) . However, the combination f × v c was significant in delamination factor at entrance ( F da E ) \text{&amp;#x00A0;}({F}_{{\text{da}}_{\text{E}}}) . Based on the optimized input parameters, they presented lower values for delamination factors ( F da E = 1.18 {F}_{{\text{da}}_{\text{E}}}=1.18 and F da S = 1.33 {F}_{{\text{da}}_{\text{S}}}=\hspace{.25em}1.33 ) and thrust force ( F t = 67.3 N {F}_{\text{t}}=67.3\hspace{.5em}\text{N} ). These values were obtained by drilling the metal-composite laminates with 85C-tool, 0.05 mm rev−1 feed rate, and 20 m min−1 cutting speed. However, the burrs at the hole output of AA2024 were considered unsatisfactory for this specific condition, which implies additional investigation.

Performances of automated digital imaging of Gram-stained slides with on-screen reading against manual microscopy

The objective of this study was to evaluate the performances of the automated digital imaging of Gram-stained slides against manual microscopy. Four hundred forty-three identified Gram-stained slides were included in this study. When both methods agreed, we considered the results as correct, and no further examination was carried out. Whenever the methods gave discrepant results, we reviewed the digital images and the glass slides by manual microscopy to avoid incorrectly read smears. The final result was a consensus of multiple independent reader interpretations. Among the 443 slides analyzed in this study, 101 (22.8%) showed discrepant results between the compared methods. The rates of discrepant results according to the specimen types were 5.7% (9/157) for positive blood cultures, 42% (60/142) for respiratory tract specimens, and 22% (32/144) for sterile site specimens. After a subsequent review of the discrepant slides, the final rate of discrepancies dropped to 7.0% (31/443). The overall agreement between the compared methods and the culture results reached 78% (345/443) and 79% (349/443) for manual microscopy and automated digital imaging, respectively. According to culture results, the specificity for automated digital imaging and manual microscopy were 90.8% and 87.7% respectively. In contrast, sensitivity was 84.1% for the two compared methods. The discrepant results were mostly encountered with microorganism morphologies of rare occurrence. The results reported in this study emphasize that on-screen reading is challenging, since the recognition of morphologies on-screen can appear different as compared to routine manual microscopy. Monitoring of Gram stain errors, which is facilitated by automated digital imaging, remains crucial for the quality control of reported Gram stain results.

Phase shifted-lateral shearing digital holographic microscopy imaging for early diagnosis of cysts in soft tissue-mimicking phantom

The diagnosis of cyst structures that can be seen in almost every part of the body can be made using various medical imaging methods recently. Although the used methods and devices provide convenience in diagnosis and treatment, sometimes they can not detect early-stage cyst structures without showing any symptoms. In this study, the cystic structures with different size and formation in phantoms which are soft tissue-mimicking structures created without the need for any living tissue obtained with biopsy process are visualized by the proposed Phase Shifted-Lateral Shearing Digital Holographic Microscopy in three dimensional. The proposed method is verified with experimental results obtained by using various phantom models created at different times and structures. Experimental results illustrate the applicability of the proposed method for imaging of the cyst structures in the created soft tissue-mimicking phantoms even if they are in different structures, formations and stages (especially in early stage). In other respects, it enables to image the biological specimens in small size using simple and low-cost setup.