Optimized Electrophoretic Deposition of Chitosan/Mesoporous Glass Nanoparticles with Gentamicin on Titanium Implants: Enhancing Hemocompatibility and Antibacterial Activity.

Titanium-based implants have long been studied and used for applications in bone tissue engineering, thanks to their outstanding mechanical properties and appropriate biocompatibility. However, many implants struggle with osseointegration and attachment and can be vulnerable to the development of infections. In this work, we have developed a composite coating via electrophoretic deposition, which is both bioactive and antibacterial. Mesoporous bioactive glass particles with gentamicin were electrophoretically deposited onto a titanium substrate. In order to validate the hypothesis that the quantity of particles in the coatings is sufficiently high and uniform in each deposition process, an easy-to-use image processing algorithm was designed to minimize human dependence and ensure reproducible results. The addition of loaded mesoporous particles did not affect the good adhesion of the coating to the substrate although roughness was clearly enhanced. After 7 days of immersion, the composite coatings were almost dissolved and released, but phosphate-related compounds started to nucleate at the surface. With a simple and low-cost technique like electrophoretic deposition, and optimized stir and suspension times, we were able to synthesize a hemocompatible coating that significantly improves the antibacterial activity when compared to the bare substrate for both Gram-positive and Gram-negative bacteria.

Soft Contact Lens Engraving Characterization by Wavefront Holoscopy.

Permanent engravings on contact lenses provide information about the manufacturing process and lens positioning when they are placed on the eye. The inspection of their morphological characteristics is important, since they can affect the user's comfort and deposit adhesion. Therefore, an inverted wavefront holoscope (a lensless microscope based on Gabor's principle of in-line digital holography) is explored for the characterization of the permanent marks of soft contact lenses. The device, based on an in-line transmission configuration, uses a partially coherent laser source to illuminate the soft contact lens placed in a cuvette filled with a saline solution for lens preservation. Holograms were recorded on a digital sensor and reconstructed by back propagation to the image plane based on the angular spectrum method. In addition, a phase-retrieval algorithm was used to enhance the quality of the recovered images. The instrument was experimentally validated through a calibration process in terms of spatial resolution and thickness estimation, showing values that perfectly agree with those that were theoretically expected. Finally, phase maps of different engravings for three commercial soft contact lenses were successfully reconstructed, validating the inverted wavefront holoscope as a potential instrument for the characterization of the permanent marks of soft contact lenses. To improve the final image quality of reconstructions, the geometry of lenses should be considered to avoid induced aberration effects.

A Pilot Clinical and Technical Validation of an Immersive Virtual Reality Platform for 3D Anatomical Modeling and Contouring in Support of Surgical and Radiation Oncology Treatment Planning.

The aim of this study was to validate a novel medical virtual reality (VR) platform used for medical image segmentation and contouring in radiation oncology and 3D anatomical modeling and simulation for planning medical interventions, including surgery. The first step of the validation was to verify quantitatively and qualitatively that the VR platform can produce substantially equivalent 3D anatomical models, image contours, and measurements to those generated with existing commercial platforms. To achieve this, a total of eight image sets and 18 structures were segmented using both VR and reference commercial platforms. The image sets were chosen to cover a broad range of scanner manufacturers, modalities, and voxel dimensions. The second step consisted of evaluating whether the VR platform could provide efficiency improvements for target delineation in radiation oncology planning. To assess this, the image sets for five pediatric patients with resected standard-risk medulloblastoma were used to contour target volumes in support of treatment planning of craniospinal irradiation, requiring complete inclusion of the entire cerebral-spinal volume. Structures generated in the VR and the commercial platforms were found to have a high degree of similarity, with dice similarity coefficient ranging from 0.963 to 0.985 for high-resolution images and 0.920 to 0.990 for lower resolution images. Volume, cross-sectional area, and length measurements were also found to be in agreement with reference values derived from a commercial system, with length measurements having a maximum difference of 0.22 mm, angle measurements having a maximum difference of 0.04°, and cross-sectional area measurements having a maximum difference of 0.16 mm2. The VR platform was also found to yield significant efficiency improvements, reducing the time required to delineate complex cranial and spinal target volumes by an average of 50% or 29 min.

An improved corner dealiasing and recognition algorithm for 2D Wadell roundness computation.

This paper optimizes the 2D Wadell roundness calculation of particles based on digital image processing methods. An algorithm for grouping corner key points is proposed to distinguish each independent corner. Additionally, the cyclic midpoint filtering method is introduced for corner dealiasing, aiming to mitigate aliasing issues effectively. The relationships between the number of corner pixels (m), the central angle of the corner (α) and the parameter of the dealiasing degree (n) are established. The Krumbein chart and a sandstone thin section image were used as examples to calculate the 2D Wadell roundness. A set of regular shapes is calculated, and the error of this method is discussed. When α ≥ 30°, the maximum error of Wadell roundness for regular shapes is 5.21%; when 12° ≤ α < 30°, the maximum error increases. By applying interpolation to increase the corner pixels to the minimum number (m0) within the allowable range of error, based on the α-m0 relational expression obtained in this study, the error of the corner circle can be minimized. The results indicate that as the value of m increases, the optimal range interval for n also widens. Additionally, a higher value of α leads to a lower dependence on m. The study's results can be applied to dealiasing and shape analysis of complex closed contours.

HeLa cell segmentation using digital image processing.

Computational cell segmentation is a vital area of research, particularly in the analysis of images of cancer cells. The use of cell lines, such as the widely utilized HeLa cell line, is crucial for studying cancer. While deep learning algorithms have been commonly employed for cell segmentation, their resource and data requirements can be impractical for many laboratories. In contrast, image processing algorithms provide a promising alternative due to their effectiveness and minimal resource demands. This article presents the development of an algorithm utilizing digital image processing to segment the nucleus and shape of HeLa cells. The research aims to segment the cell shape in the image center and accurately identify the nucleus. The study uses and processes 300 images obtained from Serial Block-Face Scanning Electron Microscopy (SBF-SEM). For cell segmentation, the morphological operation of erosion was used to separate the cells, and through distance calculation, the cell located at the center of the image was selected. Subsequently, the eroded shape was employed to restore the original cell shape. The nucleus segmentation uses parameters such as distances and sizes, along with the implementation of verification stages to ensure accurate detection. The accuracy of the algorithm is demonstrated by comparing it with another algorithm meeting the same conditions, using four segmentation similarity metrics. The evaluation results rank the proposed algorithm as the superior choice, highlighting significant outcomes. The algorithm developed represents a crucial initial step towards more accurate disease analysis. In addition, it enables the measurement of shapes and the identification of morphological alterations, damages, and changes in organelles within the cell, which can be vital for diagnostic purposes.

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.

Low-Temperature Crack Resistance of High-Content Rubber-Powder-Modified Asphalt Mixture under Freeze-Thaw Cycles.

In order to study the modification mechanisms of a warm-mixing agent and high dosage on rubber-powder-modified asphalt, as well as the influence of salt freeze-thaw cycling on the mechanism of warm-mixed high-dosage-rubber-powder-modified asphalt, macro- and micro-experimental methods were used to analyze the low-temperature crack resistance performance of six types of rubber-powder-modified asphalt mixtures under salt freeze-thaw cycling. By using digital image processing (DIC) technology to record and analyze the loading processes of specimens in semicircular three-point bending (SCB) tests, combined with atomic force microscopy (AFM) tests, the low-temperature crack resistance of the asphalt mixtures was explored, and it was inferred that the micro-mechanical performance indicators of the asphalt were correlated with the low-temperature crack resistance performance indicators of the asphalt mixtures. The results indicate that the salt solution caused greater damage to the asphalt than water. The addition of more rubber powder improved the low-temperature cracking resistance of the asphalt mixtures. There was a significant correlation between the micro-mechanical properties of the asphalt and the low-temperature crack resistance of the asphalt mixtures, and a dynamic mechanical thermal analyzer (DMT) showed a stronger correlation with the strain derivative (E'(t)) than the adhesion force index. The SDYK-type warm-mixing agent had a better effect on the low-temperature cracking resistance of the asphalt mixtures than the EM-type warm-mixing agent.

Enhancement of solid-liquid mixing state quality in a mechanical stirred reactor with serial-chaotic rotation generated by basic speed method.

In this work, a novel controllable chaotic stirring strategy that basic speed with chaotic mappings is proposed to enhance the solid-liquid mixing state quality. Specially, the modern statistical image analysis technique is introduced to explore the intensification mechanism of the mixing process. Results show that the best experimental conditions are obtained by studying the influence of factors such as the type of chaotic mapping, the speed change time, and the basic speed on the mixing state quality. Moreover, the case in which the basic speed is set to 150 r/min generated by the cascaded Logistic-Cubic chaotic mapping is the best while the speed change time is set to 5 s and the fluctuation threshold is 30. The mixing time of this case is 50 s and the shortest, energy consumption is 1.64 × 104 W/m3 and appropriate, the solid particle suspension quality is 83 and the best.

Machine learning in the detection of dental cyst, tumor, and abscess lesions.

BACKGROUND AND OBJECTIVE: Dental panoramic radiographs are utilized in computer-aided image analysis, which detects abnormal tissue masses by analyzing the produced image capacity to recognize patterns of intensity fluctuations. This is done to reduce the need for invasive biopsies for arriving to a diagnosis. The aim of the current study was to examine and compare the accuracy of several texture analysis techniques, such as Grey Level Run Length Matrix (GLRLM), Grey Level Co-occurrence Matrix (GLCM), and wavelet analysis in recognizing dental cyst, tumor, and abscess lesions. MATERIALS & METHODS: The current retrospective study retrieved a total of 172 dental panoramic radiographs with lesion including dental cysts, tumors, or abscess. Radiographs that failed to meet technical criteria for diagnostic quality (such as significant overlap of teeth, a diffuse image, or distortion) were excluded from the sample. The methodology adopted in the study comprised of five stages. At first, the radiographs are improved, and the area of interest was segmented manually. A variety of feature extraction techniques, such GLCM, GLRLM, and the wavelet analysis were used to gather information from the area of interest. Later, the lesions were classified as a cyst, tumor, abscess, or using a support vector machine (SVM) classifier. Eventually, the data was transferred into a Microsoft Excel spreadsheet and statistical package for social sciences (SPSS) (version 21) was used to conduct the statistical analysis. Initially descriptive statistics were computed. For inferential analysis, statistical significance was determined by a p value < 0.05. The sensitivity, specificity, and accuracy were used to find the significant difference between assessed and actual diagnosis. RESULTS: The findings demonstrate that 98% accuracy was achieved using GLCM, 91% accuracy using Wavelet analysis & 95% accuracy using GLRLM in distinguishing between dental cyst, tumor, and abscess lesions. The area under curve (AUC) number indicates that GLCM achieves a high degree of accuracy. The results achieved excellent accuracy (98%) using GLCM. CONCLUSION: The GLCM features can be used for further research. After improving the performance and training, it can support routine histological diagnosis and can assist the clinicians in arriving at accurate and spontaneous treatment plans.

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.

Classification of Alzheimer's disease stages from magnetic resonance images using deep learning.

Alzheimer's disease (AD) is a progressive type of dementia characterized by loss of memory and other cognitive abilities, including speech. Since AD is a progressive disease, detection in the early stages is essential for the appropriate care of the patient throughout its development, going from asymptomatic to a stage known as mild cognitive impairment (MCI), and then progressing to dementia and severe dementia; is worth mentioning that everyone suffers from cognitive impairment to some degree as we age, but the relevant task here is to identify which people are most likely to develop AD. Along with cognitive tests, evaluation of the brain morphology is the primary tool for AD diagnosis, where atrophy and loss of volume of the frontotemporal lobe are common features in patients who suffer from the disease. Regarding medical imaging techniques, magnetic resonance imaging (MRI) scans are one of the methods used by specialists to assess brain morphology. Recently, with the rise of deep learning (DL) and its successful implementation in medical imaging applications, it is of growing interest in the research community to develop computer-aided diagnosis systems that can help physicians to detect this disease, especially in the early stages where macroscopic changes are not so easily identified. This article presents a DL-based approach to classifying MRI scans in the different stages of AD, using a curated set of images from Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies databases. Our methodology involves image pre-processing using FreeSurfer, spatial data-augmentation operations, such as rotation, flip, and random zoom during training, and state-of-the-art 3D convolutional neural networks such as EfficientNet, DenseNet, and a custom siamese network, as well as the relatively new approach of vision transformer architecture. With this approach, the best detection percentage among all four architectures was around 89% for AD vs. Control, 80% for Late MCI vs. Control, 66% for MCI vs. Control, and 67% for Early MCI vs. Control.

Interlocking Evaluation of Mesoscopic Skeleton with the Compaction Degree of Hot-Mix Asphalt.

Asphalt mixtures are multi-phase composites composed of aggregates, bitumen, mineral powders, and voids, and various structures are intertwined during the compaction process. Most of the traditional research focuses on the macro-scale domain, and it is difficult to obtain the internal structure of asphalt mixture in different compaction processes. With the continuous development of digital image technology, the influence of the meso-structure of the asphalt mixture on the compaction quality of the asphalt mixture has become a new means to evaluate the performance of the asphalt mixture. In this paper, different numbers of compactions are selected to represent different stages in the compaction process, the digital images of specimens in different compaction stages are obtained by industrial CT scanning technology. Then, the images are processed and reconstructed in three dimensions using improved image segmentation methods, and the position characteristics and geometric information of coarse aggregate are obtained by combining the Oriented Bounding Box (OBB). The meso-response characteristics of the skeleton structure of the asphalt mixture during compaction were studied. The influence of the internal structure of the mixture on the compaction quality of the mixture was obtained, which is of great significance for the study of improving the durability of the pavement. The results show that the "effective coordination number" (the number of aggregate particles that can transmit force in the skeleton structure) is greatly related to the aggregate size. With the compaction process, the centroid of coarse aggregate in the upper layer of the specimen reflects the overall downward movement trend. The inclination angle of the aggregate spindle tends to be in the range of 80°~100°; the anisotropic amplitude of the xy plane increases, and the direction of the aggregate spindle becomes more and more consistent. With the increase in the number of rotational compactions, these four parameters showed obvious rules, indicating that this meso-characteristic index could well characterize the compaction quality of the asphalt mixture in the compaction process.

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.

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.

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.

Cuspal deflection of teeth restored with bulk fill flowable composite resins, with and without fiber-reinforcement and evaluated by Micro-computed Tomography.

Polymerisation shrinkage of composite resins is a clinical challenge which has been well documented in the literature. Many studies investigating polymerisation shrinkage stress measure cuspal deflection, which is a manifestation of this stress. The introduction of flowable bulk-fill composite (BFRBC) materials has streamlined the restorative process, though the shrinkage and cuspal deflection from these BFRBCs has not been compared with regards to its use with polyethylene fibers (Ribbond fibers). The authors describe a method to measure the cuspal deflection of flowable BFRBCs placed in cavities of standardised dimensions at distinct steps of the restorative process, with and without fiber-reinforcement and using x-ray micro-computed tomography. Co-ordinate points are established on the buccal and lingual aspects of scanned specimens using the Volume Graphics VG Studio max 3.2.5 (Hiedelberg, Germany 2018) software. The system allows for these landmarks to be established across each scan (of the same tooth), ensuring standardization of each specimen. Further anatomical points are used to enable analysis. Comparison of angles generated across these points determines the extent of cuspal deflection.•A method of measuring the cuspal deflection of composite resins is proposed.•Experimental procedures are provided.•Data analysis methods are outlined.

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.

AI Techniques to Identify Nerve Cell Alterations in Digital Images.

Artificial intelligence (AI), utilising computing power, has managed to influence the health sector with many applications based on algorithms, tools, and automated processes. In this work, neuronbiological images acquired by an electronic microscope are assessed under appropriate image processing to identify areas of interest. The algorithmic steps identified the alterations of nerve cells that appeared as areas in the red channel of each digital image.

A forensic evaluation method for DeepFake detection using DCNN-based facial similarity scores.

Detecting DeepFake videos has become a central task in modern multimedia forensics applications. This article presents a method to detect face swapped videos when the portrayed person in the video is known. We propose using a threshold classifier based on similarity scores obtained from a Deep Convolutional Neural Network (DCNN) trained for facial recognition. We compute a set of similarity scores between faces extracted from questioned videos and reference materials of the person depicted. We use the highest score to classify the questioned videos as authentic or fake, depending on the threshold chosen. We validate our method on the Celeb-DF (v2) dataset (Li et al., 2020) [13]. Using the training and testing splits specified on the dataset, we obtained an HTER of 0.020 and an AUC of 0.994, surpassing the most robust approaches against this dataset (Tran et al., 2021) [37]. Additionally, a logistic regression model was used to convert the highest score into a likelihood ratio for greater applicability in forensic analyses.

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.