New approaches for low phototoxicity imaging of living cells and tissues

Fluorescence microscopy is a powerful tool used in scientific and medical research, but it is inextricably linked to phototoxicity. Neglecting phototoxicity can lead to erroneous or inconclusive results. Recently, several reports have addressed this issue, but it is still underestimated by many researchers, even though it can lead to cell death. Phototoxicity can be reduced by appropriate microscopic techniques and carefully designed experiments. This review focuses on recent strategies to reduce phototoxicity in microscopic imaging of living cells and tissues. We describe digital image processing and new hardware solutions. We point out new modifications of microscopy methods and hope that this review will interest microscopy hardware engineers. Our aim is to underscore the challenges and potential solutions integral to the design of microscopy systems. Simultaneously, we intend to engage biologists, offering insight into the latest technological advancements in imaging that can enhance their understanding and practice.

Quantitative and large-format histochemistry to characterize peripheral artery compositional gradients

The femoropopliteal artery (FPA) is a long, flexible vessel that travels down the anteromedial compartment of the thigh as the femoral artery and then behind the kneecap as the popliteal artery. This artery undergoes various degrees of flexion, extension, and torsion during normal walking movements. The FPA is also the most susceptible peripheral artery to atherosclerosis and is where peripheral artery disease manifests in 80% of cases. The connection between peripheral artery location, its mechanical flexion, and its physiological or pathological biochemistry has been investigated for decades; however, histochemical methods remain poorly leveraged in their ability to spatially correlate normal or abnormal extracellular matrix and cells with regions of mechanical flexion. This study generates new histological image processing pipelines to quantitate tissue composition across high-resolution FPA regions-of-interest or low-resolution whole-section cross-sections in relation to their anatomical locations and flexions during normal movement. Comparing healthy ovine femoral, popliteal, and cranial-tibial artery sections as a pilot, substantial arterial contortion was observed in the distal popliteal and cranial tibial regions of the FPA which correlated with increased vascular smooth muscle cells and decreased elastin content. These methods aim to aid in the quantitative characterization of the spatial distribution of extracellular matrix and cells in large heterogeneous tissue sections such as the FPA. RESEARCH HIGHLIGHTS: Large-format histology preserves artery architecture. Elastin and smooth muscle content is correlated with distance from heart and contortion during flexion. Cell and protein analyses are sensitive to sectioning plane and image magnification.

Morphological analysis and grain size distribution of SnO2 nanoparticles via digital image processing across diverse calcination temperatures

This study presents a comprehensive image analysis of the SnO2 nanoparticles synthesised through calcination at diverse temperatures, which enables an estimation of grain size distribution (GSD) from field-emission scanning electron microscopy (FE-SEM) images. Even though FE-SEM images could provide us with a lot of information about sample differences, we can learn more and perform a more accurate analysis of them by using quantitative data obtained by our image processing application. The digital image processing techniques used in this research provide a detailed analysis of the nanoparticles' size and shape, enabling a deeper understanding of their unique characteristics. The results reveal the significant impact of calcination temperature on the morphology of the nanoparticles, with changes in grain size and grain size distribution observed at varying temperatures.

A New Generative Model for Textual Descriptions of Medical Images Using Transformers Enhanced with Convolutional Neural Networks

The automatic generation of descriptions for medical images has sparked increasing interest in the healthcare field due to its potential to assist professionals in the interpretation and analysis of clinical exams. This study explores the development and evaluation of a generalist generative model for medical images. Gaps were identified in the literature, such as the lack of studies that explore the performance of specific models for medical description generation and the need for objective evaluation of the quality of generated descriptions. Additionally, there is a lack of model generalization to different image modalities and medical conditions. To address these issues, a methodological strategy was adopted, combining natural language processing and features extraction from medical images and feeding them into a generative model based on neural networks. The goal was to achieve model generalization across various image modalities and medical conditions. The results showed promising outcomes in the generation of descriptions, with an accuracy of 0.7628 and a BLEU-1 score of 0.5387. However, the quality of the generated descriptions may still be limited, exhibiting semantic errors or lacking relevant details. These limitations could be attributed to the availability and representativeness of the data, as well as the techniques used.

Three-Dimensional (3D) Visualization under Extremely Low Light Conditions Using Kalman Filter

In recent years, research on three-dimensional (3D) reconstruction under low illumination environment has been reported. Photon-counting integral imaging is one of the techniques for visualizing 3D images under low light conditions. However, conventional photon-counting integral imaging has the problem that results are random because Poisson random numbers are temporally and spatially independent. Therefore, in this paper, we apply a technique called Kalman filter to photon-counting integral imaging, which corrects data groups with errors, to improve the visual quality of results. The purpose of this paper is to reduce randomness and improve the accuracy of visualization for results by incorporating the Kalman filter into 3D reconstruction images under extremely low light conditions. Since the proposed method has better structure similarity (SSIM), peak signal-to-noise ratio (PSNR) and cross-correlation values than the conventional method, it can be said that the visualization of low illuminated images can be accurate. In addition, the proposed method is expected to accelerate the development of autonomous driving technology and security camera technology.

Automated contour extraction for light-sheet microscopy images of zebrafish embryos based on object edge detection algorithm

Embryo contour extraction is the initial step in the quantitative analysis of embryo morphology, and it is essential for understanding the developmental process. Recent developments in light-sheet microscopy have enabled the in toto time-lapse imaging of embryos, including zebrafish. However, embryo contour extraction from images generated via light-sheet microscopy is challenging owing to the large amount of data and the variable sizes, shapes, and textures of objects. In this report, we provide a workflow for extracting the contours of zebrafish blastula and gastrula without contour labeling of an embryo. This workflow is based on the edge detection method using a change point detection approach. We assessed the performance of the edge detection method and compared it with widely used edge detection and segmentation methods. The results showed that the edge detection accuracy of the proposed method was superior to those of the Sobel, Laplacian of Gaussian, adaptive threshold, Multi Otsu, and k-means clustering-based methods, and the noise robustness of the proposed method was superior to those of the Multi Otsu and k-means clustering-based methods. The proposed workflow was shown to be useful for automating small-scale contour extractions of zebrafish embryos that cannot be specifically labeled owing to constraints, such as the availability of microscopic channels. This workflow may offer an option for contour extraction when deep learning-based approaches or existing non-deep learning-based methods cannot be applied.

Experimental Study on the Physicochemical Properties of Asphalt Modified by Different Anti-Stripping Agents and Their Moisture Susceptibility with Aggregates

Erosion and the stripping effect of moisture on asphalt mixtures is one of the main reasons for the shortened service life of asphalt pavements. The common mean of preventing asphalt pavements from being damaged by moisture is adding anti-stripping agents (ASAs) to asphalt mixtures. However, the effect regularity and mechanism of anti-stripping agents on the physicochemical properties of asphalt is not exactly defined. This study compared the physical properties of ASA-modified asphalt (AMAs) to determine the optimal dosage and investigated the rheological and adhesion properties. Based on the roller bottle method and water immersion method, the moisture susceptibility of AMAs with three particle sizes was investigated. The results showed that the modification of asphalt using anti-stripping agents was a physical modification. At the optimum dosage of anti-stripping agents (0.3%), the basic physical properties of AMA1 were the most desirable. ASA2 increased the resistance of asphalt for deformation at high temperature by 46%, and AMA3 had the best low-temperature performance. ASAs enhanced the dispersed and polar components in the asphalt binder, improving the adhesion energy of asphalt. AMA3 had the strongest adhesion to the aggregate, with an increase in adhesion work by 2.8 times and a 45% of increase in ER value. This was attributed to ASA3 containing with a large number of metal cations and polar functional groups. It was shown that ASAs provided the most improvement in the anti-stripping performance of asphalt mixtures with 9.5-13.2 mm particles. The amide ASA, phosphate ASA and aliphatic amine ASA improved the water damage resistance of asphalt by 65%, 45% and 78%, respectively. This study can help engineers realize the effects of different types of ASAs on the physicochemical properties of asphalt and select the most suitable type of ASAs according to the service requirements.

Trapalyzer: a computer program for quantitative analyses in fluorescent live-imaging studies of neutrophil extracellular trap formation

Neutrophil extracellular traps (NETs), pathogen-ensnaring structures formed by neutrophils by expelling their DNA into the environment, are believed to play an important role in immunity and autoimmune diseases. In recent years, a growing attention has been put into developing software tools to quantify NETs in fluorescent microscopy images. However, current solutions require large, manually-prepared training data sets, are difficult to use for users without background in computer science, or have limited capabilities. To overcome these problems, we developed Trapalyzer, a computer program for automatic quantification of NETs. Trapalyzer analyzes fluorescent microscopy images of samples double-stained with a cell-permeable and a cell-impermeable dye, such as the popular combination of Hoechst 33342 and SYTOX™ Green. The program is designed with emphasis on software ergonomy and accompanied with step-by-step tutorials to make its use easy and intuitive. The installation and configuration of the software takes less than half an hour for an untrained user. In addition to NETs, Trapalyzer detects, classifies and counts neutrophils at different stages of NET formation, allowing for gaining a greater insight into this process. It is the first tool that makes this possible without large training data sets. At the same time, it attains a precision of classification on par with state-of-the-art machine learning algorithms. As an example application, we show how to use Trapalyzer to study NET release in a neutrophil-bacteria co-culture. Here, after configuration, Trapalyzer processed 121 images and detected and classified 16 000 ROIs in approximately three minutes on a personal computer. The software and usage tutorials are available at https://github.com/Czaki/Trapalyzer.

A Comparative Study of Structural Deformation Test Based on Edge Detection and Digital Image Correlation

Digital image-correlation (DIC) algorithms rely heavily on the accuracy of the initial values provided by whole-pixel search algorithms for structural displacement monitoring. When the measured displacement is too large or exceeds the search domain, the calculation time and memory consumption of the DIC algorithm will increase greatly, and even fail to obtain the correct result. The paper introduced two edge-detection algorithms, Canny and Zernike moments in digital image-processing (DIP) technology, to perform geometric fitting and sub-pixel positioning on the specific pattern target pasted on the measurement position, and to obtain the structural displacement according to the change of the target position before and after deformation. This paper compared the difference between edge detection and DIC in accuracy and calculation speed through numerical simulation, laboratory, and field tests. The study demonstrated that the structural displacement test based on edge detection is slightly inferior to the DIC algorithm in terms of accuracy and stability. As the search domain of the DIC algorithm becomes larger, its calculation speed decreases sharply, and is obviously slower than the Canny and Zernike moment algorithms.

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review

One of the most widely researched topics in the food industry is bread quality analysis. Different techniques have been developed to assess the quality characteristics of bakery products. However, in the last few decades, the advancement in sensor and computational technologies has increased the use of computer vision to analyze food quality (e.g., bakery products). Despite a large number of publications on the application of imaging methods in the bakery industry, comprehensive reviews detailing the use of conventional analytical techniques and imaging methods for the quality analysis of baked goods are limited. Therefore, this review aims to critically analyze the conventional methods and explore the potential of imaging techniques for the quality assessment of baked products. This review provides an in-depth assessment of the different conventional techniques used for the quality analysis of baked goods which include methods to record the physical characteristics of bread and analyze its quality, sensory-based methods, nutritional-based methods, and the use of dough rheological data for end-product quality prediction. Furthermore, an overview of the image processing stages is presented herein. We also discuss, comprehensively, the applications of imaging techniques for assessing the quality of bread and other baked goods. These applications include studying and predicting baked goods' quality characteristics (color, texture, size, and shape) and classifying them based on these features. The limitations of both conventional techniques (e.g., destructive, laborious, error-prone, and expensive) and imaging methods (e.g., illumination, humidity, and noise) and the future direction of the use of imaging methods for quality analysis of bakery products are discussed.

A fast and reliable method for semi-automated planimetric quantification of dental plaque in clinical trials

AIM

To develop a simple and reproducible method for semi-automated planimetric quantification of dental plaque.

MATERIALS AND METHODS

Plaque from 20 healthy volunteers was disclosed using erythrosine, and fluorescence images of the first incisors, first premolars, and first molars were recorded after 1, 7, and 14 days of de novo plaque formation. The planimetric plaque index (PPI) was determined using a semi-automated threshold-based image segmentation algorithm and compared with manually determined PPI and the Turesky modification of the Quigley-Hein plaque index (TM-QHPI). The decrease of tooth autofluorescence in plaque-covered areas was quantified as an index of plaque thickness (TI). Data were analysed by analysis of variance (ANOVA) and Pearson correlations.

RESULTS

The high contrast between teeth, disclosed plaque, and soft tissues in fluorescence images allowed for a fast threshold-based image segmentation. Semi-automated PPI is strongly correlated with manual planimetry (r = 0.92; p < .001) and TM-QHPI recordings (r = 0.88; p < .001), and may exhibit a higher discriminatory power than TM-QHPI due to its continuous scale. TI values corresponded to optically perceived plaque thickness, and no differences were observed over time (p > .05, ANOVA).

CONCLUSIONS

The proposed semi-automated planimetric analysis based on fluorescence images is a simple and efficient method for dental plaque quantification in multiple images with reduced human input.

Influence of healing time on the outcomes of alveolar ridge preservation using a collagenated bovine bone xenograft: A randomized clinical trial

AIM

To evaluate the healing outcomes in non-molar post-extraction sockets filled with deproteinized bovine bone mineral with collagen (DBBM-C) as a function of time.

MATERIALS AND METHODS

Patients in need of non-molar tooth extraction were randomly allocated into one of three groups according to the total healing time (A-3 months; B-6 months; C-9 months). The effect of alveolar ridge preservation (ARP) therapy via socket filling using DBBM-C and socket sealing with a porcine collagen matrix (CM) was assessed based on a panel of clinical, digital, histomorphometric, implant-related, and patient-reported outcomes.

RESULTS

A total of 42 patients completed the study (n = 14 in each group). Histomorphometric analysis of bone core biopsies obtained at the time of implant placement showed a continuous increase in the proportion of mineralized tissue with respect to non-mineralized tissue, and a decrease in the proportion of remaining xenograft material over time. All volumetric bone and soft tissue contour assessments revealed a dimensional reduction of the alveolar ridge overtime affecting mainly the facial aspect. Linear regression analyses indicated that baseline buccal bone thickness is a strong predictor of bone and soft tissue modelling. Ancillary bone augmentation at the time of implant placement was needed in 16.7% of the sites (A:2; B:1; C:4). Patient-reported discomfort and wound healing index scores progressively decreased over time and was similar across groups.

CONCLUSIONS

Healing time influences the proportion of tissue compartments in non-molar post-extraction sites filled with DBBM-C and sealed with a CM. A variable degree of alveolar ridge atrophy, affecting mainly the facial aspect, occurs even after performing ARP therapy. These changes are more pronounced in sites exhibiting thin facial bone (≤1 mm) at baseline (Clinicaltrials.gov NCT03659617).

Multi-Illumination Single-Holographic-Exposure Lensless Fresnel (MISHELF) Microscopy: Principles and Biomedical Applications

Lensless holographic microscopy (LHM) comes out as a promising label-free technique since it supplies high-quality imaging and adaptive magnification in a lens-free, compact and cost-effective way. Compact sizes and reduced prices of LHMs make them a perfect instrument for point-of-care diagnosis and increase their usability in limited-resource laboratories, remote areas, and poor countries. LHM can provide excellent intensity and phase imaging when the twin image is removed. In that sense, multi-illumination single-holographic-exposure lensless Fresnel (MISHELF) microscopy appears as a single-shot and phase-retrieved imaging technique employing multiple illumination/detection channels and a fast-iterative phase-retrieval algorithm. In this contribution, we review MISHELF microscopy through the description of the principles, the analysis of the performance, the presentation of the microscope prototypes and the inclusion of the main biomedical applications reported so far.

Time-Dependent Size and Shape Evolution of Gold and Europium Nanoparticles from a Bioproducing Microorganism, a Cyanobacterium: A Digitally Supported High-Resolution Image Analysis

Herein, the particle size distributions (PSDs) and shape analysis of in vivo bioproduced particles from aqueous Au³⁺ and Eu³⁺ solutions by the cyanobacterium Anabaena sp. are examined in detail at the nanoscale. Generally, biosynthesis is affected by numerous parameters. Therefore, it is challenging to find the key set points for generating tailored nanoparticles (NPs). PSDs and shape analysis of the Au and Eu-NPs were performed with ImageJ using high-resolution transmission electron microscopy (HR-TEM) images. As the HR-TEM image analysis reflects only a fraction of the detected NPs within the cells, additional PSDs of the complete cell were performed to determine the NP count and to evaluate the different accuracies. Furthermore, local PSDs were carried out at five randomly selected locations within a single cell to identify local hotspots or agglomerations. The PSDs show that particle size depends mainly on contact time, while the particle shape is hardly affected. The particles formed are distributed quite evenly within the cells. HR-PSDs for Au-NPs show an average equivalent circular diameter (ECD) of 8.4 nm (24 h) and 7.2 nm (51 h). In contrast, Eu-NPs preferably exhibit an average ECD of 10.6 nm (10 h) and 12.3 nm (244 h). Au-NPs are classified predominantly as "very round" with an average reciprocal aspect ratio (RAR) of ~0.9 and a Feret major axis ratio (FMR) of ~1.17. Eu-NPs mainly belong to the "rounded" class with a smaller RAR of ~0.6 and a FMR of ~1.3. These results show that an increase in contact time is not accompanied by an average particle growth for Au-NPs, but by a doubling of the particle number. Anabaena sp. is capable of biosorbing and bioreducing dissolved Au³⁺ and Eu³⁺ ions from aqueous solutions, generating nano-sized Au and Eu particles, respectively. Therefore, it is a low-cost, non-toxic and effective candidate for a rapid recovery of these sought-after metals via the bioproduction of NPs with defined sizes and shapes, providing a high potential for scale-up.

Evaluating the effectiveness of physical exercise in improving standardized testing performances through attention indices

INTRODUCTION

In the United States, standardized tests have risen in prevalence, extending their importance from education placement to employment. Attention is crucial to improving testing performance. Past studies have established that acute, coordinative, aerobic exercise improves attention, which is measured by the D2 Attention Test, emotional analysis, reading time, and eye movement tracking. No studies have drawn connections between physical exercise's quantifiable improvement in attention to improvements in standardized tests; therefore, this study would attempt to do so.

METHODS

This study defines attention to be positively related to reading speed and negatively related to the number of eye drifts. High school students were selected to read and answer two reading passages from an SAT (Scholastic Assessment Test) exam, before and after a short 80% intensity run. Their reading times, facial video, and test scores were recorded. Dlib plots the facial landmark and OpenCV tracks movement of the pupil.

RESULTS

Through paired-samples t-tests, this study found out that after exercise, subjects displayed increased reading speed and fewer eye drifts, coupled with increased mean scores.

CONCLUSION

Thus, this study demonstrated that running, as an acute, coordinative, aerobic exercise, helps increase the testing performance of the SAT reading section by measuring attention. Future research could focus on including head movement as an attention index, replicate the experiment on different standardized tests or exercises, and conduct natural experiments to better simulate real-life conditions to increase applicability.

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.

Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects

The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system.

Method for the Evaluation of the Homogeneity of Asphalt Mixtures by 2-Dimensional Image Analysis

In order to evaluate the homogeneity of asphalt mixture quantitatively, the distribution characteristic of internal phases of asphalt mixture were identified based on digital image processing technique and stereology theory, and the homogeneity coefficient (i.e., K) was proposed. At the same time, the trend of variation and reliability of homogeneity of asphalt mixture were analyzed by changing the nominal maximum aggregate size, aggregate gradation and asphalt content. The results suggest that the homogeneity of asphalt mixture could be comprehensively described using DIP technique combined with stereology theory. The smaller the K, the better the distribution homogeneity of the asphalt mixture. An improvement in the homogeneity of an asphalt mixture is achieved with the decrease of the nominal maximum aggregate size and a finer aggregate gradation. The asphalt content corresponding to the optimal homogeneity of the internal structure of asphalt mixture specimen is the optimum asphalt content. According to the experimental study, the suggested values of the homogeneity coefficient were given, which provides theoretical support to control the construction quality of the hot mixture asphalt.

Bridge Crack Inspection Efficiency of an Unmanned Aerial Vehicle System with a Laser Ranging Module

In this study, an unmanned aerial vehicle (UAV) with a camera and laser ranging module was developed to inspect bridge cracks. Four laser ranging units were installed adjacent to the camera to measure the distance from the camera to the object to calculate the object’s projection plane and overcome the limitation of vertical photography. The image processing method was adopted to extract crack information and calculate crack sizes. The developed UAV was used in outdoor bridge crack inspection tests; for images taken at a distance of 2.5 m, we measured the crack length, and the error between the result and the real length was less than 0.8%. The developed UAV has a dual-lens design, where one lens is used for bridge inspections and the other lens is used for flight control. The camera of the developed UAV can be rotated from the horizontal level to the zenith according to user requirements; thus, this UAV achieves high safety and efficiency in bridge inspections.

Aggregate Geometrical Features and Their Influence on the Surface Properties of Asphalt Pavement

Aggregate geometrical features directly affect asphalt pavement surface properties, which further affect the resistance to skidding of a road surface. In order to establish a relationship between the aggregate geometrical features and pavement surface properties, this paper employs an aggregate geometric characteristic evaluation system (AGCES) to describe the form property, angularity, and surface texture of aggregate particles. The geometrical feature parameters of 15 different aggregates were examined by AGCES and the corresponding surface properties of asphalt pavement prepared from the mentioned aggregates were evaluated by sand patch method, 2-Dimension Image-based Texture Analysis Method (2D-ITAM) and Walking Friction Tester (WFT), respectively. The relationships between the pavement surface property parameters and the aggregate geometric characteristic parameters studied were developed by the Levenberg-Marquarat and universal global optimization (LM-UGO). The results show that the calculated geometric characteristic parameters are in agreement with traditional manual measurement results. The pavement surface properties are significantly influenced by aggregate angularity and aggregate surface texture. Regression relationships were established to predict pavement surface properties from the aggregate geometrical features.

Selection of Filtering and Image Texture Analysis in the Radiographic Images Processing of Horses' Incisor Teeth Affected by the EOTRH Syndrome

Equine odontoclastic tooth resorption and hypercementosis (EOTRH) is one of the horses’ dental diseases, mainly affecting the incisor teeth. An increase in the incidence of aged horses and a painful progressive course of the disease create the need for improved early diagnosis. Besides clinical findings, EOTRH recognition is based on the typical radiographic findings, including levels of dental resorption and hypercementosis. This study aimed to introduce digital processing methods to equine dental radiographic images and identify texture features changing with disease progression. The radiographs of maxillary incisor teeth from 80 horses were obtained. Each incisor was annotated by separate masks and clinically classified as 0, 1, 2, or 3 EOTRH degrees. Images were filtered by Mean, Median, Normalize, Bilateral, Binomial, CurvatureFlow, LaplacianSharpening, DiscreteGaussian, and SmoothingRecursiveGaussian filters independently, and 93 features of image texture were extracted using First Order Statistics (FOS), Gray Level Co-occurrence Matrix (GLCM), Neighbouring Gray Tone Difference Matrix (NGTDM), Gray Level Dependence Matrix (GLDM), Gray Level Run Length Matrix (GLRLM), and Gray Level Size Zone Matrix (GLSZM) approaches. The most informative processing was selected. GLCM and GLRLM return the most favorable features for the quantitative evaluation of radiographic signs of the EOTRH syndrome, which may be supported by filtering by filters improving the edge delimitation.

System for the Recognizing of Pigmented Skin Lesions with Fusion and Analysis of Heterogeneous Data Based on a Multimodal Neural Network

Simple Summary

Skin cancer is one of the most common cancers in humans. This study aims to create a system for recognizing pigmented skin lesions by analyzing heterogeneous data based on a multimodal neural network. Fusing patient statistics and multidimensional visual data allows for finding additional links between dermoscopic images and medical diagnostic results, significantly improving neural network classification accuracy. The use by specialists of the proposed system of neural network recognition of pigmented skin lesions will enhance the efficiency of diagnosis compared to visual diagnostic methods.

Abstract

Today, skin cancer is one of the most common malignant neoplasms in the human body. Diagnosis of pigmented lesions is challenging even for experienced dermatologists due to the wide range of morphological manifestations. Artificial intelligence technologies are capable of equaling and even surpassing the capabilities of a dermatologist in terms of efficiency. The main problem of implementing intellectual analysis systems is low accuracy. One of the possible ways to increase this indicator is using stages of preliminary processing of visual data and the use of heterogeneous data. The article proposes a multimodal neural network system for identifying pigmented skin lesions with a preliminary identification, and removing hair from dermatoscopic images. The novelty of the proposed system lies in the joint use of the stage of preliminary cleaning of hair structures and a multimodal neural network system for the analysis of heterogeneous data. The accuracy of pigmented skin lesions recognition in 10 diagnostically significant categories in the proposed system was 83.6%. The use of the proposed system by dermatologists as an auxiliary diagnostic method will minimize the impact of the human factor, assist in making medical decisions, and expand the possibilities of early detection of skin cancer.

Review-Emerging Portable Technologies for Gait Analysis in Neurological Disorders

The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.

Design, Calibration, and Application of a Robust, Cost-Effective, and High-Resolution Lensless Holographic Microscope

Lensless holographic microscope (LHM) is an emerging very promising technology that provides high-quality imaging and analysis of biological samples without utilizing any lens for imaging. Due to its small size and reduced price, LHM can be a very useful tool for the point-of-care diagnosis of diseases, sperm assessment, or microfluidics, among others, not only employed in advanced laboratories but also in poor and/or remote areas. Recently, several LHMs have been reported in the literature. However, complete characterization of their optical parameters remains not much presented yet. Hence, we present a complete analysis of the performance of a compact, reduced cost, and high-resolution LHM. In particular, optical parameters such as lateral and axial resolutions, lateral magnification, and field of view are discussed into detail, comparing the experimental results with the expected theoretical values for different layout configurations. We use high-resolution amplitude and phase test targets and several microbeads to characterize the proposed microscope. This characterization is used to define a balanced and matched setup showing a good compromise between the involved parameters. Finally, such a microscope is utilized for visualization of static, as well as dynamic biosamples.

Application of Neural Networks for Differential Diagnosis of Pulmonary Pathologies Based on X-Ray Images

This work is devoted to studying the possibility of creating intelligent automated systems for differential diagnosis of pulmonary diseases based on the identification of pathological structures on X-ray images of the thoracic cavity organs (TCO) using neural network technologies. A brief analysis of modern diagnostic techniques is presented; a description of the proposed algorithm for determining the type of lung tissue pathologies used in the visual analysis of X-ray images and based on the identification of the main radiological syndromes, as well as on the evaluation of the quantitative characteristics of differential X-ray diagnostics is given. By the example of classification of radiographs of healthy and tuberculosis patients, the effectiveness of the use of neural network technologies in the computer diagnosis of lung diseases is demonstrated. The studies have been carried out using a publicly available database of X-ray images of thoracic cavity organs containing 3500 images of healthy people and 3500 images of sick people.

Does the geometry of scan bodies affect the alignment accuracy of computer-aided design in implant digital workflow: an in vitro study?

OBJECTIVES

To compare 2 implant scan bodies with different geometry on the accuracy of the virtual alignment process in the digital workflow.

MATERIALS AND METHODS

A master model of the edentulous maxilla with 6 implants and multi-unit abutments (MUA) inserted was fabricated. Six dome-shaped and cuboidal scan bodies were mounted on the MUAs respectively and consecutively scanned by a laboratory scanner 10 times. The original scans were imported to a dental-specific CAD software and virtually aligned with the default CAD model in the implant library. Thus, 10 aligned models were created. Both the original scans and the aligned models were evaluated by an inspection software for deviation of the scan body surfaces, the centroids of scan body and MUA, the scan body centre-axis and the inter-MUA distances/angles. The two-sample T-test/Mann Whitney U test were used to analyse the data with the level of significance set at 0.05.

RESULTS

The cuboidal group showed significant greater deviations of the model surface (13.9 µm vs 10.7 µm) and the MUA centroids (31.7µm vs 22.8 µm) but smaller deviation of the inter-MUA angle (0.047° vs 0.070°) than those of the dome-shaped group (P<0.05). No significant differences in the deviation of scan body centroids, centre-axis and the inter-MUA distances between the 2 groups were found.

CONCLUSIONS

Virtual alignment of implant scan body affected the accuracy of the digital workflow for complete-arch implant-supported prostheses (up to ~30 µm/0.09°). Different geometries of the implant scan body could also influence the transfer accuracy in the CAD process.

Advances in Thermal Image Analysis for the Detection of Pregnancy in Horses Using Infrared Thermography

Infrared thermography (IRT) was applied as a potentially useful tool in the detection of pregnancy in equids, especially native or wildlife. IRT measures heat emission from the body surface, which increases with the progression of pregnancy as blood flow and metabolic activity in the uterine and fetal tissues increase. Conventional IRT imaging is promising; however, with specific limitations considered, this study aimed to develop novel digital processing methods for thermal images of pregnant mares to detect pregnancy earlier with higher accuracy. In the current study, 40 mares were divided into non-pregnant and pregnant groups and imaged using IRT. Thermal images were transformed into four color models (RGB, YUV, YIQ, HSB) and 10 color components were separated. From each color component, features of image texture were obtained using Histogram Statistics and Grey-Level Run-Length Matrix algorithms. The most informative color/feature combinations were selected for further investigation, and the accuracy of pregnancy detection was calculated. The image texture features in the RGB and YIQ color models reflecting increased heterogeneity of image texture seem to be applicable as potential indicators of pregnancy. Their application in IRT-based pregnancy detection in mares allows for earlier recognition of pregnant mares with higher accuracy than the conventional IRT imaging technique.

Computer Vision in Analyzing the Propagation of a Gas-Gunpowder Jet

A method of mathematically processing the digital images of targets is developed. The theoretical and mathematical justification and the experimental validation of the possibility of estimating the amount of gunshot residue (GSR) and determining the GSR distribution over the target on the basis of its digital image is provided. The analysis of the optical density in selected concentric rings in the images reveals the radial dependence of soot distribution in the cross section of a gas-gunpowder jet. The analysis of the optical density in selected sectors of the circle reveals the angular dependence of the soot distribution in the gas-gunpowder jet cross section. It is shown that the integral optical density averaged over a selected area in the target image characterizes the mass of GSP deposited on it. It is possible to quantify the differences in the radial and angular distributions of the thickness of the GSR layer on various targets obtained both with the help of weapons of different types at the same distances and with the help of weapons of the same type at different distances, by calculating the distribution of optical density on their digital images.

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.

Thermochemical Laser-Induced Periodic Surface Structures Formation by Femtosecond Laser on Hf Thin Films in Air and Vacuum

Thermochemical laser-induced periodic surface structures (TLIPSS) are a relatively new type of periodic structures formed in the focal area of linear polarized laser radiation by the thermally stimulated reaction of oxidation. The high regularity of the structures and the possibility of forming high-ordered structures over a large area open up possibilities for the practical application for changing the optical and physical properties of materials surface. Since the mechanism of formation of these structures is based on a chemical oxidation reaction, an intriguing question involves the influence of air pressure on the quality of structure formation. This paper presents the results on the TLIPSS formation on a thin hafnium film with fs IR laser radiation at various ambient air pressures from 4 Torr to 760 Torr. Despite the decrease in the oxygen content in the ambient environment by two orders of magnitude, the formation of high-ordered TLIPSS (dispersion in the LIPSS orientation angle δθ < 5°) with a period of ≈700 nm occurs within a wide range of parameters variation (laser power, scanning speed). This behavior of TLIPSS formation is in agreement with experimental data obtained earlier on the study of the kinetics of high-temperature oxidation of hafnium at various oxygen pressures.

Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review

Chronic Low Back Pain (LBP) is a symptom that may be caused by several diseases, and it is currently the leading cause of disability worldwide. The increased amount of digital images in orthopaedics has led to the development of methods related to artificial intelligence, and to computer vision in particular, which aim to improve diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of computer vision in the diagnosis and treatment of LBP. A systematic research of PubMed electronic database was performed. The search strategy was set as the combinations of the following keywords: "Artificial Intelligence", "Feature Extraction", "Segmentation", "Computer Vision", "Machine Learning", "Deep Learning", "Neural Network", "Low Back Pain", "Lumbar". Results: The search returned a total of 558 articles. After careful evaluation of the abstracts, 358 were excluded, whereas 124 papers were excluded after full-text examination, taking the number of eligible articles to 76. The main applications of computer vision in LBP include feature extraction and segmentation, which are usually followed by further tasks. Most recent methods use deep learning models rather than digital image processing techniques. The best performing methods for segmentation of vertebrae, intervertebral discs, spinal canal and lumbar muscles achieve Sørensen-Dice scores greater than 90%, whereas studies focusing on localization and identification of structures collectively showed an accuracy greater than 80%. Future advances in artificial intelligence are expected to increase systems' autonomy and reliability, thus providing even more effective tools for the diagnosis and treatment of LBP.

Integration of imaging modalities in digital dental workflows - possibilities, limitations, and potential future developments

The digital workflow process follows different steps for all dental specialties. However, the main ingredient for the diagnosis, treatment planning and follow-up workflow recipes is the imaging chain. The steps in the imaging chain usually include all or at least some of the following modalities: cone-beam computed tomographic data acquisition, segmentation of the cone-beam computed tomography image, intraoral scanning, facial three-dimensional soft tissue capture and superimposition of all the images for the creation of a virtual augmented model. As a relevant clinical problem, the accumulation of error at each step of the chain might negatively influence the final outcome. For an efficient digital workflow, it is important to be aware of the existing challenges within the imaging chain. Furthermore, artificial intelligence-based strategies need to be integrated in the future to make the workflow more simplified, accurate and efficient.

The Influence of Mixing Conditions on the Macro-Scale Homogeneity of Asphalt Mixtures Blended with Reclaimed Asphalt Pavement (RAP)

The homogeneity of asphalt mixtures blended with reclaimed asphalt pavement (RAP) is affected by many factors. Due to the complicated compositions of recycled asphalt mixtures, the inhomogeneity issue might cause insufficient mechanical properties of asphalt mixtures, even though a design method was appropriately adopted. Therefore, it is of great significance to study the influence of mixing conditions on the homogeneity of asphalt mixtures blended with RAP materials. This study focused on the macro-scale homogeneity of produced asphalt mixtures. Specifically, asphalt mixtures incorporated with 40% RAP content were produced in a laboratory using different mixing times and mixing temperatures. A multi-direction indirect tensile stiffness modulus (ITSM) test was proposed to quantify the homogeneity of produced samples. In addition, the digital image processing (DIP) method was used to identify the distribution of aggregates and RAP binder. The results indicated that the influence of mixing time on the macro-homogeneity of asphalt mixtures indicated that a longer mixing time was favorable for the material dispersion. The influence of mixing temperature mainly rested on two perspectives. One was that the temperature variation induced the change of binder viscosity. The other was that the temperature influences the diffusion process between RAP binder and new bitumen, which further affected the mechanical performance of produced asphalt mixtures.

Using High-Frequency Information and RH to Estimate AQI Based on SVR

The Environmental Protection Administration of Taiwan’s Executive Yuan has set up many air quality monitoring stations to monitor air pollution in the environment. The current weather forecast also includes information used to predict air pollution. Since air quality indicators have a considerable impact on people, the development of a simple, fast, and low-cost method to measure the AQI value is a worthy topic of research. In this study, a method was proposed to estimate AQI. Visibility had a clear positive relationship with AQI. When images and AQI were compared, it was easy to see that visibility decreased with the AQI value increase. Distance is the main factor affecting visibility, so measuring visibility with images has also become a research topic. Images with high and low PM_2.5 concentrations were used to obtain regions of interest (RoI). The pixels in the RoI were calculated to obtain high-frequency information. The high-frequency information of RoI, RH, and true AQI was used for training via SVR, which was used to generate the model for AQI estimation. One year of experimental samples was collected for the experiment. Two indices were used to evaluate the performance of the proposed method. The results showed that the proposed method could be used to estimate AQI with acceptable performance in a simple, fast, and low-cost way.

A Novel Method of Vein Detection with the Use of Digital Image Correlation

Digital image correlation may be useful in many different fields of science, one of which is medicine. In this paper, the authors present the results of research aimed at detecting skin micro-shifts caused by pulsation of the veins. A novel technique using digital image correlation (DIC) and filtering the resulting shifts map to detect pulsating veins was proposed. After applying the proposed method, the veins in the forearm were visualized. The proposed technique may be used in the diagnosis of venous stenosis and may also contribute to reducing the number of adverse events during blood collection. The great advantage of the proposed method is the lack of the need to have specialized equipment, only a typical mobile phone camera is needed to perform the test.

A novel design proposal for low-cost vein-viewer for medical and non-contact biometric applications using NIR imaging

The vein-viewer is a new revolution in the health industry. In fact, it is one of the must-have gadgets for any medical professional. The vein-viewer is device that helps to access easily veins when trying to collect a blood sample or for administering Intravenous (IV) cannulation. It is also an aid for dermatologist/aesthetic physician to access client's veins for sclerotherapy procedures or avoiding veins in cosmetic procedures. The vein-viewer is highly applicable where vascular positioning is really difficult; examples while canulating infants, obese, hairy/dark skins, dialysis/cancer patients etc. In addition, frequent attempts affect patients, causing trauma and subcutaneous haemorrhage. As palm/finger vein patterns are unique and complex, difficult to duplicate or steal as it is beneath the skin. So, in this Covid19 pandemic time, the vein-viewer finds applications in the secure non-contact bio-metric authentications for secure banking and attendance registering system to identify an individual. In this article I am trying to explain the design overview of vein-viewer system, its design challenges, cost aspects, its availability and also sharing a few inputs for the new compact, low-cost design and implementation.

Diagnostic software proposal for bone scan follow-up using false color based on the gammagrams analysis from gray tone histograms

In this work we introduce a technique to speed up the interpretation of bone scans with the aim of determining the presence of absence of metastatic disease. We use gray tone histograms resembling the use of pass band filters, in order to ensure a reliable interpretation of the bone scan, providing an accurate diagnosis. We draw particular attention to three cases. The first case corresponds to shifted histograms. If the histogram is shifted toward the origin, the bone scan is free of metastasis. If it is shifted to the right and slightly broadened, indicates the presence of a bone scan anomaly different than metastasis. On the other hand, if the histogram is broadened and shifted to the left, is suggests the presence of metastatic disease. The second case corresponds to a histogram with noticeable fluctuations, indicating the presence of metastasis. Such fluctuations could become local maxima peaks indicating the advance of the metastasis. The third case, corresponds to the false color results displayed in terms of the gray tones observed in the histogram. Such false color is assigned from the construction of a 7-color palette selected in terms of the gray tones range, easing the ad hoc false color assignation for visualization purposes. The final diagnosis is carried out in terms of the color, geometry, extension and location of the region of interest in the images. Our proposed technique has the potential to be used in high-demand oncology centers due to its simplicity and diagnostic efficiency, confirmed and tested by specialists in the Centro Medico Siglo XXI (CDMX-Mexico).

Biotic Yield Losses in the Southern Amazon, Brazil: Making Use of Smartphone-Assisted Plant Disease Diagnosis Data

Pathogens and animal pests (P&A) are a major threat to global food security as they directly affect the quantity and quality of food. The Southern Amazon, Brazil's largest domestic region for soybean, maize and cotton production, is particularly vulnerable to the outbreak of P&A due to its (sub)tropical climate and intensive farming systems. However, little is known about the spatial distribution of P&A and the related yield losses. Machine learning approaches for the automated recognition of plant diseases can help to overcome this research gap. The main objectives of this study are to (1) evaluate the performance of Convolutional Neural Networks (ConvNets) in classifying P&A, (2) map the spatial distribution of P&A in the Southern Amazon, and (3) quantify perceived yield and economic losses for the main soybean and maize P&A. The objectives were addressed by making use of data collected with the smartphone application Plantix. The core of the app's functioning is the automated recognition of plant diseases via ConvNets. Data on expected yield losses were gathered through a short survey included in an "expert" version of the application, which was distributed among agronomists. Between 2016 and 2020, Plantix users collected approximately 78,000 georeferenced P&A images in the Southern Amazon. The study results indicate a high performance of the trained ConvNets in classifying 420 different crop-disease combinations. Spatial distribution maps and expert-based yield loss estimates indicate that maize rust, bacterial stalk rot and the fall armyworm are among the most severe maize P&A, whereas soybean is mainly affected by P&A like anthracnose, downy mildew, frogeye leaf spot, stink bugs and brown spot. Perceived soybean and maize yield losses amount to 12 and 16%, respectively, resulting in annual yield losses of approximately 3.75 million tonnes for each crop and economic losses of US$2 billion for both crops together. The high level of accuracy of the trained ConvNets, when paired with widespread use from following a citizen-science approach, results in a data source that will shed new light on yield loss estimates, e.g., for the analysis of yield gaps and the development of measures to minimise them.

X-ray Absorption (XRA): A New Technique for the Characterization of Granular Activated Carbons

The X-ray absorption (XRA) method using digital image processing techniques is a reliable technique to determine the exhaustion degree of granular activated carbons (GACs). Using an innovative digital image processing technique, the identification of individual adsorbed molecules or ions in a GAC was possible. Adsorption isotherm models (Langmuir and Freundlich) were used to simulate the adsorption equilibrium data of Methylene Blue (MB), nickel, cobalt and iodine. Freundlich equation was found to have the highest value of R² compared with Langmuir. The identification of distinctive patterns applying XRA for different adsorbed ions and molecules onto GAC was explored. It is demonstrated that unique XRA configurations for each adsorbed ion or molecule are found, as well as a proportional relationship between its incident energy (needed to achieve maximum photon attenuation) and the (effective) atomic number, the adsorbate mass and the molar or atomic mass of adsorbed molecule or ion. XRA method in combination with image histogram modifications was used to obtain a digital signature of adsorbed ions/molecules, giving distinct GSI values for each one in the used energy range. Probabilistic models prove that XRA results are within relationships between effective atomic number and photonic interaction probability, reinforcing the potentialities of XRA for monitoring (multi-)ion and/or molecule combinations on GAC using advanced digital image processing techniques. It was proved that the proposed approach could assess different adsorbed ions/molecules onto GACs in water purification systems.

Electrical Resistance Tomography for Control Applications: Quantitative Study of the Gas-Liquid Distribution inside A Cyclone

Phase separation based centrifugal forces is effective, and thus widely explored by the process industry. In an inline swirl separator, a core of the light phase is formed in the center of the device and captured further downstream. Given the inlet conditions, this gas core created varies in shape and size. To predict the separation behavior and control the process in an optimal way, the gas core diameter should be measured with the minimum possible intrusiveness. Process tomography techniques such as electrical resistance tomography (ERT) allows us to measure the gas core diameter in a fast and non-intrusive way. Due to the soft-field nature and ill-posed problem in solving the inverse problem, especially in the area of low spatial resolution, the reconstructed images often overestimate the diameter of the object under consideration leading to unreliable measurements. To use ERT measurements as an input for the controller, the estimated diameters should be corrected based on secondary measurements, e.g., optical techniques such as high-speed cameras. In this context, image processing and image analysis techniques were adapted to compare the diameter calculated by an ERT system and a fast camera. In this paper, a correction method is introduced to correct the diameter obtained by ERT based on static measurements. The proposed method reduced the ERT error of dynamic measurements of the gas core size from over 300% to below 20%, making it a reliable sensing technique for controlled separation processes.

Failure Process of Modeled Recycled Aggregate Concrete under Uniaxial Compression

This paper investigates the failure processes of recycled aggregate concrete by a model test and numerical simulations. A micromechanical numerical modeling approach to simulate the progressive cracking behavior of the modeled recycled aggregate concrete, considering its actual meso-structures, is established based on the discrete element method (DEM). The determination procedure of contact microparameters is analyzed, and a series of microscopic contact parameters for different components of modeled recycled aggregate concrete (MRAC) is calibrated using nanoindentation test results. The complete stress–strain curves, cracking process, and failure pattern of the numerical model are verified by the experimental results, proving their accuracy and validation. The initiation, growth, interaction, coalescence of microcracks, and subsequent macroscopic failure of the MRAC specimen are captured through DEM numerical simulations and compared with digital image correlation (DIC) results. The typical cracking modes controlled by meso-structures of MRAC are concluded according to numerical observations. A parameter study indicates the dominant influence of the macroscopic mechanical behaviors from the shear strength of the interfacial transition zones (ITZs).

Automatic fall detection using region-based convolutional neural network

The common classifiers usually used to detect fall incidents depend on building and maintaining complex feature extraction for accurate machine learning tasks. However, these efforts have not succeeded in determining an ideal classifier or feature extraction for fall detection. In this research, we address the feature extraction problem along with the choice of the most appropriate classifier by using deep learning where the most prominent features are learned over the numerous layers of the network. More specifically, a general framework that relies on a faster region-based convolutional neural network was designed and developped to recognize the fall incidents. In particular, we designed three custom architectures and exploited transfer learning by using pre-trained networks such as the VGG-16 and AlexNet to overcome the fall detection challenge. The performance of the proposed networks showed the advantage of the pre-trained networks, where VGG-16 scored highest in those measures followed by AlexNet, the custom networks showed impressive results that were close to the pre-trained networks.

WingMesh: A Matlab-Based Application for Finite Element Modeling of Insect Wings

The finite element (FE) method is one of the most widely used numerical techniques for the simulation of the mechanical behavior of engineering and biological objects. Although very efficient, the use of the FE method relies on the development of accurate models of the objects under consideration. The development of detailed FE models of often complex-shaped objects, however, can be a time-consuming and error-prone procedure in practice. Hence, many researchers aim to reach a compromise between the simplicity and accuracy of their developed models. In this study, we adapted Distmesh2D, a popular meshing tool, to develop a powerful application for the modeling of geometrically complex objects, such as insect wings. The use of the burning algorithm (BA) in digital image processing (DIP) enabled our method to automatically detect an arbitrary domain and its subdomains in a given image. This algorithm, in combination with the mesh generator Distmesh2D, was used to develop detailed FE models of both planar and out-of-plane (i.e., three-dimensionally corrugated) domains containing discontinuities and consisting of numerous subdomains. To easily implement the method, we developed an application using the Matlab App Designer. This application, called WingMesh, was particularly designed and applied for rapid numerical modeling of complicated insect wings but is also applicable for modeling purposes in the earth, engineering, mathematical, and physical sciences.

Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques

One of the most important operations during the manufacturing process of a pressure vessel is welding. The result of this operation has a great impact on the vessel integrity; thus, welding inspection procedures must detect defects that could lead to an accident. This paper introduces a computer vision system based on structured light for welding inspection of liquefied petroleum gas (LPG) pressure vessels by using combined digital image processing and deep learning techniques. The inspection procedure applied prior to the welding operation was based on a convolutional neural network (CNN), and it correctly detected the misalignment of the parts to be welded in 97.7% of the cases during the method testing. The post-welding inspection procedure was based on a laser triangulation method, and it estimated the weld bead height and width, with average relative errors of 2.7% and 3.4%, respectively, during the method testing. This post-welding inspection procedure allows us to detect geometrical nonconformities that compromise the weld bead integrity. By using this system, the quality index of the process was improved from 95.0% to 99.5% during practical validation in an industrial environment, demonstrating its robustness.

Imaging photoplethysmography and videocapillaroscopy enable noninvasive study of zebrafish cardiovascular system functioning

We report on the noninvasive method for in vivo study of fish's cardiovascular system, that is, the heart and the structure of vessels that carry blood throughout the body. The proposed approach is based on combined photoplethysmographic and videocapillaroscopic microscopic imaging and enables noncontact two-dimensional mapping of blood volume changes. We demonstrate that the obtained data allows precise measurements of heartbeat, blood flow velocity and other important parameters (see Videos S1 and S2). To validate the developed image processing technique, we have carried out multiple experiments on zebrafish-a well-proven informative model organism widely used to understand cardiac development. The proposed approach may be effective for the study of cardiovascular system formation and functioning as well as the impact of various influencing factors on them.

Proposal of the CAD System for Melanoma Detection Using Reconfigurable Computing

This work proposes dedicated hardware to real-time cancer detection using Field-Programmable Gate Arrays (FPGA). The presented hardware combines a Multilayer Perceptron (MLP) Artificial Neural Networks (ANN) with Digital Image Processing (DIP) techniques. The DIP techniques are used to extract the features from the analyzed skin, and the MLP classifies the lesion into melanoma or non-melanoma. The classification results are validated with an open-access database. Finally, analysis regarding execution time, hardware resources usage, and power consumption are performed. The results obtained through this analysis are then compared to an equivalent software implementation embedded in an ARM A9 microprocessor.

PM2.5 Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression

Fine aerosols with a diameter of less than 2.5 microns (PM_2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring the PM_2.5 concentration is needed. To relieve this problem, this paper attempts to provide an easy alternative approach to PM_2.5 concentration estimation. The proposed approach is based on image processing schemes and a simple linear regression model. It uses images with a high and low PM_2.5 concentration to obtain the difference between these images. The difference is applied to find the region with the greatest impact. The approach is described in two stages. First, a series of image processing schemes are employed to automatically select the region of interest (RoI) for PM_2.5 concentration estimation. Through the selected RoI, a single feature is obtained. Second, by employing the single feature, a simple linear regression model is used and applied to PM_2.5 concentration estimation. The proposed approach is verified by the real-world open data released by Taiwan’s government. The proposed scheme is not expected to replace component analysis using physical or chemical techniques. We have tried to provide a cheaper and easier way to conduct PM_2.5 estimation with an acceptable performance more efficiently. To achieve this, further work will be conducted and is summarized at the end of this paper.

Systematic Review and Study of S Curves for Biomass Quantification in Solid-state Fermentation (SSF) and Digital Image Processing (DIP) Applied to Biomass Measurement in Food Processes

BACKGROUND

There are several methods for the quantification of biomass in SSF, such as glucosamine measurement, ergosterol content, protein concentration, change in dry weight or evolution of CO2 production. However, all have drawbacks when obtaining accurate data on the progress of the SSF due to the dispersion in cell growth on the solid substrate, and the difficulty encountered in separating the biomass. Studying the disadvantages associated with the process of biomass quantification in SSF, the monitoring of the growth of biomass by a technique known as digital image processing (DIP), consists of obtaining information on the production of different compounds during fermentation, using colorimetric methods based on the pixels that are obtained from photographs.

OBJECTIVE

The purpose of this study was to know about the state of the technology and the advantages of DIP.

METHODS

The methodology employed four phases; the first describes the search equations for the SSF and the DIP. A search for patents related to SSF and DIP carried out in the Free Patents Online and Patent inspiration databases. Then there is the selection of the most relevant articles in each of the technologies. As a third step, modifications for obtaining the best adjustments were also carried out. Finally, the analysis of the results was done and the inflection years were determined by means of six mathematical models widely studied.

RESULTS

For these models, the inflection years were 2018 and 2019 for both the SSF and the DIP. Additionally, the main methods for the measurement of biomass in SSF were found, and are also indicated in the review, as DIP measurement processes have already been carried out using the same technology.

CONCLUSION

In addition, the DIP has shown satisfactory results and could be an interesting alternative for biomass measurement in SSF, due to its ease and versatility.

Novel and Automatic Rice Thickness Extraction Based on Photogrammetry Using Rice Edge Features

The dimensions of phenotyping parameters such as the thickness of rice play an important role in rice quality assessment and phenotyping research. The objective of this study was to propose an automatic method for extracting rice thickness. This method was based on the principle of binocular stereovision but avoiding the problem that it was difficult to directly match the corresponding points for 3D reconstruction due to the lack of texture of rice. Firstly, the shape features of edge, instead of texture, was used to match the corresponding points of the rice edge. Secondly, the height of the rice edge was obtained by way of space intersection. Finally, the thickness of rice was extracted based on the assumption that the average height of the edges of multiple rice is half of the thickness of rice. According to the results of the experiments on six kinds of rice or grain, errors of thickness extraction were no more than the upper limit of 0.1 mm specified in the national industry standard. The results proved that edge features could be used to extract rice thickness and validated the effectiveness of the thickness extraction algorithm we proposed, which provided technical support for the extraction of phenotyping parameters for crop researchers.

In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology

This study uses machine vision, feature extraction, and support vector machine (SVM) to compose a vibration monitoring system (VMS) for an in situ evaluation of the performance of industrial motors. The vision-based system respectively offers a spatial and temporal resolution of 1.4 µm and 16.6 ms after the image calibration and the benchmark of a laser displacement sensor (LDS). The embedded program of machine vision has used zero-mean normalized correlation (ZNCC) and peak finding (PF) for tracking the registered characteristics on the object surface. The calibrated VMS provides time–displacement curves related to both horizontal and vertical directions, promising remote inspections of selected points without attaching additional markers or sensors. The experimental setup of the VMS is cost-effective and uncomplicated, supporting universal combinations between the imaging system and computational devices. The procedures of the proposed scheme are (1) setting up a digital camera, (2) calibrating the imaging system, (3) retrieving the data of image streaming, (4) executing the ZNCC criteria, and providing the time–displacement results of selected points. The experiment setup of the proposed VMS is straightforward and can cooperate with surveillances in industrial environments. The embedded program upgrades the functionality of the camera system from the events monitoring to remote measurement without the additional cost of attaching sensors on motors or targets. Edge nodes equipped with the image-tracking program serve as the physical layer and upload the extracted features to a cloud server via the wireless sensor network (WSN). The VMS can provide customized services under the architecture of the cyber–physical system (CPS), and this research offers an early warning alarm of the mechanical system before unexpected downtime. Based on the smart sensing technology, the in situ diagnosis of industrial motors given from the VMS enables preventative maintenance and contributes to the precision measurement of intelligent automation.