Image Data Processing Research Articles

Differential Diagnostics of the Disappearing Lung Syndrome in Lymphangioleiomyomatosis and COVID-19 Pneumonia Using Digital Software Processing of Computer Tomography Data (Clinical Cases)

About 20 % of people who fell ill during the COVID-19 pandemic had a severe course of the disease, which was accompanied by various complications. One of these complications is the disappearing lung syndrome, which can be observed both in the acute period of the disease and in the post-COVID period. Under the mask of pulmonary complications of COVID-19, rare interstitial lung diseases may be diagnosed late. COVID-19 is characterised by the development of systemic thrombovasculitis against the background of a hyperimmune response caused by SARS-CoV-2. These pathological processes can lead to the formation of giant multicompartmental cystic cavities in the lungs, which are similar to those observed in lymphangioleiomyomatosis (LAM). Objective — to investigate the possibility of differential diagnosis of the disappearing lung syndrome in lung lymphangioleiomyomatosis and the complicated course of viral pneumonia COVID-19 using digital software processing of CT data Materials and methods. The data of CT lung of patients with LAM and patients with a complicated course of viral pneumonia COVID-19 were analyzed in dynamics. CT was performed on an Aquilion TSX-101A Tochiba scanner (Japan) with subsequent digital processing using the Dragonfly program, OBYECT RESEARCH SYSTEMS (ORS), Montreal, Canada, and comparison of the obtained results with pathomorphological changes. Examples of own observations are given. Results and discussion. Researched of changes in the structure of the lung parenchyma in cases of LAM and patients with a complicated course of nosocomial viral pneumonia of COVID-19 were studied by means of software digital processing of CT OGK data. The obtained results in the form of segmented histograms are correlated with pathomorphological changes in lung tissue.Digital software processing of CT data clearly reflects the morphological structure of the lung parenchyma and allows diagnosis and differential diagnosis of «disappearing lung syndrome» in various diseases. Conclusions. Carrying out digital software processing of CT OGK data allows differential diagnosis of various pathological processes, which are radiologically manifested by the same symptoms.

Accuracy of an Artificial Intelligence Chatbot’s Interpretation of Clinical Ophthalmic Images

Ophthalmology is reliant on effective interpretation of multimodal imaging to ensure diagnostic accuracy. The new ability of ChatGPT-4 (OpenAI) to interpret ophthalmic images has not yet been explored. To evaluate the performance of the novel release of an artificial intelligence chatbot that is capable of processing imaging data. This cross-sectional study used a publicly available dataset of ophthalmic cases from OCTCases, a medical education platform based out of the Department of Ophthalmology and Vision Sciences at the University of Toronto, with accompanying clinical multimodal imaging and multiple-choice questions. Across 137 available cases, 136 contained multiple-choice questions (99%). The chatbot answered questions requiring multimodal input from October 16 to October 23, 2023. The primary outcome was the accuracy of the chatbot in answering multiple-choice questions pertaining to image recognition in ophthalmic cases, measured as the proportion of correct responses. χ2 Tests were conducted to compare the proportion of correct responses across different ophthalmic subspecialties. A total of 429 multiple-choice questions from 136 ophthalmic cases and 448 images were included in the analysis. The chatbot answered 299 of multiple-choice questions correctly across all cases (70%). The chatbot's performance was better on retina questions than neuro-ophthalmology questions (77% vs 58%; difference = 18%; 95% CI, 7.5%-29.4%; χ21 = 11.4; P < .001). The chatbot achieved a better performance on nonimage-based questions compared with image-based questions (82% vs 65%; difference = 17%; 95% CI, 7.8%-25.1%; χ21 = 12.2; P < .001).The chatbot performed best on questions in the retina category (77% correct) and poorest in the neuro-ophthalmology category (58% correct). The chatbot demonstrated intermediate performance on questions from the ocular oncology (72% correct), pediatric ophthalmology (68% correct), uveitis (67% correct), and glaucoma (61% correct) categories. In this study, the recent version of the chatbot accurately responded to approximately two-thirds of multiple-choice questions pertaining to ophthalmic cases based on imaging interpretation. The multimodal chatbot performed better on questions that did not rely on the interpretation of imaging modalities. As the use of multimodal chatbots becomes increasingly widespread, it is imperative to stress their appropriate integration within medical contexts.

Performance of ChatGPT on the test of understanding graphs in kinematics

The well-known artificial intelligence-based chatbot ChatGPT-4 has become able to process image data as input in October 2023. We investigated its performance on the test of understanding graphs in kinematics to inform the physics education community of the current potential of using ChatGPT in the education process, particularly on tasks that involve graphical interpretation. We found that ChatGPT, on average, performed similarly to students taking a high-school level physics course, but with important differences in the distribution of the correctness of its responses, as well as in terms of the displayed “reasoning” and “visual” abilities. While ChatGPT was very successful at proposing productive strategies for solving the tasks on the test and expressed correct reasoning in most of its responses, it had difficulties correctly “seeing” graphs. We suggest that, based on its performance, caution and a critical approach are needed if one intends to use it in the role of a tutor, a model of a student, or a tool for assisting vision-impaired persons in the context of kinematics graphs. Published by the American Physical Society 2024

ВЛИЯНИЕ ИСКУССТВЕННОГО СТАРЕНИЯ НА ИЗМЕНЕНИЕ ШЕРОХОВАТОСТИ ГЛАЗУРОВАННОЙ И ПОЛИРОВАННОЙ ПОВЕРХНОСТИ ДИОКСИДА ЦИРКОНИЯ: ИССЛЕДОВАНИЕ IN VITRO

Aim: to evaluate the characteristics of glazed and polished surface during hydrothermal aging using different types of zirconium dioxide. &#x0D; Materials and Methods. The same standardized samples from different types of zirconium dioxide with a diameter of 10 mm and a height of 1 mm were used for all experiments. These types included Katana (Kuraray Noritake Dental, Tokyo, Japan) HTML (control) UTML, STML, and ZirCAD Prime (Ivoclar Vivadent). Atomic force microscopy (AFM) was used to measure the roughness and to visualize the surface morphology of the samples. The Bruker Innova instrument was used as an atomic force microscope. Image acquisition was carried out in semi-contact mode. The probe sensor was TESP-V2 with a triangular-shaped probe and a tip radius of 7 nm. The scanning area was 50 x 50 μm. Frequency 0.5 Hz. The study of roughness was reduced to the processing of AFM image data. For this purpose, three arbitrary sections were made in the height map channel and the necessary roughness parameters Ra, Rz were calculated using software.&#x0D; Results. There was an increase in roughness during artificial aging of the control group of glazed surface, Prime group of polished surface, and Prime group of glazed surface. There is a decrease of roughness in the process of artificial aging of the control group of glazed surface, for STML and UTML groups of both glazed and polished surface. In the case of samples of group "polishing" it is possible to observe that roughness is formed by traces of abrasive material. From the results of the study of roughness it can be noted that for the samples of the group "Glaze" roughness is formed mainly by traces of brush or other tools used in the process of glaze application&#x0D; Conclusion. Artificial aging affects the roughness of the glazed and polished surface of zirconium dioxide with cubic phase predominance depending on the amount of yttrium content.

A perspective on FAIR quality control in multiplexed imaging data processing.

Multiplexed imaging approaches are getting increasingly adopted for imaging of large tissue areas, yielding big imaging datasets both in terms of the number of samples and the size of image data per sample. The processing and analysis of these datasets is complex owing to frequent technical artifacts and heterogeneous profiles from a high number of stained targets To streamline the analysis of multiplexed images, automated pipelines making use of state-of-the-art algorithms have been developed. In these pipelines, the output quality of one processing step is typically dependent on the output of the previous step and errors from each step, even when they appear minor, can propagate and confound the results. Thus, rigorous quality control (QC) at each of these different steps of the image processing pipeline is of paramount importance both for the proper analysis and interpretation of the analysis results and for ensuring the reusability of the data. Ideally, QC should become an integral and easily retrievable part of the imaging datasets and the analysis process. Yet, limitations of the currently available frameworks make integration of interactive QC difficult for large multiplexed imaging data. Given the increasing size and complexity of multiplexed imaging datasets, we present the different challenges for integrating QC in image analysis pipelines as well as suggest possible solutions that build on top of recent advances in bioimage analysis.

HyperVein: A Hyperspectral Image Dataset for Human Vein Detection.

HyperSpectral Imaging (HSI) plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. HyperSpectral (HS) image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper presents a HS image dataset encompassing left- and right-hand images captured from 100 subjects with varying skin tones. The dataset was annotated using anatomical data to represent vein and non-vein areas within the images. This dataset is utilised to explore the effectiveness of dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward's Linkage Strategy using Mutual Information (WaLuMI) for vein detection. To generate experimental results, the HS image dataset was divided into train and test datasets. Optimum performing parameters for each of the dimensionality reduction techniques in conjunction with the Support Vector Machine (SVM) binary classification were determined using the Training dataset. The performance of the three dimensionality reduction-based vein detection methods was then assessed and compared using the test image dataset. Results show that the FPCA-based method outperforms the other two methods in terms of accuracy. For visualization purposes, the classification prediction image for each technique is post-processed using morphological operators, and results show the significant potential of HS imaging in vein detection.

DEEP LEARNING FOR POLYCYSTIC OVARIAN SYNDROME CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK

Polycystic Ovarian Syndrome (PCOS) is the main cause of infertility in women. This condition results in abnormal hormone levels. Women who experience this syndrome will have irregular hormone levels and experience irregular menstrual cycles as well, thereby affecting the reproductive system. Symptoms that arise as a result of the increase in these hormones can be seen from the growth of hair on the legs, weight gain which results in not being ideal, irregular menstruation, unusual acne growth, and oily skin. The problem of Polycystic Ovarian Syndrome can cause disturbances in ovulation and cause infertility in women. Urgency This research requires a classification that has good accuracy in diagnosing early to minimize the rate of pregnancy failure. The aim of the research is to be able to model early detection of Polycystic Ovarian Syndrome with high accuracy so that it can help the health team in detecting Polycystic Ovarian Syndrome or not having Polycystic Ovarian Syndrome. The research stage has 3 stages including the first stage of identifying problems and collecting datasets from Telkom University dataverse in the form of images and literature reviews of various sources. The second stage is Pre Processing of image data, Data Training, modeling design by managing image data and classifying using the Convolutional Neural Network Algorithm deep learning model and testing. The third stage is evaluating the test results and discussing the results of accuracy in determining the status of Normal Polycystic Ovarian Syndrome or PCOS. The results of training and validation on the ovarian xray image dataset using the CNN architecture that has been made, 40 iterations (epochs), and 4 step_per_epochs show an accuracy value of 0.8947 or 89.47% and a loss value of 0.2684.

An end-to-end workflow for nondestructive 3D pathology.

Recent advances in 3D pathology offer the ability to image orders of magnitude more tissue than conventional pathology methods while also providing a volumetric context that is not achievable with 2D tissue sections, and all without requiring destructive tissue sectioning. Generating high-quality 3D pathology datasets on a consistent basis, however, is not trivial and requires careful attention to a series of details during tissue preparation, imaging and initial data processing, as well as iterative optimization of the entire process. Here, we provide an end-to-end procedure covering all aspects of a 3D pathology workflow (using light-sheet microscopy as an illustrative imaging platform) with sufficient detail to perform well-controlled preclinical and clinical studies. Although 3D pathology is compatible with diverse staining protocols and computationally generated color palettes for visual analysis, this protocol focuses on the use of a fluorescent analog of hematoxylin and eosin, which remains the most common stain used for gold-standard pathological reports. We present our guidelines for a broad range of end users (e.g., biologists, clinical researchers and engineers) in a simple format. The end-to-end workflow requires 3-6 d to complete, bearing in mind that data analysis may take longer.

Detection of acute and early-delayed radiation-induced changes in the white matter of the rat brain based on numerical processing of optical coherence tomography data.

Detection of radiation-induced changes of the brain white matter is important for brain neoplasms repeated surgery. We investigated the influence of irradiation on the scattering properties of the white matter using optical coherence tomography (OCT). Healthy Wistar rats undergone the irradiation of the brain right hemisphere. At seven time points from the irradiation procedure (2-14 weeks), an ex vivo OCT study was performed with subsequent calculation of attenuation coefficient values in the corpus callosum followed by immunohistochemical analysis. As a result, we discovered acute and early-delayed changes characterized by the edema of different severity, accompanied by a statistically significant decrease in attenuation coefficient values. In particular, these changes were found at 2 weeks after irradiation in the irradiated hemisphere, while at 6- and 12-week time points they affected both irradiated and contralateral hemisphere. Thus, radiation-induced changes occurring in white matter during the first 3 months after irradiation can be detected by OCT.

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review.

Environment perception plays a crucial role in autonomous driving technology. However, various factors such as adverse weather conditions and limitations in sensing equipment contribute to low perception accuracy and a restricted field of view. As a result, intelligent connected vehicles (ICVs) are currently only capable of achieving autonomous driving in specific scenarios. This paper conducts an analysis of the current studies on image or point cloud processing and cooperative perception, and summarizes three key aspects: data pre-processing methods, multi-sensor data fusion methods, and vehicle-infrastructure cooperative perception methods. Data pre-processing methods summarize the processing of point cloud data and image data in snow, rain and fog. Multi-sensor data fusion methods analyze the studies on image fusion, point cloud fusion and image-point cloud fusion. Because communication channel resources are limited, the vehicle-infrastructure cooperative perception methods discuss the fusion and sharing strategies for cooperative perception information to expand the range of perception for ICVs and achieve an optimal distribution of perception information. Finally, according to the analysis of the existing studies, the paper proposes future research directions for cooperative perception in adverse weather conditions.

A novel blockchain and fractional DCT based image storage and retrieval framework

Securing medical images are a challenging task for researchers as they are prone to cyber- attacks. A potential solution to this problem can be blockchain, which provide secure and unchangeable storage. Fractional Discrete Cosine Transform (frDCT) has the capability to minimizes the amount of data necessary for expressing an image in a secure way. This paper presents a novel framework for compressing as well as securely storing and retrieving medical images by extraction of feature maps from medical images by frDCT, followed by encoding and storing the feature map on decentralized cloud and linking them on blockchain using fractional angle α. The proposed framework provides numerous benefits such as improved data sharing and collaboration, enhanced security, compression as well as efficient retrieval and processing of medical image data which in turn decreases the computational energy requirement providing sustainability. The performance of proposed framework has been evaluated by employing image quality metric such as MSE, PSNR, SSIM and multi-SSIM by comparing it with correct and incorrect α values on six different values of frDCT parameter α.

Comparison between vascular Doppler ultrasound and contrast exams in chronic peripheral arterial disease.

Vascular Doppler ultrasound (DUS) has evolved over recent years because of improvements in the technology involved in the acquisition and processing of sound and image data. The method is an excellent option for use in diagnosis of peripheral arterial disease considering its availability, low cost, and absence of harmful effects. The breakdown of logistics supply chains caused by the COVID-19 pandemic caused worldwide shortages of iodinated contrast, highlighting the need to validate alternative diagnostic methods. To use DUS for decision-making when choosing between by-pass and endovascular surgery for femoropopliteal arterial disease and compare the results to those of iodinated contrast exams. We compared DUS with examinations using contrast for identification of stenoses/occlusions and indication of surgical treatment (by-pass vs. endovascular). In the first phase of the study the results were merely compared, DUS vs. angiotomography. Then, in the second phase, the vascular ultrasound results were used for screening between by-pass and endovascular treatment, comparing DUS with angiotomography in cases scheduled for by-pass and with arteriography in endovascular patients. In phase 1, the sensitivity of DUS compared to CT angiography was 100% for the SFA territory. When considering solely the choice of bypass vs. endovascular treatment, the results showed 100% agreement for phase 1 and 94% for phase 2. Notwithstanding the sample size, the study fulfilled its objective of demonstrating the reliability of DUS for indicating the treatment choice between by-pass and endovascular surgery.

Intelligent Data Processing and Mining of Histopathological Images using Improved Tunicate Swarm Algorithm with Deep Learning

Intelligent Data Processing and Mining of Histopathological Images using Improved Tunicate Swarm Algorithm with Deep Learning

Panta Rhei: A software platform for acquisition and processing of image and spectral data

Panta Rhei: A software platform for acquisition and processing of image and spectral data

Identifying Chromosome Movement Patterns During Meiosis Using ChroMo.

During meiosis, transient associations between the nuclear envelope and telomeres transmit nuclear movements to chromosomes, enabling their pairing and recombination. Recent advances in the field of quantitative cell biology allow a large volume of information about the kinetics of these chromosome movements to be extracted and analyzed with the aim of identifying biologically relevant movement patterns. To this end, we have developed ChroMo, a freely available application for the unsupervised study of chromosome movements in fission yeast meiosis. ChroMo contains a set of time series algorithms to identify chromosome movement motifs that are not easily observable by direct human visualization and to establish causal relationships between phenotypes. In this chapter, we present a detailed protocol for the processing of raw live imaging data from fission yeast and its subsequent analysis in ChroMo.

The positive effects of digital media design combined with AI intelligence on art brand planning and promotion

Abstract With the development of science and technology, digital media and AI intelligence are currently spreading to the traditional brand planning and promotion business, and more and more brands will adopt an empowered brand promotion business in the future. This paper is based on natural language processing and other AI intelligent technology, which includes image, copy, comment, and audio data processing for advertising promotion intelligent creation systems. Combined with the characteristics of art brand planning and promotion, based on the principle of comprehensiveness and importance, it constructs a system of factors influencing digital media and AI intelligence on brand promotion from the four dimensions of brand product, brand capability, brand market, and brand support. The results show that the average purchase intention index of the brand promotion mode integrating digital media and AI intelligence is 4.9710, which is significantly higher than that of the traditional promotion mode, which is 4.1354. This study can help art enterprises identify the relationship and key elements between the influencing factors of brand promotion and guide brand enterprises to implement more targeted brand planning and promotion strategies.

Image-based investigation of lip aging features in a large number of Korean women.

The lips play a significant role in shaping facial aesthetics. Due to the distinct attributes of lips in contrast to other facial skin, a unique approach is imperative for managing lip aging. We analyzed lip characteristics (morphology, wrinkles, and color) to investigate visual changes and distinctive attributes of aging lips. By utilizing image data processing methods, including facial landmark detection, pattern recognition, and color quantification, we extracted 11 lip characteristic indices (four morphological indices, four wrinkle indices, and three color indices) from high-resolution images of 1000 Korean women aged 20-69. Correlation tests were conducted to assess the relationship between lip characteristic indices and age, and also between lip morphological and wrinkle indices. Lip height significantly decreased, while lip width and lip ratio (lip width divided by the sum of the upper and lower lip height) significantly increased with aging. Lip wrinkles significantly increased with aging, whereas lip colors (redness and yellowness) decreased. The lip wrinkle indices, which are segmented for the first time in this study, exhibited significant correlations with lip width, and three of them additionally were correlated with lip ratio (p<0.05). The results imply such morphological changes can be associated with wrinkle formation of human lips. The indices suggested in this study can be used for assessing lip aging characteristics, and the study results can contribute to deeper understanding of lip aging.

The Use of EVI Algorithm in Predicting Rice Productivity, Harau District, Lima Puluh Kota Regency, West Sumatra

Using remote sensing data in predicting rice productivity can provide information quickly and easily for relatively large areas. Harau District is a rice center in Lima Puluh Kota Regency and is one of the many rice-producing areas in West Sumatra Province. This study aims to determine a mathematical model to estimate rice productivity in Harau District, Lima Puluh Kota Regency, using the EVI (Enhanced Vegetation Index) algorithm. This study used two methods, field observation and MODIS image data processing (MOD13A1 v006). The results of the analysis obtained a mathematical model to predict rice productivity in Harau District, namely y = 5393.3x4 - 8140.7x3 + 4518.9x2 - 1121.6x + 113.55 where y is the productivity value (tons/ha) and x as the EVI value. The percentage of errors in observation and estimation data was 15.284%, and the results of the observation and estimation data validation test (NSE) were 0.734, a good category.

Distribusi Spasial Lamun di Perairan Sekotong Barat-TWP Gita Nada Lombok Barat Menggunakan Citra Landsat 8 OLI

West Sekotong Village is one of the coastal water areas included in the Gita Nada Aquatic Tourism Park (TWP) area which has a seagrass habitat. Seagrass beds in West Sekotong are threatened due to increased tourism and community activities. This research aims to determine the distribution and extent of seagrass beds on the coast of West Sekotong-TWP Gita Nada. This research uses a remote sensing approach through interpretation of Landsat 8 OLI imagery. This research consists of 2 steps, namely image data processing and supporting data. Image data processing includes several stages, namely image data collection, geometric correction, radiometric correction, image cutting, image composite, image data sharpening, image data interpretation, accuracy testing, area measurement, and final layout. Supporting data was obtained by searching related literature, interviews and surveys of activities that have the potential to have an impact on seagrass ecosystems. The research results show that the distribution of seagrass in West Sekotong-TWP Gita Nada covers the coast of the large island (mainland) and the coast of small islands. The distribution of seagrass on the mainland is found on the coasts of Medang, Tanjung Kelor, Batu Kijuk, Tawun, Labu, Kelapa, Pandanan, Gili Genting, Labuan Petung, Temeran, and Gawah Pudak. In the coastal areas of small islands, seagrass is found on Gili Sudak, Gili Tangkong, Gili Nanggu, Gili Poh, and Gili Lontar. The total area of seagrass beds in West Sekotong is around 144.68 ha. On the mainland coast the area is 118.96 ha (82.2%) and on small islands the area is around 25.72 ha (17.8%). The distribution of seagrass in West Sekotong is influenced by many factors, among which those observed in this research are the type of substrate, the presence of river estuaries, and the use of land and coastal waters.

Prediction of Skin Diseases using Convolutional Neural Networks as an Effort to Prevent Their Spread in Islamic Boarding School Environments

Skin disease is a common health problem in Islamic boarding school environments. This disease can spread quickly among students due to close contact and sharing the same facilities. Preventing the spread of skin diseases is a top priority in efforts to maintain the health and welfare of students in Islamic boarding schools. In this research, we propose the use of machine learning techniques to predict skin diseases in Islamic boarding school students. The main goal of this research is to develop a predictive model that can help identify skin diseases quickly and accurately. It is hoped that this will enable the prevention of the spread of skin diseases in the Islamic boarding school environment. The method used in this research involves the following steps: skin disease image data collection, data processing and cleaning, feature extraction from patient data, and machine learning model training and evaluation. We will use a Convolutional Neural Network (CNN) machine learning algorithm to build a predictive model. The dataset used in this research consists of images of melanoma, acne and acne skin diseases. In addition, validation will be carried out using data that has never been seen before to test the performance of the predictive model. It is hoped that the results of this research can make a significant contribution in preventing the spread of skin diseases in the Islamic boarding school environment. With accurate predictive models, health workers in Islamic boarding schools can take appropriate preventive measures to control skin diseases effectively. Apart from that, this research can also be a basis for developing a health information system that supports preventive measures for skin diseases in Islamic boarding schools more widely.