Scale Image Research Articles

A Study on Image Interpretations of Computed Radiography for Iron Samples Using Image Evaluation Techniques

In the non-destructive radiographic testing methods used for specimens with cultural heritage, analog film radiography is being replaced with digital radiography by the introduction of an imaging plate computed radiography system. Adoption of the computed radiography system has helped standardize digital image quality.</br>This study analyzed the X-ray transmission characteristics and evaluated image quality according to image developing time for iron, the most important metal in cultural heritage. Computed radiography images were acquired. The transmittance of the images was determined based on Gray Scale and Duplex-wire Image Quality Indicator data. In addition, the change in images with the image developing time was compared and analyzed using the Structural Similarity Index Measure, mean square error, and peak signal-to-noise ratio.

THE CONCEPTUAL METAPHOR IN KOREAN POEM 'INDONESIA INMIN-EGE JUNEUN SI [인도네시아]' BY PARK INHWAN: COGNITIVE SEMANTIC ANALYSIS

This study aims to find out the metaphors in the poem entitled ‘Puisi buat Rakyat Indonesia' / 'Indonesia Inmin-ege Juneun Si [인도네시아 주는 시] by Park Inhwan. The method of this research was descriptive qualitative. The theory used was the cognitive semantics of conceptual metaphor according to Lakoff and Johnson (2013); image schema according to Cruse and Croft (2004). The results show that the types of metaphors are divided into 12 structural metaphors with 2 conceptual meanings of stolen and extermination; 1 conceptual meaning contains severe colonialism, natural wealth, stolen, bad condition, owned, independence, colonize, slump, and feels beautiful; 4 orientational metaphors with conceptual meaning of being independent country, colonized, oppressed, and lost in the war; and 12 ontological metaphors with conceptual meanings of tortured, loss of hope for the future, surrender, religion, tactics, independence, joint attacks, blaze of spirit, torture, hereditary, taking many victims, and victory. The schematic images are force subtheme compulsion, counterforce, restraint, and enablement; the unity image scheme subtheme part & whole and merging; existence image schema subtheme object, process, and cycle; space image scheme, up & down; scale image schema subtheme paths; the identity image scheme subtheme matching.

Hyperspectral image classification based on combining texture and multiscale guided filter feature of principal component

Combining spectral and spatial features can increase the land-cover classification accuracy of hyperspectral images (HSI), but improvements are still needed to overcome problems remain in the extraction and use of spatial features, such as fixed extraction scale, loss of homogeneous region boundary and texture detail. Therefore, a novel classification frame is constructed herein. First, the first principal component of a Karhunen–Loeve transform of the HSI is taken as the guidance image to extract guide filter features of the other selected principal components under different scales and the appropriate multiscale range is automatically determined by calculating the interclass distance and intraclass distance based on the training samples. Second, the texture features, extracted by a local binary pattern, are used to emphasize the spatial information. Finally, logistic regression via variable splitting and augmented Lagrangian is used to obtain class labels from each scale image feature, and the majority voting algorithm is used to obtain the last class label of each pixel. Two standard HSI datasets, Indian Pines and Pavia University, are used to test this frame, and methods based on spectrum or spatial filtering (Type I) and methods combining spectral and spatial features (Type II) are used for comparison. The overall accuracy (OA) of the proposed frame is on average 13.76% and 4.77% higher than that of Type I and Type II, respectively, when 30 pixels per class are used as training samples. One dataset with a spatial resolution of 0.8 m, obtained by Panchromatic multispectral imaging spectrometer, is used for comparison with Type I, resulting in an average increase in OA of 7.05%, when 30 pixels per class are used as training samples. These results indicate the validity of the proposed frame.

Fast sparse flow field prediction around airfoils via multi-head perceptron based deep learning architecture

In order to obtain the information about flow field, traditional computational fluid dynamics methods need to solve the Navier-Stokes equations on the mesh with boundary conditions, which is a time-consuming task. In this work, a data-driven method based on convolutional neural network and multi-head perceptron is used to predict the incompressible laminar steady sparse flow field around the airfoils. Firstly, we use convolutional neural network to extract the geometry parameters of the airfoil from the input gray scale image. Secondly, the extracted geometric parameters together with Reynolds number, angle of attack and flow field coordinates are used as the input of the multi-layer perceptron and the multi-head perceptron. The proposed multi-head neural network architecture can predict the aerodynamic coefficients of the airfoil in seconds. Furthermore, the experimental results show that for sparse flow field, multi-head perceptron can achieve better prediction results than multi-layer perceptron.

Examination of OH and H2O2 production by uniform and non-uniform modes of dielectric barrier discharge in He/air mixture

In this work we have carried out a parametrical study of hydroxyl radical (OH) generation in nanosecond dielectric barrier discharge (DBD) in He/air mixture using a laser-induced fluorescence approach. The foci of the study are the investigation of differences between uniform and non-uniform modes of the discharges and the difference in production of OH and H2O2 Nanosecond-time scale imaging of the discharge shows transition from streamer to diffuse mode when applied electric field to the discharge gap approaches ∼90 kV cm−1. The results show that both OH production in the gas phase and downstream H2O2 delivery rates to liquid depend on the discharge mode operation and are respectively 30% and 3 times higher for the non-uniform DBD compared to the diffuse discharge.

CULTURAL, ARCHITECTURAL, ‘SENSE OF PLACE’ AND SUFISTIC BELIEFS IN MOSQUE TOURISM CASE STUDY: MASJID AR-RAHMAN, PULAU GAJAH, KELANTAN

The mosque is a sacred important religious symbol for bringing Muslims together as demonstrated during the time of Prophet Muhammad (PBUH). This paper investigates the potential of Masjid Ar-Rahman of Pulau Gajah, Kelantan as a spot for mosque tourism. Although this mosque was constructed in 2016, it has demonstrated simplicity in its scale and traditional image. It has the value of hybrid assimilation or Hindu-Buddhist syncretism and tolerance and coined as the Nusantara hybridized Malay and Javanese architectural styles. The typology of this humble Malay Mosque architecture is of medium-scale and reflects the Sufistic contextual value beliefs encouraging religious and architectural tourism alike. The conceptual framework capitalises the research gap found in mosque cultural, architectural and Sufistic beliefs research by further delving into constructing the ‘Sense of Place’ in relation to the ‘Sacred Places’. This research employs qualitative methods of interviewing visitors, applying phenomenological and case study approaches supported by architectural documentation in emphasising the symbolic and semiotic aesthetics aspects in constructing the ‘Sense of Place’ bonded by the Sufistic symbolic aesthetics. The theory is constructed in the deeply rooted Islamic Mosque architecture via Sufistic beliefs that provides platform for the mosque tourism activities.

The Reliability and Validity Study of the Eating-related Intrusive Thoughts Inventory (INPIAS)

Intrusive thoughts (ITs) have been addressed with a transdiagnostic conceptualization recently. The Eating-related Intrusive Thoughts Inventory (INPIAS), composed of eating-related ITs (Part 1), consequences of interference (Part 2A) and control strategies (Part 2B) of ITs, was developed to examine ITs in Eating Disorders (EDs). The Turkish adaptation of INPIAS via reliability and validity analyses was conducted in this study. The sample consisted of 339 university students. INPIAS and Eating Disorder Examination Scale and Body Image Scale (for concurrent validity), Obsessive-Compulsive Inventory-Revised Form, Beck Anxiety Scale and Beck Depression Scale (for convergent validity) and the Demographics Form were used. Results revealed that the five-factor solution from the exploratory factor analysis (Part 1) and the original factor structures (Part 2A and Part 2B) showed a better fit. INPIAS had positive correlations with other scales. The reliability scores revealed Cronbach alpha values of .98 (Part 1), .92 (Part 2A), and .89 (Part 2B) and test-retest correlations of .98 (Part 1), .93 (Part 2A), and .92 (Part 2B). So, INPIAS may be utilized to examine ITs related to eating, diet, body image, exercise, and related control strategies. INPIAS is assumed to enlighten future studies about the relationship between ITs and EDs.

Plantlia: free android app for measurement of foliar area and color with automated scaling

There is a plethora of apps available for different biological applications, however, mobile software for plant measurement, specifically for field conditions are limited. Additionally, there is a need to create large training datasets for machine learning applications. A free app called Plantlia, which is available to download from the Google Play Store, aims to resolve this with an intuitive interface, and a method to automatically scale images using homography. Plantlia also includes methods to share results from either direct measurements or thresholded images. This paper aims to describe some of the functions of Plantlia, as well as show scenarios that display its performance. Images from cellphones and drone pictures were used for validation on different devices. This is important, as it means that users with low-cost equipment, like drones with no GPS information, can still analyze localized field information. Similarly, researchers can do communal efforts to share and receive in-field data to create machine learning datasets.

Top Surgery and Chest Dysphoria Among Transmasculine and Nonbinary Adolescents and Young Adults

Transgender and nonbinary (TGNB) adolescents and young adults (AYA) designated female at birth (DFAB) experience chest dysphoria, which is associated with depression and anxiety. Top surgery may be performed to treat chest dysphoria. To determine whether top surgery improves chest dysphoria, gender congruence, and body image in TGNB DFAB AYA. This is a nonrandomized prospective cohort study of patients who underwent top surgery between December 2019 and April 2021 and a matched control group who did not receive surgery. Patients completed outcomes measures preoperatively and 3 months postoperatively. This study took place across 3 institutions in a single, large metropolitan city. Patients aged 13 to 24 years who presented for gender-affirming top surgery were recruited into the treatment arm. Patients in the treatment arm were matched with individuals in the control arm based on age and duration of testosterone therapy. Patients in the surgical cohort underwent gender-affirming mastectomy; surgical technique was at the discretion of the surgeon. Patient-reported outcomes were collected at enrollment and 3 months postoperatively or 3 months postbaseline for the control cohort. The primary outcome was the Chest Dysphoria Measure (CDM). Secondary outcomes included the Transgender Congruence Scale (TCS) and Body Image Scale (BIS). Baseline demographic and surgical variables were collected, and descriptive statistics were calculated. Inverse probability of treatment weighting (IPTW) was used to estimate the association of top surgery with outcomes. Probability of treatment was estimated using gradient-boosted machines with the following covariates: baseline outcome score, age, gender identity, race, ethnicity, insurance type, body mass index, testosterone use duration, chest binding, and parental support. Overall, 81 patients were enrolled (mean [SD] age, 18.6 [2.7] years); 11 were lost to follow-up. Thirty-six surgical patients and 34 matched control patients completed the outcomes measures. Weighted absolute standardized mean differences were acceptable between groups with respect to body mass index, but were not comparable with respect to the remaining demographic variables baseline outcome measures. Surgical complications were minimal. IPTW analyses suggest an association between surgery and substantial improvements in CDM (-25.58 points; 95% CI, -29.18 to -21.98), TCS (7.78 points; 95% CI, 6.06-9.50), and BIS (-7.20 points; 95% CI, -11.68 to -2.72) scores. Top surgery in TGNB DFAB AYA is associated with low complication rates. Top surgery is associated with improved chest dysphoria, gender congruence, and body image satisfaction in this age group.

An effective and efficient broad-based ensemble learning model for moderate-large scale image recognition

An effective and efficient broad-based ensemble learning model for moderate-large scale image recognition

Minimally Invasive Polarization Sensitive Optical Coherence Tomography (PS-OCT) for assessing Pre-OA, a pilot study on technical feasibility

ObjectiveEfforts to develop chondroprotective approaches to halt osteoarthritis (OA) progression have recently increased. Current imaging techniques are critical in managing advanced OA, but greater resolution is needed to identify reversible stages (pre-OA). Optical coherence tomography (OCT) is a micron scale imaging technology widely used in ophthalmology, cardiology, and neurology. We previously demonstrated that polarization sensitive OCT (PS-OCT) can identify pre-OA in vitro, in animals, and in open surgical fields. This feasibility study examines performing intraarticular PS-OCT using a flexible endocatheter introduced through a stiff 18-gauge spinal needle. Results are critical for designing larger clinical trials examining minimally invasive PS-OCT's ability to identify pre-OA. DesignFifteen patients undergoing arthroscopic partial medial meniscectomy were selected to confirm their risk for rapid progression to OA. Magnetic resonance imaging (MRI) was obtained at time 0 and at 2.5 years to determine if significant OA developed over this short period and for correlation with time zero PS-OCT results. ResultsOver half of the patients developed frank OA by 2.65 ​± ​0.28 years. All cartilage surfaces were successfully imaged by PS-OCT, but endocatheter redesign is needed. Normal to severely abnormal areas by PS-OCT (all normal by MRI) were successfully identified. PS-OCT assessments were promising for predicting OA progression (p ​< ​0.008). However, the study's low power prevented definite conclusions regarding predictive value. ConclusionsThis pilot study produced at least two outcomes critical for future larger trial designs: medial meniscectomy patients are well-suited for studying PS-OCT's ability to predict future OA and substantial endocatheter redesign is needed.

From Distortion Manifold to Perceptual Quality: a Data Efficient Blind Image Quality Assessment Approach

Though current no-reference image quality assessment (NR-IQA) approaches have achieved impressive performance gain thanks to deep learning techniques, it is claimed that the risk of over-fitting exists. To improve model generalization ability, most of the current researches incorporate mass data to train or tune the data-driven models. However, the process of image data collection and quality label annotation is quite time-consuming and labour-intensive. Therefore, in this paper, we explore an alternative solution to promote model generalizability but with relatively small fractions of training data. Compared with previous approaches which make effort to approximate the whole complex image distribution, we propose to explicitly learn an image distortion manifold first, which lies in a much lower dimension space and also representative in capturing general degradation patterns. We then project the images to their perceived quality from the learned manifold to obtain quality predictions. Since the manifold embeds general distortion features despite of varying image contents, it can be learned with relatively small amount of samples. In order to learn the manifold and quality projection, we introduce a two-branched network to learn both low level distortions and high level semantics. We also propose a simple but efficient training framework, composing of a masked labelling strategy and a gradual weighting curriculum to fulfill the task. Thanks to the learned distortion manifold, the proposed model achieves superior generalizability compared with previous models. Extensive experiments demonstrate its effectiveness in terms of training with limited data, testing on large scale images, and with unseen types of distorted images.

Materials property mapping from atomic scale imaging via machine learning based sub-pixel processing

Direct visualization of the atomic structure in scanning transmission electron microscopy has led to a comprehensive understanding of the structure-property relationship. However, a reliable characterization of the structural transition on a picometric scale is still challenging because of the limited spatial resolution and noise. Here, we demonstrate that the primary segmentation of atomic signals from background, succeeded by a denoising process, enables structural analysis in a sub-pixel accuracy. Poisson noise is eliminated using the block matching and three-dimensional filtering with Anscombe transformation, and remnant noise is removed via morphological filtering, which results in an increase of peak signal-to-noise ratio from 7 to 11 dB. Extracting the centroids of atomic columns segmented via K-means clustering, an unsupervised method for robust thresholding, achieves an average error of less than 0.7 pixel, which corresponds to 4.6 pm. This study will contribute to a profound understanding of the local structural dynamics in crystal structures.

Particle detection by means of neural networks and synthetic training data refinement in defocusing particle tracking velocimetry

The presented work addresses the problem of particle detection with neural networks (NNs) in defocusing particle tracking velocimetry. A novel approach based on synthetic training data refinement is introduced, with the scope of revising the well documented performance gap of synthetically trained NNs, applied to experimental recordings. In particular, synthetic particle image (PI) data is enriched with image features from the experimental recordings by means of deep learning through an unsupervised image-to-image translation. It is demonstrated that this refined synthetic training data enables the neural-network-based particle detection for a simultaneous increase in detection rate and reduction in the rate of false positives, beyond the capability of conventional detection algorithms. The potential for an increased accuracy in particle detection is revealed with NNs that utilise small scale image features, which further underlines the importance of representative training data. In addition, it is demonstrated that NNs are able to resolve overlapping PIs with a higher reliability and accuracy in comparison to conventional algorithms, suggesting the possibility of an increased seeding density in real experiments. A further finding is the robustness of NNs to inhomogeneous background illumination and aberration of the images, which opens up defocusing PTV for a wider range of possible applications. The successful application of synthetic training-data refinement advances the neural-network-based particle detection towards real world applicability and suggests the potential of a further performance gain from more suitable training data.

The efficacy and safety of Jin's three-needle therapy vs. placebo acupuncture on anxiety symptoms in patients with post-stroke anxiety: A study protocol for a randomized controlled trial.

BackgroundA large number of clinical RCTs have verified that Jin's three-needle therapy (JTNT) has a great contribution to promoting the function of paralyzed limbs and relieving anxiety disorders for patients with post-stroke anxiety (PSA). However, there is still a lack of sham needle control, and its placebo effect cannot be ruled out. This study firstly verifies the real effectiveness of JTNT. Besides, the changes in serum indexes on the hypothalamic-pituitary-adrenal axis (HPA axis) are observed dynamically by the Enzyme-Linked ImmunoSorbent Assay (ELISA). The activation of different brain regions by JTNT is recorded using resting functional magnetic resonance imaging (rs-fMRI). Therefore, we can provide more practical and powerful evidence-based medical evidence for clinical decisions.MethodThis is a 16 week parallel, single-blind, random, controlled trial, including baseline, 4 weeks of treatment, and 12 weeks of follow-up. A total of 114 participants will be randomly divided into three groups in the proportion of 1:1:1. Participants will receive Jin's three-needle therapy in the active acupuncture group and accept sham needle treatment in the sham acupuncture group. In the waitlist control group, patients will not receive any acupuncture treatment. Outcomes cover three types of indicators, including scale indicators, serum indicators, and imaging indicators. The primary outcome is the change in the performance of anxiety symptoms, which is estimated by the 14-item Hamilton Anxiety Rating Scale (HAMA-14) and the 7-item Generalized Anxiety Disorder scale (GAD-7). Secondary outcomes are physical recovery and daily quality of life, which are evaluated by the National Institute of Health stroke scale (NIHSS) and the Modified Barthel Index Score (MBI Scale). Therefore, the assessment of the scale is carried out at baseline, 2nd, 4th, 8, 12, and 16 weeks. Adrenocorticotropin and cortisol will be quantitatively detected by ELISA at baseline and 4 weeks after treatment. In addition, regional homogeneity analysis (ReHo) will be used to record the activity of brain regions at baseline and 4 weeks after intervention.DiscussionThe study aims to provide high-quality clinical evidence on the effectiveness and safety of JTNT for patients with PSA. In addition, this trial explores a possible mechanism of JTNT for patients with PSA.Clinical trial registrationChinese Clinical Trial Registry, identifier [ChiCTR2200058992].

Deep Learning Approach for Event Monitoring System

With an increasing number of extreme events and complexity, more alarms are being used to monitor control rooms. Operators in the control rooms need to monitor and analyze these alarms to take suitable actions to ensure the system’s stability and security. Security is the biggest concern in the modern world. It is important to have a rigid surveillance that should guarantee protection from any sought of hazard. Considering security, Closed Circuit TV (CCTV) cameras are being utilized for reconnaissance, but these CCTV cameras require a person for supervision. As a human being, there can be a possibility to be tired off in supervision at any point of time. So, we need a system to detect automatically. Thus, we came up with a solution using YOLO V5. We have taken a data set and used robo-flow framework to enhance the existing images into numerous variations where it will create a copy of grey scale image, a copy of its rotation and a copy of its blurred version which will be used to get an enlarged data set. This work mainly focuses on providing a secure environment using CCTV live footage as a source to detect the weapons. Using YOLO algorithm, it divides an image from the video into grid system and each grid detects an object within itself.

A New Block Based Non-Blind Hybrid Color Image Watermarking Approach Using Lifting Scheme and Chaotic Encryption Based on Arnold Cat Map

Online platforms became preferred mode of communication due to advancement in communication technology. Sharing of digital documents over online communication medium grown exponentially and thus demanded a secure, robust and transparent watermarking technique for authenticity of digital media and copyright protection. This research study proposes a robust and secure non-blind SVD-LWT watermarking technique. Color images are employed instead of gray scale images and Y channel of YCbCr color model is utilized to embed secret digital information. The selected color model is in accordance with the human visual system and Y channel is ideal for data hiding. Two level LWT, SVD is used and diagonal matrix of Y channel of host (cover) and watermark image along with scaling factor (α) is used to embed digital data. Block based and chaotic image encryption transform are used for image scrambling. The performances of presented watermarking scheme evaluated with the aid of fidelity parameters namely MSE, PSNR, SSIM and NCC.

775 Role of Inversion Imaging in the Diagnoses of Neck of Femur Fractures

Abstract Aim The objective of this study was to assess whether inversion of grey scale radiograph images has any impact on the diagnostic sensitivity and inter-observer reliability in diagnosing neck of femur fractures. Method We performed a retrospective, single centre study, using 50 randomly selected AP pelvis radiographs. The images included a combination of normal, intracapsular and extracapsular neck of femur fractures, which had been confirmed on CT, MRI and/or subsequent surgery. Four independent observers (two orthopaedic specialists, one registrar and one SHO in orthopaedics) reviewed the images and graded each radiograph image using the Likert scale in response to the statement “there is a fracture”. Following this, the same radiographs were inverted to grey scale images and sent for a second review using the same method. RAND correlation was used for statistical analysis. Results Overall, observers appeared to have similar accuracy and equivocal rates with normal and inversion radiographic imaging. One of the T&amp;O Consultant's had much higher equivocal rate when analysing inverted radiographs compared to conventional digital radiographs but had slightly higher accuracy with inversion (95.35% compared to 93.88%) as well as the Registrar (83.33% compared to 81.63%). The second Trauma and Orthopaedics Consultant had slightly more accuracy with conventional digital radiographs (89.58% compared with 87.50%). Conclusions Inversion of digital radiographs did not affect the diagnostic sensitivity of neck of femur fractures in our study, and similarly did to not have a statistically significant impact in clinician confidence in diagnosing neck of femur fractures.

Early Detection of White Matter Changes with Cognitive Decline in Parkinson's Patients

Objective: The aim of this study was to detect changes in white matter in patients with Parkinson's disease applied by diffusion tensor imaging to predict cognitive impairment.&#x0D; Methods: Montreal cognitive assessment was applied to 50 Parkinson's disease patients to confirm cognitive decline (M: F = 41:9; age: 62.72±9.07 years) and to 20 Parkinson's disease patients with no cognitive impairment as a control (M: F =13:7; age 58.95±11.22). All patients underwent disease severity testing by using Modified Hoehn and Yahr Scale, Unified Parkinson disease rating scale and Diffusion tensor imaging (DTI) for the corpus callosum and cingulum including their involved parts to define affected tracts.&#x0D; Results: In PD with cognitive impairment subjects, the cognitive affection correlated with abnormal DTI parameters of the corpus callosum and cingulum. There were FA or MD differences in both the corpus callosum and cingulum pathways. These findings were independent of age, sex and total white matter volume.&#x0D; Conclusion: Patients with Parkinson's disease associated with cognition decline are detected by tractography changes of the corpus callosum and cingulum.

Treatment of knee cartilage by cultured stem cells and three dimensional scaffold: a phase I/IIa clinical trial.

Damage of the knee cartilage is a common condition manifesting itself mainly by pain and/or swelling that may substantially reduce the quality of life while ultimately leading to osteoarthritis in affected patients. Here, we aimed to evaluate the safety and efficacy of cultured autologous bone marrow mesenchymal stem cells (BM-MSCs) attached to the 3D Chondrotissue® scaffold by autologous blood plasma coagulation (BiCure® ortho MSCp) in the treatment of knee cartilage defects. The primary endpoint of this phase I/IIa clinical trial was to evaluate the safety of the treatment. The secondary objective was to determine the short-to-medium-term therapeutic outcomes by standardized scoring questionnaires including Lysholm Knee Scoring Scale (Lysholm score), Knee Injury and Osteoarthritis Outcome Score (KOOS), and pain Visual Analogue Scale (VAS) systems and imaging (X-ray and magnetic resonance imaging, MRI). A total of six patients were included and followed for 12months after the surgery. BiCure® ortho MSCp was well tolerated with no adverse events associated with the investigational medicinal product. Significant improvements were observed in Lysholm scores and KOOS while X-ray showed no deterioration of the arthritis and MRI revealed a persistent filling of the chondral defects by the implant. Overall, our data demonstrate the safety of the tested investigational medicinal product. The function of the treated knee improved within oneyear after surgery in all enrolled patients. EudraCT No.: 2018-004,067-31; October 18 2018.