Digital Imaging Applications Research Articles

Integrating Digital Imaging and AI in Forensic Odontology: Enhancing Identification Accuracy and Efficiency

Background: The integration of digital imaging and artificial intelligence (AI) in forensic odontology can significantly enhance the accuracy and efficiency of dental identification processes. These technologies provide precise and rapid analysis, which is crucial for forensic investigations. However, implementing such technologies also introduces new ethical challenges that must be addressed. Purpose: To explore the synergistic use of digital imaging and artificial intelligence (AI) in forensic odontology. This section focuses on how these technologies improve identification accuracy and efficiency while discussing the ethical considerations that arise from their implementation. Methodology: This methodology involves a comprehensive review of the existing literature on the applications of digital imaging and AI in forensic odontology. Key studies, clinical trials, and case examples are analyzed to evaluate the effectiveness of these technologies in improving forensic outcomes. The review assesses these technologies’ impact on identification accuracy and efficiency while addressing ethical concerns such as data privacy and AI biases Results: The review highlights that the combination of digital imaging and AI significantly enhances the accuracy and speed of dental identification. Advanced algorithms can process large datasets and identify subtle patterns that are often missed by human examiners, resulting in more reliable forensic outcomes. However, ethical challenges, including data privacy concerns and potential biases in AI algorithms, are critical areas that require careful consideration and regulation. Ongoing research and policy development are essential to address these issues and ensure the ethical application of AI in forensic odontology.

Prediction of Tumor-Associated Macrophages and Immunotherapy Benefits Using Weakly Supervised Contrastive Learning in Breast Cancer Pathology Images.

The efficacy of immune checkpoint inhibitors is significantly influenced by the tumor immune microenvironment (TIME). RNA sequencing of tumor tissue can offer valuable insights into TIME, but its high cost and long turnaround time seriously restrict its utility in routine clinical examinations. Several recent studies have suggested that ultrahigh-resolution pathology images can infer cellular and molecular characteristics. However, few study pay attention to the quantitative estimation of various tumor infiltration immune cells from pathology images. In this paper, we integrated contrastive learning and weakly supervised learning to infer tumor-associated macrophages and potential immunotherapy benefit from whole slide images (WSIs) of H &E stained pathological sections. We split the high-resolution WSIs into tiles and then apply contrastive learning to extract features of each tile. After aggregating the features at the tile level, we employ weak supervisory signals to fine-tune the encoder for various downstream tasks. Comprehensive experiments on two independent breast cancer cohorts and spatial transcriptomics data demonstrate that the computational pathological features accurately predict the proportion of tumor-infiltrating immune cells, particularly the infiltration level of macrophages, as well as the immune subtypes and potential immunotherapy benefit. These findings demonstrate that our model effectively captures pathological features beyond human vision, establishing a mapping relationship between cellular compositions and histological morphology, thus expanding the clinical applications of digital pathology images.

Image processing technology based on OMP reconstruction optimization algorithm

With the widespread application of digital images, image processing technology plays an important role in fields such as computer vision and image analysis. Based on the orthogonal matching pursuit algorithm, an image processing method is proposed. In the process, sparse representation and reconstruction algorithm are used for image compressed sensing to complete image sampling operation. Afterwards, the theory of overcomplete sparse representation is introduced to optimize sparse representation, and an overcomplete dictionary is used to remove Gaussian noise, achieving the goal of image processing. The experimental results indicate that the research method do not show significant deficiencies in signal reconstruction when testing reconstructed signals under sparsity of 8; When testing the calculation time, the calculation time of the research method is about 0.212 s when the sparsity is 5 in the Lenna; In the error test, the mean square difference of the research method in the Lenna is stable at about 14.6; When conducting application analysis, the variance eigenvalues of the research method remained below 9.4. This indicates that the research method has good performance and can effectively process images, providing new technical support for image processing.

Application of Digital Holographic Imaging to Monitor Real-Time Cardiomyocyte Hypertrophy Dynamics in Response to Norepinephrine Stimulation.

Cardiomyocyte hypertrophy, characterized by an increase in cell size, is associated with various cardiovascular diseases driven by factors including hypertension, myocardial infarction, and valve dysfunction. In vitro primary cardiomyocyte culture models have yielded numerous insights into the intrinsic and extrinsic mechanisms driving hypertrophic growth. However, due to limitations in current approaches, the dynamics of cardiomyocyte hypertrophic responses remain poorly characterized. In this study, we evaluate the application of the Holomonitor M4 digital holographic imaging microscope to track dynamic changes in cardiomyocyte surface area and volume in response to norepinephrine treatment, a model hypertrophic stimulus. The Holomonitor M4 permits non-invasive, label-free imaging of three-dimensional changes in cell morphology with minimal phototoxicity, thus enabling long-term imaging studies. Untreated and norepinephrine-stimulated primary neonatal rat cardiomyocytes were live-imaged on the Holomonitor M4, which was followed by image segmentation and single-cell tracking using the HOLOMONITOR App Suite software version 4.0.1.546. The 24 h treatment of cultured cardiomyocytes with norepinephrine increased cardiomyocyte spreading and optical volume as expected, validating the reliability of the approach. Single-cell tracking of both cardiomyocyte surface area and three-dimensional optical volume revealed dynamic increases in these parameters throughout the 24 h imaging period, demonstrating the potential of this technology to explore cardiomyocyte hypertrophic responses with greater temporal resolution; however, technological limitations were also observed and should be considered in the experimental design and interpretation of results. Overall, leveraging the unique advantages of the Holomonitor M4 digital holographic imaging system has the potential to empower future work towards understanding the molecular and cellular mechanisms underlying cardiomyocyte hypertrophy with enhanced temporal clarity.

Digital Holography and 3D Imaging 2023: introduction to the joint feature issue in Applied Optics and Journal of the Optical Society of America A.

The Optica Topical Meeting on Digital Holography and 3D Imaging (DH) was held 14-17 August 2023 in Boston, Massachusetts. The meeting was organized co-jointly with the Optica Imaging Congress. Feature issues based on the DH meeting series have been released by Applied Optics (AO) since 2007. Since 2017, AO and the Journal of the Optical Society of America A (JOSA A) have presented a feature issue in each journal. This feature issues includes 17 papers in AO and 9 in JOSA A. Together they cover a large range of topics, reflecting the rapidly expanding techniques and applications of digital holography and 3D imaging. The upcoming DH Conference (DH 2024) will be held from 3 to 6 June in Paestum, Italy.

Digital Holography and 3D Imaging: introduction to the joint feature issue in Applied Optics and Journal of the Optical Society of America A.

The Optica Topical Meeting on Digital Holography and 3D Imaging (DH) was held 14-17 August 2023 in Boston, Massachusetts. The meeting was organized co-jointly with the Optica Imaging Congress. Feature issues based on the DH meeting series have been released by Applied Optics (AO) since 2007. Since 2017, AO and the Journal of the Optical Society of America A (JOSA A) have presented a feature issue in each journal. This feature issues includes 17 papers in AO and 9 in JOSA A. Together they cover a large range of topics, reflecting the rapidly expanding techniques and applications of digital holography and 3D imaging. The upcoming DH Conference (DH 2024) will be held from 3 to 6 June in Paestum, Italy.

MCMC algorithm based on Markov random field in image segmentation.

In the realm of digital image applications, image processing technology occupies a pivotal position, with image segmentation serving as a foundational component. As the digital image application domain expands across industries, the conventional segmentation techniques increasingly challenge to cater to modern demands. To address this gap, this paper introduces an MCMC-based image segmentation algorithm based on the Markov Random Field (MRF) model, marking a significant stride in the field. The novelty of this research lies in its method that capitalizes on domain information in pixel space, amplifying the local segmentation precision of image segmentation algorithms. Further innovation is manifested in the development of an adaptive segmentation image denoising algorithm based on MCMC sampling. This algorithm not only elevates image segmentation outcomes, but also proficiently denoises the image. In the experimental results, MRF-MCMC achieves better segmentation performance, with an average segmentation accuracy of 94.26% in Lena images, significantly superior to other common image segmentation algorithms. In addition, the study proposes that the denoising model outperforms other algorithms in peak signal-to-noise ratio and structural similarity in environments with noise standard deviations of 15, 25, and 50. In essence, these experimental findings affirm the efficacy of this study, opening avenues for refining digital image segmentation methodologies.

Image Encryption Algorithm based on Pseudo-random Sequence

The applications of digital images is widespread in various fields such as medical diagnosis, film production, intelligent transportation, security monitoring, virtual reality and computer vision. The security of image transmission is a widely discussed topic, and ensuring the protection of digital images has become a focal point in Internet security communication. The selection of an algorithm with strong encryption performance holds significant theoretical and practical importance for image transmission. This paper takes the pseudo-random sequence as a starting point and primarily utilizes scrambling operations, the Mersenne rotation algorithm, and operations based on chaotic sequences to explore image encryption algorithms. Firstly, it introduces the encryption principles of these three methods, followed by respective application cases and implementation using MATLAB software. Pseudo-random sequences are employed as tools in all three methods, reo rdering pixel positions according to their order in the pseudo-random sequence during scrambling operation. In the Mersenne rotation algorithm, pseudo-random sequences are used to generate random number seeds. Chaotic sequences are utilized in combination with scrambling operations to achieve image encryption through generating pseudo-random sequences. Finally, after analyzing information entropy, histograms, encryption/decryption times, and adjacent pixel correlations, we select the optimal image encryption algorithm that provides better digital image encryption effects and higher security levels for each application case along with code implementations.

Tinjauan Pewarnaan Hemaktosilin-Eosin dan Periodic Acid-Schiff terhadap Kerusakan Hati Mencit yang Diinduksi Aloksan

The liver is an organ that serves several functions in the body, including glucose metabolism to provide energy to other tissues. Hepatocytes are the primary functional cells of the liver, which also regulate liver glucose release via glucose transport protein-2. Hepatocyte injury could occur from toxic substances and diseases such as Diabetes Mellitus. Alloxan is an organic compound commonly used in diabetes research as a diabetogenic agent. Alloxan causes diabetes through selective inhibition of glucose-stimulated insulin secretion and induced formation of reactive oxygen species (ROS) that promotes pancreatic beta cell necrosis. Alloxan also affects the liver's histological condition, including the hepatocellular structure and glycogen content. HE and PAS are used for evaluating this condition. However, both should be reviewed to evaluate their abilities. Mice were divided into control and test groups, each consisting of 5 mice. The test group was intervein-induced with 25, 50, and 100mg/KgBB alloxan on the second day of arrival. The mice's livers were then taken on the seventh day; tissue processing was carried out to get 20 blocks of mice's livers. Two 5-μm-thick paraffin-embedded sections from each group were stained with hematoxylin-eosin, and periodic acid Schiff, respectively. Mice's liver slides are examined microscopically for the degree of injury and glycogen concentration for further evaluation using ImageJ digital imaging application. This study found that microscopical and ANOVA tests of both staining methods successfully produced significant differences between control and various dose alloxan-induced groups of mice.

Research on the Narrative Arts Artistic Expression of Digital Images in Museums A Case Study of H.C Andersson's House

With the rapid development of digital technology, domestic museum exhibitions gradually use digital images as the main exhibition technology, but there is still a lack of diversified and innovative application methods. Taking the digital images in the H.C Andersson's House as an example, based on the two dimensions of "human" centered immersive space and empathetic story, this paper sums up three different artistic expression methods of "switching perspectives", "interactive narration" and "immersive space", aiming to provide reference for the digital image application in domestic museum exhibitions.

Perbaikan Citra Tanda Tangan Digital Menggunakan Metode Otsu Thressholding dan Sauvola

Abstract : The digital era forces people to digitize in all fields. Digital product that is often found is digital image. One kind of digital image application is the use of a digital image signature embedded in a document. However, often the results are unsatisfactory, such as background color problems, noise, lack of clarity, etc. The quality of digital image signatures can be improved by implementing the Otsu Thresholding and Sauvola methods. These two methods were chosen because they are widely used in document image quality improvement. The purpose of this study is to produce a better digital image signature and to compare the performance of these two methods. The results showed that the quality of the images produced by these two methods was better than the original image or by using standard filters from a word processing application. Meanwhile, from the two methods used, Sauvola's method was slightly better than Otsu's method. In terms of visual evaluations, the Sauvola method total score is 13 compare to Otsu method score that is 11. Meanwhile, the PSNR ratio show that the two methods give the same results, that is 34,571 db.

Review Study of Image De-Noising on Digital Image Processing and Applications

This paper reviews several studies of image de-noising on digital image processing and applications. Noisy images contain different noise that exist either due to environment or electronic interferences. Ergo, de-noising is crucial to eliminate the noise that disturb data collecting process. The impact of de-noising on image processing can result for accurate and precise data collected from the image. Additionally, de-noising process required several crucial steps that help to enhance knowledge on digital image and its application. Hence, study and understanding de-noising can improve multiple aspect such as image quality, data sensitivity and specificity, accuracy of the collected data, and increase the percentage of each parameter.

Application 3D Forensic Science in a Criminal Investigation

This manuscript discusses the modern approach and application of 3D digital imaging in forensic science. It presents the basic principles and approaches of 3D modeling methods. Selected methods of image capture and its subsequent processing into a 3D model are applied to a specific object. This object is captured by a mobile phone camera, a LiDar sensor, and a 3D scanner for further image processing for different desired image outputs. The text describes the photogrammetry method, the workflow with the LiDar sensor, and the 3D model of the object intended for 3D printing. The paper discusses the potential of the selected methods and their application in forensic sciences.

A New Method to Detect Splicing Image Forgery Using Convolutional Neural Network

Recently, digital images have been considered the primary key for many applications, such as forensics, medical diagnosis, and social networks. Image forgery detection is considered one of the most complex digital image applications. More profoundly, image splicing was investigated as one of the common types of image forgery. As a result, we proposed a convolutional neural network (CNN) model for detecting splicing forged images in real-time and with high accuracy, with a small number of parameters as compared with the recently published approaches. The presented model is a lightweight model with only four convolutional layers and four max-pooling layers, which is suitable for most environments that have limitations in their resources. A detailed comparison was conducted between the proposed model and the other investigated models. The sensitivity and specificity of the proposed model over CASIA 1.0, CASIA 2.0, and CUISDE datasets are determined. The proposed model achieved an accuracy of 99.1% in detecting forgery on the CASIA 1.0 dataset, 99.3% in detecting forgery on the CASIA 2.0 dataset, and 100% in detecting forgery on the CUISDE dataset. The proposed model achieved high accuracy, with a small number of parameters. Therefore, specialists can use the proposed approach as an automated tool for real-time forged image detection.

Peningkatan Minat Belajar Keterampilan Bercerita melalui Gambar Seri Digital pada Siswa Sekolah Dasar

This study aims to increase interest in learning storytelling skills through digital series pictures in elementary school students. In this study, we will discuss the application of digital serial images to increase interest in learning storytelling skills using digital serial images and the obstacles that arise during application and solutions to overcome them. The methods of data collection in this study were interviews, documentation, and observation. While the data analysis technique is a qualitative descriptive technique of each cycle. The results of this study can be concluded that the interest in learning storytelling skills obtained has increased from cycle I to cycle II by 19%. Thus it can be concluded that through the media of digital series pictures can increase interest in learning storytelling skills in elementary schools. This can be seen from the presentation of interest in telling stories in Indonesian subjects before the action and after the action in each cycle the results achieved have increased
 
 Keywords: Digital Series Pictures, Interest in Learning

Application of Digital Imaging and Artificial Intelligence to Pathology of the Placenta.

Digital imaging, including the use of artificial intelligence, has been increasingly applied to investigate the placenta and its related pathology. However, there has been no comprehensive review of this body of work to date. The aim of this study was to therefore review the literature regarding digital pathology of the placenta. A systematic literature search was conducted in several electronic databases. Studies involving the application of digital imaging and artificial intelligence techniques to human placental samples were retrieved and analyzed. Relevant articles were categorized by digital image technique and their relevance to studying normal and diseased placenta. Of 2008 retrieved articles, 279 were included. Digital imaging research related to the placenta was often coupled with immunohistochemistry, confocal microscopy, 3D reconstruction, and/or deep learning algorithms. By significantly increasing pathologists' ability to recognize potentially prognostic relevant features and by lessening inter-observer variability, published data overall indicate that the application of digital pathology to placental and perinatal diseases, along with clinical and radiology correlation, has great potential to improve fetal and maternal health care including the selection of targeted therapy in high-risk pregnancy.

Application of Digital Imaging Allied to Chemometrics in the Use of Non-destructive Phenotyping of Sesame Seeds

In this article, digital image processing and analysis (DIPA) combined with chemometric methods, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to discriminate sesame seeds through their digitized images. For this purpose, four groups of seeds were used: BRS Anahí and BRS Seda cultivars, a lineage and a commercial sample. The images were scanned using an HP officejet 7610 scanner and, for extraction of the red-green-blue channels and colorimetric profile, the ImageJ software was used. The DIPA combined with chemometric methods allowed us to discriminate the four groups of sesame seeds efficiently, and a minimum accumulated variance of 89.03% of the total variance was obtained. The trends observed via the PCA were confirmed through the dendrograms obtained using the HCA. The results achieved in this work indicate that the proposed methodology can be a simple analytical alternative for the non-destructive phenotypic discrimination of seeds, with their color as an attribute.

A Fractal Approach to Nonlinear Topographical Features of Healthy and Keratoconus Corneas Pre- and Post-Operation of Intracorneal Implants

Fractal dimension (FD) together with advances in imaging technologies has provided an increasing application of digital images to interpret biological phenomena. In ophthalmology, topography-based images are increasingly used in common practices of clinical settings. They provide detailed information about corneal surfaces. Few-micron alterations of the corneal geometry to the elevation and curvature cause a highly multifocal surface, change the corneal optical power up to several diopters, and therefore adversely affect the individual’s vision. Keratoconus (KCN) is a corneal disease characterized by a local alteration of the corneal anatomical and mechanical features. The formation of cone-shaped regions accompanied by thinning and weakening of the cornea are the major manifestations of KCN. The implantation of tiny arc-like polymeric sections, known as intracorneal implants, is considered to be effective in restoring the corneal curvature. This study investigated the FD nature of healthy corneas (n = 7) and compared it to the corresponding values before and after intracorneal implant surgery in KCN patients (n = 7). The generalized Hurst exponent, Higuchi, and Katz FDs were computed for topography-based parameters of corneal surfaces: front elevation (ELE-front), back elevation (ELE-back), and corneal curvature (CURV). The Katz FD showed better discriminating ability for the diseased group. It could reveal a significant difference between the healthy corneas and both pre- and post-implantation topographies (p < 0.001). Moreover, the Katz dimension varied between the topographic features of KCN patients before and after the treatment (p < 0.036). We propose to describe the curvature feature of corneal topography as a “strange attractor” with a self-similar (i.e., fractal) structure according to the Katz algorithm.

Digital skin imaging applications, part II: a comprehensive survey of post-acquisition image utilization features and technology standards.

Despite the increasing ubiquity and accessibility of teledermatology applications, few studies have comprehensively surveyed their features and technical standards. Importantly, features implemented after the point of capture are often intended to augment image utilization, while technical standards affect interoperability with existing healthcare systems. We aim to comprehensively survey image utilization features and technical characteristics found within publicly discoverable digital skin imaging applications. Applications were identified and categorized as described in Part I. Included applications were then further assessed by three independent reviewers for post-imaging content, tools, and functionality. Publicly available information was used to determine the presence or absence of relevant technology standards and/or data characteristics. A total of 20 post-image acquisition features were identified across three general categories: (1) metadata attachment, (2) functional tools (i.e., those that utilized images or in-app content to perform a user-directed function), and (3) image processing. Over 80% of all applications implemented metadata features, with nearly half having metadata features only. Individual feature occurred and feature richness varied significantly by primary audience (p < 0.0001) and function (p < 0.0001). On average, each application included under three features. Less than half of all applications requested consent for user-uploaded photos and fewer than 10% provided clear data use and privacy policies. Post-imaging functionality in skin imaging applications varies significantly by primary audience and intended function, though nearly all applications implemented metadata labeling. Technical standards are often not implemented or reported consistently. Gaps in the provision of clear consent, data privacy, and data use policies should be urgently addressed.

A chaos-based image compression and encryption scheme using fractal coding and adaptive-thresholding sparsification

With the wide application of digital images in the network, the problems of information security and transmission efficiency need to be solved urgently. Fractal encoding as an efficient compression method, its security is not guaranteed. To solve these problems, this paper proposes a new image compression and encryption scheme based on chaotic map and frequency division joint compression coding, which uses the combination of fractal encoding and adaptive-thresholding sparsification to compress images. Arnold scrambling and chaos-based plane scrambling diffusion are embedded in two compression processes respectively. In addition, an efficient encryption algorithm is designed based on the coupling-enhanced chaotic map, which encrypt the compressed data as a whole through simultaneous scrambling of parent blocks and sub-blocks and zigzag round-trip diffusion. The simulation results demonstrate that the proposed image compression and encryption scheme is suitable for gray and color images, and can achieve higher compression ratio and improve the efficiency of fractal compression. The designed encryption algorithm is reliable, and more efficient than several advanced image encryption schemes.