Image System Research Articles

Compact and cGMP-compliant automated synthesis of [18F]FSPG on the Trasis AllinOne™

Background(S)-4-(3-18F-Fluoropropyl)-ʟ-glutamic acid ([18F]FSPG) is a positron emission tomography radiotracer used to image system xc−, an antiporter that is upregulated in several cancers. Not only does imaging system xc− with [18F]FSPG identify tumours, but it can also provide an early readout of response and resistance to therapy. Unfortunately, the clinical production of [18F]FSPG has been hampered by a lack of robust, cGMP-compliant methods. Here, we report the automated synthesis of [18F]FSPG on the Trasis AllinOne™, overcoming previous limitations to provide a user-friendly method ready for clinical adoption.ResultsThe optimised method provided [18F]FSPG in 33.5 ± 4.9% radiochemical yield in just 35 min when starting with 18–25 GBq. Importantly, this method could be scaled up to > 100 GBq starting activity with only a modest reduction in radiochemical yield, providing [18F]FSPG with a molar activity of 372 ± 65 GBq/µmol and excellent radiochemical purity (96.8 ± 1.1%). The formulated product was stable when produced with these high starting activities.ConclusionsWe have developed the first automated synthesis of [18F]FSPG on the Trasis AllinOne™. The method produces [18F]FSPG with excellent radiochemical purity and in high amounts suitable for large clinical trials and off-site distribution. The method expands the number of synthesis modules capable of producing [18F]FSPG and has been carefully designed for cGMP compliance to simplify regulatory approval for clinical production. The methods developed for the purification of high-activity [18F]FSPG are transferrable and should aid the development of clinical [18F]FSPG productions on other synthesis modules.

Development of an image quality evaluation system for bedside chest X-ray images using scatter correction processing.

In plain radiography, scattered X-ray correction processing (Virtual Grid: VG) is used to estimate and correct scattered rays in images. We developed an objective evaluation system for bedside chest X-ray images using VG and investigated its usefulness. First, we trained the blind/referenceless image spatial quality evaluator (BRISQUE) on 200 images obtained by portable chest radiography. We then evaluated optimal chest phantom VG images as well as those that deviated from the VG setting conditions using BRISQUE. Furthermore, we conducted a subjective evaluation using the mean opinion score (MOS) and established an objective evaluation system for VG images. Finally, the degree of agreement between the MOS subjectively evaluated by 14 radiological technologists and that determined by the objective evaluation system for 100 clinical images obtained by portable chest radiography was calculated using Cohen's kappa coefficient. The correlation coefficient between the BRISQUE score and MOS for chest phantom images was - 0.96 (p < 0.05). The two scores showed a very high linear correlation, indicating the potential of the BRISQUE score as an alternative to MOS. The Cohen's kappa coefficient for the objective evaluation system using the optimal conversion table was 0.42. Conversely, there was a very high detection rate of 82.86% for poor-quality images. An objective evaluation system for bedside chest X-ray images using VG that uses no-reference image quality evaluation helps provide proper image quality. Furthermore, such a system can be constructed with a small amount of training data, which increases the possibility of introducing it to a variety of facilities.

Dual-Modal Approach for Ship Detection: Fusing Synthetic Aperture Radar and Optical Satellite Imagery.

The fusion of synthetic aperture radar (SAR) and optical satellite imagery poses significant challenges for ship detection due to the distinct characteristics and noise profiles of each modality. Optical imagery provides high-resolution information but struggles in adverse weather and low-light conditions, reducing its reliability for maritime applications. In contrast, SAR imagery excels in these scenarios but is prone to noise and clutter, complicating vessel detection. Existing research on SAR and optical image fusion often fails to effectively leverage their complementary strengths, resulting in suboptimal detection outcomes. This research presents a novel fusion framework designed to enhance ship detection by integrating SAR and optical imagery. This framework incorporates a detection system for optical images that utilizes Contrast Limited Adaptive Histogram Equalization (CLAHE) in combination with the YOLOv7 model to improve accuracy and processing speed. For SAR images, a customized Detection Transformer model, SAR-EDT, integrates advanced denoising algorithms and optimized pooling configurations. A fusion module evaluates the overlaps of detected bounding boxes based on intersection over union (IoU) metrics. Fused detections are generated by averaging confidence scores and recalculating bounding box dimensions, followed by robust postprocessing to eliminate duplicates. The proposed framework significantly improves ship detection accuracy across various scenarios.

SECURE AND EFFICIENT CLASSIFICATION OF CHEST X-RAY IMAGES USING CRYPTOGRAPHY AND MACHINE LEARNING TECHNIQUES

Chest X-ray images are an essential diagnostic tool in the medical field, but transmitting and storing these images can raise security concerns. Privacy-preserving techniques in healthcare databases protect sensitive patient information from unauthorized access, breaches, and misuse. Using a combination of elliptic curve cryptography (ECC) for encryption, a convolutional neural network (CNN) for feature extraction, and a long short-term memory (LSTM) network for classification, the aim of this research is to develop a safe and effective classification system for chest X-ray images. The research integrates hyperparameter optimization, essential for LSTM, as it dramatically impacts the model's performance. Additionally, the research employs an oppositionbased learning strategy, the traditional wild horse optimization (WHO) approach and an enhanced WHO (EWHO), to enhance the classification performance. The research objectives are to diagnose and classify chest X-ray images into four types: normal lung, COVID-19-infected lung, lung opacity, and viral pneumonia. The proposed opposition-based learning model improves the accuracy, efficiency, and consistency of diagnostic and treatment decisions. The proposed EWHO-configured LSTM approach attains 97.4&amp;#37; accuracy for an efficient classification system for chest X-ray images. The research highlights the importance of privacy-preserving techniques in healthcare databases to protect sensitive patient information from unauthorized access, breaches, and misuse.

Character/Word Modelling: A Two-Step Framework for Text Recognition in Natural Scene Images

Text recognition from images is a complex task in computer vision. Traditional text recognition methods typically rely on Optical Character Recognition (OCR); however, their limitations in image processing can lead to unreliable results. However, recent advancements in deep-learning models have provided an effective alternative for recognizing and classifying text in images. This study proposes a deep-learning-based text recognition system for natural scene images that incorporates character/word modeling, a two-step procedure involving the recognition of characters and words. In the first step, Convolutional Neural Networks (CNN) are used to differentiate individual characters from image frames. In the second step, the Viterbi search algorithm employs lexicon-based word recognition to determine the optimal sequence of recognized characters, thereby enabling accurate word identification in natural scene images. The system is tested using the ICDAR 2003 and ICDAR 2013 datasets from the Kaggle repository, and achieved accuracies of 79.8% and 81.5%, respectively.

QISSALARDA DAVR VOQELIGI TASVIRI (“SHUM BOLA” VA “SHUM BOLANING NABIRALARI” QISSALARI ASOSIDA)

The article discusses the continuity of Nosir Fazilov’s story “Shum bolaning nabiralari”, Gafur Gulam’s story “Shum bola” in relation to the system of images, and reveals the influence of the reality of the period between the stories on the epic patterns of the work.

Philosophical beauty concept in the Yury Buida’s story “Lilaya Fimochka”

Under conditions of deformation, the attitude of a modern person to spiritual and moral values is of particular relevance to the axiological potential of the philosophical category of beauty and its interpretation in the works of the latest Russian literature. The purpose of this article is to study the author’s interpretation of the aesthetic imperative “beauty” in the story by Yury Buida “Lilaya Fimochka”, which is the quintessential author's understanding of this constant. The tasks were to identify the specifics of the concept of beauty through the analysis of plot and composition, a complex of motifs, a system of images and characters. To achieve the purpose and objectives, methods of cultural-historical and motivational analysis weare involved. It was determined that the plot of the story is an author's myth of the death of divine beauty as the basis of a harmonious universe. Receptions of deconstruction, antithesis, detailing and parody contribute to isolating the following attributes of beauty: blindness, unconsciousness, indifference to man, characteristic of a milestone. Buida’s artistic discovery is the double vision technique (authorial and characters), revealing the ambiguity and diversity of beauty, sometimes perceived as ugliness. It is important that evil and beauty are endowed with an irrational nature. The semantic pairs were revealed: church – market / bazaar, star – man, light – darkness, beauty – a monster, the semantics of which are conceptual in nature. The complex of motives is represented by the motifs of self-abasement, suffering, retribution and resurrection, carnival life, forming the philosophical implication of the work. Ambivalent symbolic images (temple, eyes, angle, stone, cross, ice) bring diversity to the story. The following types of parts were distinguished: olfactory, portrait, habitats, zoomorphic, optical, color, the transformation of some parts into a metaphor is noted. Beauty, which appears as the foundation of world, predetermines the possible spiritual revival of humanity mired in sin.

The Ethno-world of the Nenets writer Yuri Vella: Concepts. Reception

The article is devoted to the formation and development of artistry of the newly written Nenets literature. The main goal was to determine the origins and reveal the ethnocodes of artistic concepts and conceptosphere. Among the tasks was the manifestation of their role and significance in the creation of the image system. There was also a search for methodological approaches to comprehending the complex and little-studied literary process that takes place in peculiar conditions of transculturation. Interdisciplinary, historical-cultural and comparativist approaches were substantiated and activated, and analytical and synthetic reading of texts was used. The research was conducted on the material of Yuri Vella's texts written both in Nenets and Khanty languages and translated into Russian, and those created by him in Russian. The author's active search for peculiar genres and his aspiration to preserve his ancestral ethnicity in the process of transculturation are noted. The specificity of symbolism of a number of transformed and embodied primordial images (archetypes) is revealed. The difference between cognitive and artistic concepts is outlined. It is shown how artistic concepts and the conceptosphere, including mental signs and phenomena preserved by the historical memory of Nenets, participate in the creation of images and the development of plots, in the embodiment of the chosen idea and the disclosure of the theme.

How do different categories of nature‐related landscape images shape emotional experience?

The aim of this study was to explore whether and how diverse categories of nature‐related landscape images would influence the emotional dimensions of pleasure, arousal and dominance in the process of aesthetic appreciation, as well as to develop a new nature‐related image system (NIS). A total of 575 high‐quality images across six categories were selected and assessed by 197 students from the dimensions of aesthetic feeling (AF), pleasure, arousal and dominance. The results showed that different categories evoked varying levels of each dimension, and the impact of landscape categories on emotional experiences cannot be overlooked. Specially, flower landscape showed a clear experimental dissociation in terms of AF and pleasure compared to other categories, suggesting that flower images tend to evoke a positive emotional experience overall, regardless of AF level. Moreover, it also suggested that the effects of flowers on emotions could be considered independently from other categories in future research.

Design of automatic follicle detection and ovarian classification system for ultrasound ovarian images

Design of automatic follicle detection and ovarian classification system for ultrasound ovarian images

A FEMALE LITERARY PORTRAIT IN THE NOVEL «THREE LOVES» BY ARCHIBALD JOSEPH CRONIN

The article analyses the artistic concept of female characters in the dynamics of the author’s worldview on the material of the Scottish writer’s novel, identifies attempts to create a typology of Archibald Joseph Cronin’s female characters, and reveals the specificity of the heroines’ portrayal in realistic prose. The author believes that the study of female images with the help of literary portrait helps to determine the essential features of the prose and artistic world of the writer. The article highlights figurative analogies connected with autobiographical facts, and the typological approach chosen by the author to the female image system in the writer’s work allows not only to determine the internal logic of Archibald Cronin’s artistic worldview, but also the presence of “literary and aesthetic commonality”. The research proves that the writer managed to colourfully draw all female images with the help of his heroine, through her emotions and relationships with other characters. Thus, the writer’s female literary portrait is characterised by the preservation of the features of everyday plausibility and individuality: the female characters in the novel have both general typical features and individual ones.

Longitudinal in silico imaging study comparing digital mammography and digital breast tomosynthesis systems.

In silico clinical trials are becoming more sophisticated and allow for realistic assessment and comparisons of medical image system models. These fully computational models enable fast and affordable trial designs that can closely capture trends seen on real clinical trials. To evaluate three breast imaging system models for digital mammography (DM) and digital breast tomosynthesis (DBT) in a fully-in-silico longitudinalstudy. We developed in silico models for three different breast imaging systems by modeling relevant characteristics such as detector technology, pixel size, number of projections, and angular span. We use a computational image reader to detect masses at different growing stages to compute the relative system performance. Similarly, we compare calcification cluster detectability across systems. The Detectability area under the ROC curve (AUC) was calculated for each combination of breast density, device model, lesion size and type, and search area. We compared the absolute and relative AUC values for DM and DBT. The trial consisted of 45000 simulated images corresponding to 750 virtual digital patientmodels. We observed proportional AUC values with increasing mass size. On the other hand, higher breast densities showed lower AUC values. For masses, we found significant performance differences between device models. The highest average AUC difference between DBT and DM was 0.109, benefiting DBT. For calcifications, DM showed higher performance than DBT, especially in highly dense breasts. The highest AUC difference on a model was -0.055, benefitingDM. In this fully-in-silico imaging trial, we compared three imaging systems with different detector technologies on the same cohort of virtual digital patient models. We found that breast device systems can lead to visibility differences in masses and calcifications. Our longitudinal, multi-device in silico study was possible because of the versatility and flexibility of in silico methods. This study shows the advantages of this in silico methodology in lowering the resources needed for device development, optimization, and regulatory evaluation.

Automatic Colorizing Black and White Image System Using Deep Learning

Colorization involves transforming a grayscale (black-and-white) image into a colorized version that reflects the semantic color tones of the original input. Over recent years, this process has garnered substantial attention, with significant advancements achieved by researchers in the field. The technique has numerous applications, such as in medical imaging and the restoration of historical artifacts. Various methods have been developed to address this challenge, leveraging technologies like Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). These models differ in their architectural frameworks and are evaluated using diverse datasets. This paper explores some of these methodologies, comparing the performance of generative models with traditional deep neural networks, while also highlighting existing limitations. Additionally, it provides a concise overview of past and current progress in the domain of image colorization, drawing on contributions from numerous researchers and studies. a widely recognized deep learning algorithm. The proposed model processes grayscale images as input and predicts their corresponding colors based on training data. The Lab color space is employed in this approach, where the model receives the L channel (lightness) as input and predicts the ab channels (color information) as output. A subset of the ImageNet dataset was used for training and evaluation, consisting of 39,604 randomly selected images, divided into 80% for training and 20% for testing.

Образы Тристана и Изольды в художественном мире произведений русских поэтов-символистов

This article is devoted to the consideration of the eternal images of Tristan and Isolde in the lyrical works of Russian symbolist poets. Russian culture and literature are interested in the famous Celtic legend against the background of a wide fascination with the medieval culture of Western Europe, perceived by symbolists in line with the ideas of panaesthetism of the era of Russian modernity. The purpose of the study is to determine the functions, place and content of the images of Tristan and Isolde in the artistic world of poems by Russian symbolist poets. This world is revealed in the categories of a lyrical event, a system of images, a lyrical situation, motive and plot. The research material was such works as "Humbly betraying myself to burn..." (1910) by M. Voloshin, "Thursday" (1909-1910) by C. de Gabriak, "The Elusive Kiss ..." (1919) by V. I. Ivanov, "My darling went fishing..." (1921) by F. K. Sologub, "The Temptation of Death" (1922) by V. Y. Bryusov. The scientific novelty of the study lies in the fact that as a result of the analysis, the ideas about the peculiarities of the interpretation of the eternal images of Tristan and Isolde by Russian symbolist poets were significantly rethought and concretized, and the national specifics of their interpretation were also determined. Based on the results of the comparative analysis, the following results were obtained for the first time. The images of Tristan and Isolde appear in the lyrical works of Russian symbolist poets in the function of generalized, multi-valued symbols, which are interpreted very inconsistently. They are used to comprehend the life trials of lyrical heroes, and their meaning is created in the halo of previous interpretations, starting with Dante's Divine Comedy and ending with R. Wagner's opera, as well as in correlation with other mythological heroes and autobiographical subtext. In general, Tristan and Isolde, contrary to the previous Western European tradition, seem to be a sign of such a spiritual experience that denies the ideal of great love as a harmonizing and enlightening force. Mutual passion, principles of honor attract lyrical heroes, correlated with Tristan or Isolde, to the torments of hell and death, and their hopes for happiness turn out to be illusory.

Computer vision: applications in Biomedical Engineering

The focus of this study is the presentation of two systems, which use computer vision techniques. In the first, the authors applied these techniques to the acquisition and processing system of ovitrap images. However, the second system is a physiotherapeutic evaluation platform for dynamic monitoring of body balance. By using a depth image of the patient (based on a 3D camera), the algorithm can predict the exact positions of the joints of an individual, without temporal information. The two studies described presented showed promising results, which demonstrated that computer vision techniques are of great importance. The experiments with the Aedes Aegypti mosquito egg has allowed accurate detection and counting of them. The experiments with body balance analysis has allowed the tracking of the center of mass of an individual during physiotherapeutic exercises. Software has detected the movement of the center of mass, satisfying a physiotherapeutic need. The techniques developed for computer vision have many possible applications in different areas and different populations, not only in the areas presented in this study.

The Influence of Brand Image, Product Quality, Promotion, and Purchasing System on Purchasing Decisions (Honda Automatic Motorcycle Purchase Decision in Yogyakarta City)

This study aims to analyze brand image, product quality, promotion, and purchasing system on purchasing decisions of Honda automatic motorcycles in the city of Yogyakarta. The population in this study were all users of Honda automatic motorcycles in the city of Yogyakarta. The sample in the study was 150 respondents. This study uses a quantitative approach. The data collection method uses a questionnaire that has been tested for validity and reliability. The data analysis technique used to answer the hypothesis is multiple linear regression T test and F test. The results of this study indicate that brand image has a positive and significant effect on purchasing decisions, product quality has a positive and significant effect on purchasing decisions, promotion has a positive and significant effect on purchasing decisions, purchasing systems have a positive and significant effect on purchasing decisions and show that brand image, product quality, promotion and purchasing systems have a simultaneous effect on purchasing decisions. The R square value of 0.659 indicates that the ability of the independent variable to explain the variation of the dependent variable is 65.9% and the remaining 34.1% is explained by variables outside the research model..

Study on preparation and influencing factors of coal gangue fill slurry material

The large quantity of coal gangue will become an increasingly serious part of environmental pollution. However, it can be aggregated with water and fly ash to form a slurry with a stabile rheology that can be used to fill underground mining areas to reduce environmental pollution. In this study, an orthogonal test scheme was formulated, and the significance of the influence of test factors on the test index was judged using variance analysis and range analysis. By analyzing the effects of mass concentration, fly ash percentage, and aggregate grade on the rheology and stability of the slurry, a fill slurry that meets the requirements of reasonable indexes was developed. Finally, the diffusion effect of the prepared slurry was explored using a simulation. The results show that the mass concentration had the strongest effect on the plastic viscosity, yield stress, extensibility, water secretion rate, and segregation rate of the gangue slurry. The effect of aggregate gradation on the yield stress, water secretion rate, and segregation rate of slurry was stronger than that of the fly ash content, while the effect of the fly ash content on the plastic viscosity and extensibility of the slurry was stronger than that of the aggregate gradation. With the increase in mass concentration and the content of fine particles in the aggregate, the plastic viscosity of the slurry increased, the slump extension decreased, and the fluidity decreases while the yield stress increased, the water secretion rate and segregation rate decreased, and the stability increased. The optimal ratio of the slurry was determined to be 70 % mass concentration, 10 % fly ash, and an aggregate grading consisting of 80.34 % 0–1 mm particles, 12.95 % 1–3 mm particles, and 6.71 % 3–5 mm particles. The flow and diffusion effect of the optimized gangue slurry in the mining area was observed through an image and stress data acquisition system, and it was determined that the slurry could well be filled into the rock voids in the mining area. The results of the study can provide some theoretical basis for the preparation of slurry to fill hollow zones.

Mapping Phosphorus Concentration in Lake Mainit, Philippines Using Landsat 8 OLI and GIS Techniques

Abstract. Lake Mainit is the fourth largest lake in the Philippines and water is one of the most important natural resources for humans and other living organisms. Sensors on satellites can measure the amount of solar radiation absorbed by surface water at different wavelengths, which can be compared with parameters of water quality. This study explored the use of Landsat 8 Operational Land Imager (OLI) and Geographic Information System (GIS) techniques for assessing the Phosphorus (P) content in the lake. Water quality data of Phosphorus from Environmental Management Bureau (EMB) was utilized to correlate with spectral values. Linear forward regression results yielded significant correlation with band combination B2/B4 with an R2 of 55% for Phosphorus (P). The model was then used to map the P content in the lake and was found out that the concentration ranges from 0.0228768 mg/L to 0.23145 mg/L. The study was effective in correlating P content and the bands of Landsat 8 OLI. In addition, GIS techniques were used in extracting spectral values of bands.

Deep learning method for detecting fluorescence spots in cancer diagnostics via fluorescence in situ hybridization

Fluorescence in Situ Hybridization (FISH) is a technique for macromolecule identification that utilizes the complementarity of DNA or DNA/RNA double strands. Probes, crafted from selected DNA strands tagged with fluorophore-coupled nucleotides, hybridize to complementary sequences within the cells and tissues under examination. These are subsequently visualized through fluorescence microscopy or imaging systems. However, the vast number of cells and disorganized nucleic acid sequences in FISH images present significant challenges. The manual processing and analysis of these images are not only time-consuming but also prone to human error due to visual fatigue. To overcome these challenges, we propose the integration of medical imaging with deep learning to develop an automated detection system for FISH images. This system features an algorithm capable of quickly detecting fluorescent spots and capturing their coordinates, which is crucial for evaluating cellular characteristics in cancer diagnosis. Traditional models struggle with the small size, low resolution, and noise prevalent in fluorescent points, leading to significant performance declines. This paper offers a detailed examination of these issues, providing insights into why traditional models falter. Comparative tests between the YOLO series models and our proposed method affirm the superior accuracy of our approach in identifying fluorescent dots in FISH images.

A new method for judging thermal image quality with applications

Infrared thermal imaging, a non-destructive testing technology, measures the surface temperature of objects. Assessing thermal image quality is crucial for image monitoring, system design, algorithm optimization, and benchmarking. However, developing objective metrics that align with human perception is challenging due to the distinct structure of thermal images, which often feature high background temperatures and minimal variance between objects and the background. Existing methods typically target specific local features or overall image contrast, but new measures are needed to bridge the gap between objective performance and the unique characteristics of thermal images.We propose a novel image quality assessment (IQA) method inspired by the human vision system, specifically designed for thermal images, harmonizing local and global data. The primary contributions include (1) innovative local, global, and hybrid thermal quality assessment methods that deliver precise image quality predictions without needing reference images, (2) an experimental analysis evaluating the developed blind thermal IQA measure’s applicability to various thermal images, and (3) a comprehensive analysis of traditional IQA measure-based methods applied to publicly accessible thermal databases. Extensive simulations demonstrate our method’s competitive performance and strong alignment with human perception of image quality.