Digital Image Research Articles

METHODOLOGY FOR THE APPLICATION OF THE DIGITAL IMAGE CORRELATION (DIC) FOR INVESTIGATING RC BEAMS

The article discusses the improvement of the digital image correlation (DIC) method for analyzing deformations in RC beams, specifically focusing on the importance of zero-strain verification. This step is critical for ensuring high measurement accuracy, as it helps identify and minimize systematic and random errors. Before starting the research, system calibration is conducted, which includes the assessment of background noise and stability that influence the results. The study shows that proper sample preparation, pattern creation, and control of external factors allow for obtaining reliable data. The application of DIC enables remote monitoring of cracks and evaluation of the stress-strain state of structures. It has been established that this method is useful not only for scientific experiments but also in practical engineering, contributing to the increased reliability of structures.

FPGA Accelerated Implementation of 3D Mesh Secret Sharing Based on Symmetric Similarity of Model

Secret sharing is particularly important in the field of information security, which allows for the reconstruction of secret information from secure shares. However, due to the large amount of data and non-integer data type of 3D (three-dimensional) models, it is less efficient to perform sharing compared to the 2D(two-dimensional) digital images. In order to enable efficient sharing of 3D models as well, this article designs different circuit modules to parallelize the process of sharing 3D models. The proposed structure exploits the vertex extension to perform a complete reconstruction of the 3D model. In the sharing phase, the symmetric similarity of 3D model is exploited to realize the sharing in parallel, which improves the efficiency of sharing. In the reconstruction phase, the Strassen algorithm is used to optimize matrix multiplication, which saves circuit resources. Simulation results show that the structure performs more than 100 times faster than software. The utilization of circuit resources and the size of the share are both optimized. The validity of the designed hardware architecture is confirmed after analyzing the data from experiments performed on several 3D models.

A comparison of artificial intelligence-enhanced electrocardiography approaches for prediction of time-to-mortality using electrocardiogram images

Abstract Background Most artificial intelligence-enhanced ECG (AI-ECG) models used to predict adverse events including death require that the ECGs be stored digitally. However, the majority of clinical facilities worldwide store ECGs as images. Methods 1,163,401 ECGs (189,539 patients) from a secondary care dataset were available as both natively digital traces and PDF images. A digitisation pipeline extracted signals from PDFs. Separate 1D convolutional neural network (CNN) models were trained on natively digital or digitised ECGs, with a discrete-time survival loss function to predict time-to-mortality. A 2D CNN model was trained on 310x868 pixel ECG images. External validation was performed in 958,954 ECGs (645,373 patients) from a Brazilian primary care cohort and 1022 ECGs (1022 patients) from a Chagas disease cohort. Results The image 2D CNN model and digitised 1D CNN model performed comparably to natively digital 1D CNN model in internal (C-index 0.780 (0.779-0.781), 0.772 (0.771-0.774) and 0.775 (0.774-0.776 respectively) and external validation. Models trained on natively digital 1D ECGs had comparable performance when applied to digitised 1D ECGs (C-index 0.773 (0.771-0.774). Conclusion Both the image 2D CNN and digitised 1D CNN enable mortality prediction from ECG images, with comparable performance to natively digital 1D CNN. Models trained on natively digital 1D ECGs can also be applied to digitised 1D ECGs, without any significant loss in performance. This works allows AI-ECG mortality prediction to be applied in diverse global settings lacking digital ECG infrastructure.

Using GeoMx DSP Spatial Proteomics to Investigate Immune Infiltration of NOD Mouse Islet and Exocrine Compartments

Abstract Purpose Type 1 Diabetes (T1D) pathogenesis involves immune cells infiltrating pancreatic Islets of Langerhans, leading to T cell activation, beta cell destruction, and impaired insulin production. However, infiltration has a heterogenic nature that isn’t described in detail, as not all islets are infiltrated. The aim of this study was to investigate if the observed heterogeneity is coupled to differences in immune and/or dysfunctional status of islets or exocrine cells, and if specific markers could elucidate mechanistic details of T1D pathogenesis. Procedures The GeoMx platform was used to spatially quantify protein levels in pancreatic islets and exocrine tissue in Non-Obese Diabetic (NOD) mice. The protein panel included 17 immune activity markers and nine dysfunction markers. Immunohistochemical (IHC) staining and digital image analysis was used to analyze select marker proteins. Results Use of the GeoMx platform to investigate T1D was shown to be possible, as Granzyme B protein levels were found to be lower in distal islet areas when compared to proximal areas. Smooth Muscle Actin protein levels were higher in exocrine areas proximal to immune-infiltrated islets, when compared to distally located exocrine areas. Findings from GeoMx were however not observed in IHC-stained sections. Conclusions This study demonstrates that investigating T1D is possible with spatial proteomics, as the assays revealed presence of heterogenic islet areas in NOD mice, which may play a role in T1D progression and escape from immune recognition. This study highlights the potential of spatial technologies for elucidating T1D pathogenesis and future treatment strategies.

Abstract B029: Mapping the vascular walls of colorectal cancer

Abstract Introduction: Properties of tumor angiogenesis impact on tumor growth and composition of the tumor microenvironment. Tumor vessels are composed of pericytes and other mural cells. Detailed characterization of the cellular composition of the microvasculature can suggest functional vessel and case properties relevant for outcome, response to treatment and immune surveillance. Results: Based on single cell RNA-seq from three human stage II/III colon cancers and pilot in-situ multi-antibody staining of six tumors two main clusters of perivascular cells were identified; B1 (MCAM+, MYH11-) and B2 (MCAM+, MYH11+) cells. B1 cells displayed pericyte features including expression of RGS5, whereas B2 cells showed high expression of smooth muscle cell markers like ACTG2 and Desmin. Desmin-high B2 cells were predominantly located in muscular layers, whereas Desmin-low/MYH11-high B2 cells showed perivascular enrichment. An extended novel analyses workflow of digital image analysis was used to profile peri-endothelial composition in 19 intervals; 11 1-µm intervals from 0 to 10 µm and 8 5-µm intervals from 10 to 50 µm from endothelial areas. Each of these expansion areas was quantitated regarding density of PDGFRB+/- cells including B1/B2 cells, and a MYH11+/MCAM- cells. Density of macrophage subsets (CD68-positive subsets defined by CD163 and CD11c) were also quantitated. In general, B1 cells showed stronger spatial association with endothelial cells than B2 cells, and this spatial association was stronger for the PDGFRB+ B1 subset. Survival analysis indicated that prognostic relevance was strongest when PDGFRB+ (favorable prognosis) cells were closest to endothelial cells. Regarding perivascular macrophage density, M0 and transit M1/M2 macrophages were predominantly enriched in the peri-endothelial regions, whereas density of M1 and M2 increased gradually as distance from vessels increased. M2 density was overall associated with poor prognosis, whereas high density of other macrophage subsets overall was associated with good prognosis. Ongoing studies are using a clustering-based approach to identify vessel subsets, and prognosis associations are being consolidated in additional cohorts. Summary: This study provides a novel approach for high resolution profiling of perivascular status in CRC, and possibly other tumor types, that could be of specific importance to identify tumor features associated with response to angiogenesis-directed therapies. Citation Format: Linglong( 凌 龙 ) Huang( 黄 ), Mercedes Herrera, Jonas Sjölund, Vladimir Chocoloff, Simon Joost, Rasul M. Tabiev, Lina Wik Leiss, Carina Strell, Luis Nunes, Artur Mezheyeuski, David Edler, Anna Martling, Fredrik Pontén, Bengt Glimelius, Tobias Sjöblom, Maria Kasper, Kristian Pietras, Arne Östman. Mapping the vascular walls of colorectal cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr B029.

Wallets' explorations across non-fungible token collections.

Non-fungible tokens (NFTs), which are immutable and transferable tokens on blockchain networks, have been used to certify the ownership of digital images often grouped in collections. Depending on individual interests, wallets explore and purchase NFTs in one or more image collections. Among many potential factors of shaping purchase trajectories, this paper specifically examines how visual similarities between collections affect wallets' explorations. Our model characterizes each wallet's explorations with a Lévy flight and shows that wallets tend to favor collections having similar visual features to their previous purchases while their behaviors vary widely. The model also predicts the extent to which the next collection is close to the most recent collection of purchases with respect to visual features. These results are expected to enhance and support recommendation systems for the NFT market.

Accuracy and reliability of webceph on posteroanterior cephalogram- A retrospective study

Lateral cephalogram plays a vital role in orthodontic diagnosis and treatment planning, from the advent of cephalometric radiography, whereas limited attention was given to posteroanterior cephalogram which evaluates skeletal discrepancies in transverse dimension. Thus, our study focuses on comparing the accuracy and reliability of fully automated AI driven software ‘WebCeph’, a semi-automated software ‘EasyCeph’ and manual tracing on posteroanterior cephalogram.A sample of 30 pretreatment posteroanterior cephalometric radiographic digital images were collected and analyzed using manual tracing, WebCeph and EasyCeph with 20 landmarks (16 linear and 2 angular measurements). For manual tracing, hard copies of digital images were obtained, whereas for EasyCeph and WebCeph, direct digital images used for analysis. ANOVA Test was done to compare the measurements of each parameter among WebCeph, EasyCeph and manual tracing. Bonferroni post hoc test was performed for individual comparison among 3 groups. The intraoperator reliability were evaluated by correlation coefficient test after 1 month by retracing 5 randomly selected cephalograms. On comparing three methods, 6 out of 18 parameters showed statistically significant differences. On individual comparison, EasyCeph shows significant differences with its counterparts, whereas WebCeph and manual tracing values shows no statistically significant difference. The intraclass correlation coefficient shows strong correlation for manual tracing (0.84 to 0.95) and EasyCeph (0.78 to 0.89)The automated cephalometric measurements from WebCeph are reasonably consistent, accurate and reliable when compared with Manual tracing and EasyCeph.

Using Compressed JPEG and JPEG2000 Medical Images in Deep Learning: A Review

Machine Learning (ML), particularly Deep Learning (DL), has become increasingly integral to medical imaging, significantly enhancing diagnostic processes and treatment planning. By leveraging extensive datasets and advanced algorithms, ML models can analyze medical images with exceptional precision. However, their effectiveness depends on large datasets, which require extended training times for accurate predictions. With the rapid increase in data volume due to advancements in medical imaging technology, managing the data has become increasingly challenging. Consequently, irreversible compression of medical images has become essential for efficiently handling the substantial volume of data. Extensive research has established recommended compression ratios tailored to specific anatomies and imaging modalities, and these guidelines have been widely endorsed by government bodies and professional organizations globally. This work investigates the effects of irreversible compression on DL models by reviewing the relevant literature. It is crucial to understand how DL models respond to image compression degradations, particularly those introduced by JPEG and JPEG2000—both of which are the only permissible irreversible compression techniques in the most commonly used medical image format—the Digital Imaging and Communications in Medicine (DICOM) standard. This study provides insights into how DL models react to such degradations, focusing on the loss of high-frequency content and its implications for diagnostic interpretation. The findings suggest that while existing studies offer valuable insights, future research should systematically explore varying compression levels based on modality and anatomy, and consider developing strategies for integrating compressed images into DL model training for medical image analysis.

A novel method to judge the safe position of C2 lamina screw with double holes.

To evaluate the feasibility of C2 laminar screw (C2LS) fixation assisted by double holes and to explore a novel method for judging its safe positioning. The Digital Imaging in Communications format data of 25 C2 vertebrae specimens were obtained by computed tomography thin-slice scanning, and the data were imported into Mimics software for three-dimensional reconstruction. The bone cortex was removed at the starting and terminal midpoints of the lamina as the observation holes. Points A, M, and P represented the three different entry points of the C2LS. Point M was designated as the central entry point, point A was 1mm from the extension surface of point M to the ventral side, and point P was 1mm from the extension surface of point M to the dorsal side. Further classified into groups A, M, and P based on the screw path correlation parameters at each entry point. The distance between holes 1 and 2 (D1 and D2) was measured at the maximum safe inclination, parallel, and maximum safe extroversion positions when the screw-insertion points of C2LS were at the center points A, M, and P, respectively. The data of D1 and D2 of the three groups (A, M, P) in different screw directions were analyzed and compared. Twenty-five specimens were measured in the parallel, maximum safe inclination, and maximum safe extroversion groups. In the parallel relationship, there were significant differences in the D1 and D2 data between the M and P groups. In the introversion relationship, there were significant differences in the D1 and D2 data between the M and P groups. In the extroversion relationship, there were significant differences in the D1 and D2 data among the A, M, and P groups, respectively. Use of the C2LS assisted by double holes was able to determine the safe position of the screw in operation. When D1 is greater than D2, the screw position can be considered relatively safe, with the intraoperative visual manifestation of a deep screw in hole 1 and a shallow screw in hole 2. If D1 is close to D2, it is necessary to ensure that the screws in both holes 1 and 2 are shallow, then the screw position is safe. If the screw in hole 1 is observed to be shallower than in hole 2 during surgery, regardless of the entry point position, there is a significant risk of ventral cortical perforation. Especially when the entry point is more anterior, even when inserting the screw parallel to the lamina, caution must be taken against the risk of ventral cortical perforation.

An introduction for non-experts on using X-ray micro computed tomography as a tool for pore scale digital subsurface characterisation of siliciclastic materials

This paper presents an overview of using X-ray micro computed tomography (μCT) as a valuable tool for micro scale investigation of siliciclastic materials. When processed using digital image analysis (DIA), valuable quantitative data can be extracted from μCT 3D images. Subsurface reservoirs are of great importance to society as fluid-bearing formations, but also as storage reservoirs for carbon dioxide. μCT imaging has the capability to perform preliminary, highly detailed investigations of potential reservoirs. This approach has a range of benefits when compared to traditional 2D techniques, such as optical and scanning electron microscopy (SEM). Key advantages include the technique being non-destructive and capable of 3D and 4D visualisation. This facilitates rapid repeated digital measurements and experiments on microstructures. Digital samples can also be readily shared within the scientific community to replicate results and quickly launch new investigations. However, limitations still exist, posing challenges to the wider application of such a methodology. Such limitations include the identification of a representative elementary volume (REV), computational cost, and suitable processing of the output image data. Here, we highlight the value of using μCT and DIA, from our first experiences, to facilitate pore scale siliciclastic reservoir characterisation, but also highlight our perceived limitations and barriers to its much wider application. This paper introduces the key processing stages, opportunities and limitations of these techniques.

A New Breast Cancer Discovery Strategy: A Combined Outlier Rejection Technique and an Ensemble Classification Method

Annually, many people worldwide lose their lives due to breast cancer, making it one of the most prevalent cancers in the world. Since the disease is becoming more common, early detection of breast cancer is essential to avoiding serious complications and possibly death as well. This research provides a novel Breast Cancer Discovery (BCD) strategy to aid patients by providing prompt and sensitive detection of breast cancer. The two primary steps that form the BCD are the Breast Cancer Discovery Step (BCDS) and the Pre-processing Step (P2S). In the P2S, the needed data are filtered from any non-informative data using three primary operations: data normalization, feature selection, and outlier rejection. Only then does the diagnostic model in the BCDS for precise diagnosis begin to be trained. The primary contribution of this research is the novel outlier rejection technique known as the Combined Outlier Rejection Technique (CORT). CORT is divided into two primary phases: (i) the Quick Rejection Phase (QRP), which is a quick phase utilizing a statistical method, and (ii) the Accurate Rejection Phase (ARP), which is a precise phase using an optimization method. Outliers are rapidly eliminated during the QRP using the standard deviation, and the remaining outliers are thoroughly eliminated during ARP via Binary Harris Hawk Optimization (BHHO). The P2S in the BCD strategy indicates that data normalization is a pre-processing approach used to find numeric values in the datasets that fall into a predetermined range. Information Gain (IG) is then used to choose the optimal subset of features, and CORT is used to reject incorrect training data. Furthermore, based on the filtered data from the P2S, an Ensemble Classification Method (ECM) is utilized in the BCDS to identify breast cancer patients. This method consists of three classifiers: Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The Wisconsin Breast Cancer Database (WBCD) dataset, which contains digital images of fine-needle aspiration samples collected from patients’ breast masses, is used herein to compare the BCD strategy against several contemporary strategies. According to the outcomes of the experiment, the suggested method is very competitive. It achieves 0.987 accuracy, 0.013 error, 0.98 recall, 0.984 precision, and a run time of 3 s, outperforming all other methods from the literature.

Deep Learning using Spatial Distances, Normalized Coordinates, Scaling and Visual Words for Large Datasets

Image Retrieval has become popular and crucial task and the number of digital images on (web servers) amplified, it became increasingly very difficult to classify and track images. Many methods have been used to make image exploration effective and reliable, such as search based on the file name, image tagging, etc. but none have proved a good idea to work in real scenario. Our proposed methodology applies deep learning using spatial distances, normalized coordinates, scaling and visual words for large data sets for retrieval of images with highest accuracy. The proposed methodology has three basic steps: the first is Content Analysis. The image is passed through coarser intervolving phase, second is CNN, third is RGB color evaluation, fourth is Retrieved feature vectors and fifth is results derivation. Proposed methodology was applied on the three famous datasets namely, Cifar-100, FTVL and Fashion. Experiments conducted on these datasets have shown outstanding results.

Mechanical properties and crack deflection mechanisms in 3D-Printed porous geopolymers with cellular structures

ABSTRACT This study focuses on using helical design patterns via 3D printing to create geopolymer with a highly porous structure in order to enhance their strength-density relationship and fracture properties. In this regard, to create porous structure, different pitch angles and infill densities were chosen, and mechanical and fracture properties were examined. The results of mechanical strength tests revealed that while the pitch angle does not significantly affect compressive strength, flexural strength is improved by implementing a low pitch angle helical structure, which leads to the strength-density relationship improvement. Additionally, the work of fracture results demonstrated an enhancement for samples with low pitch angles, such as α15° and α30°, compared to cast and non-directional printed samples. The digital image correlation and fracture surface analysis showed several fracture mechanisms, predominantly crack deflection and twisting, in samples with low pitch angles, which contributes to the observed improvements in the work of fracture.

Study on mechanical properties and acoustic emission characteristics of composite rock mass with different thicknesses of weak interlayer.

To study the influence of the thickness of a weak interlayer on the deformation and failure characteristics of composite rock mass. Based on the measured data of sand-mudstone interbedded floor rock mass in the Yima mining area, Henan Province, China. Using quartz sand, gypsum, and cement as similar materials make composite rock-like specimens with weak interlayers. The mechanical properties, deformation evolution process, failure characteristics, and acoustic emission laws of rock samples with different interlayer thicknesses under uniaxial compression were studied using the digital image correlation method and acoustic emission monitoring technology. The results show that: (1) With an increase in the thickness of the weak interlayer, there is a corresponding decrease in the compressive strength of the specimen. When the thickness of the weak interlayer exceeds 5cm, the compressive strength of the composite rock specimen is even less than that of the single weak rock-like. (2) Under uniaxial compression conditions, the strain concentration zone first appears in the weak interlayer part of the composite rock-like specimen. As the pressure continues to increase, the specimen is the first to fail at the position of the maximum strain concentration zone. (3) The thickness of the weak interlayer is an important factor affecting the failure mode of composite rock-like specimens. With the increase of the thickness of the weak interlayer, the composite rock-like specimens change from overall failure to local failure. (4) With the increase of the thickness of the weak interlayer in the middle of the composite rock specimen, the maximum value of the energy released by the single acoustic emission event and the maximum value of the cumulative acoustic emission energy decrease during the compression failure process. The research results can provide a reference for the manufacture of composite rock mass with a weak interlayer and the deformation and failure analysis of composite rock mass.

Mesoscopic Tensile Deformation of Steel Cord with Diverse Layered Nonuniformity Based on Microcomputed Tomography Optimizing Simulation Model

The finite‐element analysis plays a crucial role in enhancing the quality and predicting the safety of steel cord. The conventional parametric model for finite‐element analysis often overlooks the inhomogeneity of steel cord, making it difficult to achieve precise simulation. This article utilizes microcomputed tomography to track the mesoscopic deformation of real steel cord during stretching, reconstructing 3D digital images and quantitatively analyzing the corresponding rule for an optimized finite‐element modeling method. In comparison to the parametric model's experimental deviation of 28.37% in strain, the actual structural tensile model aligns with the tensile test with a deviation of only 2.88%, indicating high processing quality. In addition, when simulating the impact of layered nonuniformity on the fracture strain of cords, it is observed that a small degree of nonuniformity leads to an increase in compression between the cord filaments and subsequently enhances the fracture strain. This study provides a practical and dependable quantitative analysis on a mesoscopic scale to optimize the design and production of cords.

Damage evaluation in plain-woven CFRP plate with circular hole under cyclic tensile loading using electrical impedance tomography

Understanding and predicting damage progression within thin plain-woven carbon fiber-reinforced polymer (CFRP) components is an essential issue for scheduling maintenance intervals and structural inspections and typically requires extensive test series. However, uncertainties always remain and result in conservative design and unnecessary downtimes due to the repeated inspections of a healthy structure. Structural health monitoring (SHM) can be used to reduce these uncertainties, as it allows one to evaluate the condition of a mechanical structure during operation. Numerous SHM methods have been developed, which achieve different levels of damage identification but are often investigated at small coupons with nonrealistic structural damages and loading conditions on both structure and sensor. The present research investigates electrical impedance tomography (EIT) featuring an elastoresistive thin-film sensor to evaluate damage that propagates from a circular hole in a large plate under cyclic tensile–tensile multiblock loading. Applied fatigue loads were increased multiple times up to a total number of two million cycles. During the loading, damage initiated at the hole and continuously propagated into the plate. Repeated EIT evaluations of the applied sensor and validating evaluations by means of digital image correlation clearly revealed the robustness of the hardware and the potential of the SHM method for damage evaluation of plain-woven CFRP components.

See, Touch, Feel: Theorizing Twitter/X Images for Diplomacy

Social media images are central to the practice of digital diplomacy. Some of the most contentious political engagements involve digital images designed to challenge or reaffirm the national identity of key political players. Yet the role of touch in conditioning how social media images are seen is curiously absent from International Relations analyses of the visual politics of digital diplomacy. We know far too little about this phenomenon. This article seeks to contribute to the study of images in global politics by developing a tripartite theorization of how the visual, tactile, and emotional intersect to mediate how Twitter images are seen within the practice of digital diplomacy. The article pays attention to the tactile experience of using Twitter, bridging the gap between seeing social media images as visual objects, and the affective resonance these images have as a marker of political power. I argue that touch acts as a conduit between the embodied, material, and visual to produce an affective encounter that contributes to how users feel about Twitter images. The intertextuality of the visual, tactile, and emotional dynamics of Twitter images are important elements of visual social media practices sustaining the strategic representation of identity. This framework allows me to build a theoretically grounded skilful speculation as to how digital diplomacy crisis events such as the China-Australia Twitter spat unfold and resonate beyond the digital.

Enhanced Wavelet Scattering Network for Image Inpainting Detection

The rapid advancement of image inpainting tools, especially those aimed at removing artifacts, has made digital image manipulation alarmingly accessible. This paper proposes several innovative ideas for detecting inpainting forgeries based on a low-level noise analysis by combining Dual-Tree Complex Wavelet Transform (DT-CWT) for feature extraction with convolutional neural networks (CNN) for forged area detection and localization, and lastly by employing an innovative combination of texture segmentation with noise variance estimations. The DT-CWT offers significant advantages due to its shift-invariance, enhancing its robustness against subtle manipulations during the inpainting process. Furthermore, its directional selectivity allows for the detection of subtle artifacts introduced by inpainting within specific frequency bands and orientations. Various neural network architectures were evaluated and proposed. Lastly, we propose a fusion detection module that combines texture analysis with noise variance estimation to give the forged area. Also, to address the limitations of existing inpainting datasets, particularly their lack of clear separation between inpainted regions and removed objects—which can inadvertently favor detection—we introduced a new dataset named the Real Inpainting Detection Dataset. Our approach was benchmarked against state-of-the-art methods and demonstrated superior performance over all cited alternatives.

HoloLume: Point-of-Application Holographic Imaging Solution

Abstract Digital holographic imaging has emerged as a label-free, non-invasive, and powerful tool, offering a transformative method for the real-time investigation of morphology, density, and other physical and mechanical features of biological and non-biological samples. The rapid development of these portable, economical imaging systems for clinical/non-clinical setups, especially in remote areas, is of great interest. These systems are early point-of-care applications in diagnosis, research, education, and training. This article reports a general-purpose, fully automated portable imaging tool, "HoloLume," that offers high-resolution 3D imaging that facilitates the comprehensive understanding and analysis of different biological/non-biological samples. The proposed microscopic system HoloLume realizes the interferogram generation of samples using common-path optics configuration with Fresnel biprism, and its motorized stage precisely scans the whole sample with a step of 3.87 μm for quick identification of the region of interest, which makes sure a time-efficient and robust analysis. The prototype, assembled from off-the-shelf components, costs approximately USD 1700 and weighs around 3 kg. To validate and verify the performance of the HoloLume microscope, we have conducted experiments on both non-biological and biological samples. Thus, its non-invasive nature, high temporal stability, and economical lightweight prototyping make it an ideal choice for hands-on learning and quantitative analysis, reducing the need for time-consuming sample preparation and conventionally expensive microscopic systems. Furthermore, the adaptability of the proposed imaging tool to remote environments ensures wider accessibility and its easy integration with other imaging modalities enables the user to gain insight into advanced research and diagnostics.

Non-destructive measurement of chlorophyll content in Sonchus oleraceus L. (Asteraceae) under variations in cultivation and planting season using digital image analysis

Non-destructive measurement of chlorophyll content in Sonchus oleraceus L. (Asteraceae) under variations in cultivation and planting season using digital image analysis