Image Applications Research Articles

Utility of tele-guidance for point-of-care ultrasound: a single center prospective diagnostic study.

Point-of-care-ultrasound (POCUS) improves diagnostic accuracy and expedites lifesaving procedures. Remote areas disproportionately underuse ultrasound (US) due to a dearth of US trained professionals, imaging tools, and lack of quality assurance. Handheld US probes have been approved for diagnostic imaging but there have been limited studies examining their tele-guidance features. This study investigates whether physician tele-guidance improved ultrasound image acquisition by untrained scanners. To determine the effectiveness of tele-guidance for ultrasound image acquisition, 30 participants (15 in each study arm: experimental and control) with no ultrasound or medical experience gathered images of the heart, right kidney, and gallbladder of a standardized patient using a handheld ultrasound probe (Butterfly iQ +). All participants watched a standardized ultrasound tutorial video and were randomized into the control or experimental group. A physician assisted the experimental group using ultrasound probe's tele-guidance feature while the control group received no assistance. Time to image acquisition was recorded for both groups, and the images were graded by 3 blinded radiologists using the RACE tool to determine image and diagnostic quality. There was evidence that mean imaging time was greater in the control group for the heart, right kidney, and gallbladder (p < 0.0001, all; Cohen's DL: 2.0, 1.7, 3.0, respectively). Similarly, there was evidence that the predicted mean image quality for the heart, right kidney, and gallbladder was higher for the experimental group compared to the control group (3.46 versus 1.86, 4.49 versus 1.58, and 3.93 versus 1.5, respectively; p < 0.0001, all). There was also evidence that the diagnostic quality of images had a statistically higher predicted probability of meaningful interpretation for the experimental group for pericardial fluid, intraperitoneal fluid, and acute cholecystitis (p = 0.003, p < 0.0001, p < 0.0001, respectively). Tele-guidance improves time to image acquisition and clinical applicability of ultrasound images obtained by untrained scanners.

Social Media Image and Computer Vision Method Application in Landscape Studies: A Systematic Literature Review

This study systematically reviews 55 landscape studies that use computer vision methods to interpret social media images and summarizes their spatiotemporal distribution, research themes, method trends, platform and data selection, and limitations. The results reveal that in the past six years, social media–based landscape studies, which were in an exploratory period, entered a refined and diversified phase of automatic visual analysis of images due to the rapid development of machine learning. The efficient processing of large samples of crowdsourced images while accurately interpreting image content with the help of text content and metadata will be the main topic in the next stage of research. Finally, this study proposes a development framework based on existing gaps in four aspects, namely image data, social media platforms, computer vision methods, and ethics, to provide a reference for future research.

Algebraic method for LU decomposition in commutative quaternion based on semi‐tensor product of matrices and application to strict image authentication

As a kind of commutative and associative four‐dimensional algebra, commutative quaternion has better applications in color image and signal processing than quaternion. Matrix decomposition is of great concern in the theoretical study and numerical calculation of commutative quaternion. Two kinds of disadvantages of commutative quaternion make the decomposition of commutative quaternion matrix extremely difficult. On one hand, commutative quaternion is not a kind of complete four dimensional division algebra because of the zero divisors. On the other hand, computing the inverse of commutative quaternion is very complicated. In this paper, the semi‐tensor product (STP) of matrices will be used to overcome the above two kinds of shortcomings. And we will propose a real structure‐preserving algorithm based on STP of matrices for commutative quaternion LU decomposition, which makes full use of high‐level operations, relation of operations between commutative quaternion matrices and their ‐representation matrices. Numerical experiments will be provided to demonstrate the efficiency of the real structure‐preserving algorithm based on STP of matrices. Meanwhile, we will apply the structure‐preserving algorithm for strict image authentication.

Multi-Branch Spectral Channel Attention Network for Breast Cancer Histopathology Image Classification

Deep-learning-based breast cancer image diagnosis is currently a prominent and growingly popular area of research. Existing convolutional-neural-network-related methods mainly capture breast cancer image features based on spatial domain characteristics for classification. However, according to digital signal processing theory, texture images usually contain repeated patterns and structures, which appear as intense energy at specific frequencies in the frequency domain. Motivated by this, we make an attempt to explore a breast cancer histopathology classification application in the frequency domain and further propose a novel multi-branch spectral channel attention network, i.e., the MbsCANet. It expands the interaction of frequency domain attention mechanisms from a multi-branch perspective via combining the lowest frequency features with selected high frequency information from two-dimensional discrete cosine transform, thus preventing the loss of phase information and gaining richer context information for classification. We thoroughly evaluate and analyze the MbsCANet on the publicly accessible BreakHis breast cancer histopathology dataset. It respectively achieves the optimal image-level and patient-level classification results of 99.01% and 98.87%, averagely outperforming the spatial-domain-dominated models by a large margin, and visualization results also demonstrate the effectiveness of the MbsCANet for this medical image application.

Dual residual attention network for image denoising

In image denoising, deep convolutional neural networks (CNNs) can obtain favorable performance on removing spatially invariant noise. However, many of these networks cannot perform well on removing the real noise (i.e. spatially variant noise) that is generated during image acquisition or transmission, which severely impedes their application in practical image denoising tasks. In this paper, we propose a novel Dual-branch Residual Attention Network (DRANet) for image denoising, which has both the merits of a wide model architecture and the attention-guided feature learning. The proposed DRANet includes two different parallel branches, which can capture complementary features to enhance the learning ability of the model. We designed a new residual attention block (RAB) and a novel hybrid dilated residual attention block (HDRAB) for the upper and lower branches, respectively. The RAB and HDRAB can capture rich local features through multiple skip connections between different convolutional layers, and the unimportant features can be dropped. Meanwhile, the long skip connections in each branch and the global feature fusion between the two parallel branches can effectively capture the global features as well. Extensive experiments demonstrate that compared with other state-of-the-art denoising methods, our DRANet can produce competitive denoising performance both on the synthetic and real-world noise removal. The code for DRANet is accessible at https://github.com/WenCongWu/DRANet.

Logic Gate Circuits Based on CeOx/WOy Memristor for the Odd/Even Checker and Encryption/Decryption of Image Applications

AbstractDue to its powerful brain‐like parallel computing and efficient data processing capabilities, memristors are considered to be the core components for building the next generation of artificial intelligence systems. In this study, the CeOx/WOy heterojunction is employed as the functional layer, and various metal materials are utilized as the top electrode to fabricate the memristor. The results indicate that the memristive performance of the Ag/CeOx/WOy/ITO device can be improved by using Ag as the top electrode. By studying the conductivity mechanism of the device, a conductivity model is established that regulates oxygen vacancies and Ag conductive filaments. Furthermore, using the as‐prepared memristor, it is constructed four basic digital logic circuits: OR, AND, XOR, and XNOR, as well as a half adder and a full adder that can be used for digital arithmetic operations. Specifically, an odd/even checker is developed based on XOR and XNOR logic circuits to verify the correctness of data transmission. Finally, it is also designed and implemented a cryptographic array based on a memristor, which can be applied to encrypt and decrypt a series of numbers and images. Therefore, this work extends the application of memristor toward digital circuits, information transmission, data processing and image security encryption.

Quantum image encryption algorithm via optimized quantum circuit and parity bit-plane permutation

Quantum image encryption technology employs the unique features of superposition, entanglement, and quantum state instability, offering advantages like high efficiency, parallelism, and robust resistance to decryption attempts. In this work, we propose a quantum image encryption algorithm via an optimized quantum circuit and parity bit-plane permutation named OCPBP. Our research commences with applying an optimization algorithm to BRQI image preparation. This optimization significantly reduces the number of auxiliary qubits from n+3 to 2 in the preparation of (n+5)-CNOT gate preparation. This reduction makes a significant simplification of the complexity and memory space required for BRQI image preparation. Moreover, we introduce an innovative operation based on parity pixel bit-planes for permutation, which provides a fresh perspective on quantum image encryption based on the BRQI model. Furthermore, we generate quantum key images for XOR operations using the improved logistic map, effectively tackling the challenge of non-random keys that can be a concern in conventional BRQI encryption techniques. We also perform a row-column-based permutation operation to enhance the algorithm’s resilience against potential attacks. Through extensive theoretical analysis and comparative experiments, we confirm the heightened security and reduced complexity of the proposed OCPBP algorithm compared to BRQI-based and chaotic image encryption methods. It holds significant promise for improving quantum image communication and application.

Deuteron system in finite volume with particle-dimer picture

This article delves into the application of Dimer images in addressing two-body problems within lattice quantum chromodynamics (LQCD), providing the finite volume energy spectrum of deuterons in the context of S13-D13 coupled-channels. The author commences by introducing the Dimer-particle method and formulating the Lagrangian for the scattering of two fermions. Additionally, the author defines the relativistic high partial zeta-function in a moving reference system and outlines a method for its rapid convergence calculation. The article further scrutinizes zeta-function representations across various momentum shells and discusses issues related to symmetry breakdown in specific momentum configurations. In sum, this work presents innovative methods for incorporating spin and isospin indices into dimer models, thereby establishing closer relevance to LQCD and deuteron studies.

Retraction Note: An integrated 3D model based face recognition method using synthesized facial expressions and poses for single image applications

Retraction Note: An integrated 3D model based face recognition method using synthesized facial expressions and poses for single image applications

Tagyîr Mawdhî’ Inhirâf Qiblat al-Masjid fî Bamîkasân ‘alâ Asasi al-Tiknôlôjiyya al-Mutaqaddimah: al-Tahlîla al-Ijtimâ’î wa al-Tsaqafî

Reposition of wrongly-established qibla direction typically brings into controversy, particularly among takmir as the mosque officiants. However, what occured in Pamekasan shows different trends. This research therefore focuses on how the Pamekasan based takmir responded on the qibla direction reposition issue. It is a descriptive one based on qualitative data. The data was collected using interview to the takmir and participatory observation at the research locus. The obtained data was then sorted and systematically arranged for verification to draw a conclusion. The research comes to a conclusion that; first, the deviation of qibla direction on the research locus was caused by wrongly-determined direction in the initial mosque building process due to the ignorance of Islamic astronomy or so called ilmu falaq theories. The mistake was figured out trough verification on the sun image and qiblat direction application. Second; the reposition was initiated by takmir who firstly consulted with experts on Islamic law and falaq, made scientific investigation by rechecking the mosque qibla direction using advanced technology, then announced the process result to the mosque congregation, and made a deliberation forum before doing the reposition replacing the older with the more accurate and newer one. Third; takmirs’ perception on the mosque qibla reposition tends to be supporting rather than opposing.

Deep learning-based immunohistochemical estimation of breast cancer via ultrasound image applications.

Breast cancer is the key global menace to women's health, which ranks first by mortality rate. The rate reduction and early diagnostics of breast cancer are the mainstream of medical research. Immunohistochemical examination is the most important link in the process of breast cancer treatment, and its results directly affect physicians' decision-making on follow-up medical treatment. This study aims to develop a computer-aided diagnosis (CAD) method based on deep learning to classify breast ultrasound (BUS) images according to immunohistochemical results. A new depth learning framework guided by BUS image data analysis was proposed for the classification of breast cancer nodes in BUS images. The proposed CAD classification network mainly comprised three innovation points. First, a multilevel feature distillation network (MFD-Net) based on CNN, which could extract feature layers of different scales, was designed. Then, the image features extracted at different depths were fused to achieve multilevel feature distillation using depth separable convolution and reverse depth separable convolution to increase convolution depths. Finally, a new attention module containing two independent submodules, the channel attention module (CAM) and the spatial attention module (SAM), was introduced to improve the model classification ability in channel and space. A total of 500 axial BUS images were retrieved from 294 patients who underwent BUS examination, and these images were detected and cropped, resulting in breast cancer node BUS image datasets, which were classified according to immunohistochemical findings, and the datasets were randomly subdivided into a training set (70%) and a test set (30%) in the classification process, with the results of the four immune indices output simultaneously from training and testing, in the model comparison experiment. Taking ER immune indicators as an example, the proposed model achieved a precision of 0.8933, a recall of 0.7563, an F1 score of 0.8191, and an accuracy of 0.8386, significantly outperforming the other models. The results of the designed ablation experiment also showed that the proposed multistage characteristic distillation structure and attention module were key in improving the accuracy rate. The extensive experiments verify the high efficiency of the proposed method. It is considered the first classification of breast cancer by immunohistochemical results in breast cancer image processing, and it provides an effective aid for postoperative breast cancer treatment, greatly reduces the difficulty of diagnosis for doctors, and improves work efficiency.

DeepPyramid+: medical image segmentation using Pyramid View Fusion and Deformable Pyramid Reception

PurposeSemantic segmentation plays a pivotal role in many applications related to medical image and video analysis. However, designing a neural network architecture for medical image and surgical video segmentation is challenging due to the diverse features of relevant classes, including heterogeneity, deformability, transparency, blunt boundaries, and various distortions. We propose a network architecture, DeepPyramid+, which addresses diverse challenges encountered in medical image and surgical video segmentation.MethodsThe proposed DeepPyramid+ incorporates two major modules, namely “Pyramid View Fusion” (PVF) and “Deformable Pyramid Reception” (DPR), to address the outlined challenges. PVF replicates a deduction process within the neural network, aligning with the human visual system, thereby enhancing the representation of relative information at each pixel position. Complementarily, DPR introduces shape- and scale-adaptive feature extraction techniques using dilated deformable convolutions, enhancing accuracy and robustness in handling heterogeneous classes and deformable shapes.ResultsExtensive experiments conducted on diverse datasets, including endometriosis videos, MRI images, OCT scans, and cataract and laparoscopy videos, demonstrate the effectiveness of DeepPyramid+ in handling various challenges such as shape and scale variation, reflection, and blur degradation. DeepPyramid+ demonstrates significant improvements in segmentation performance, achieving up to a 3.65% increase in Dice coefficient for intra-domain segmentation and up to a 17% increase in Dice coefficient for cross-domain segmentation.ConclusionsDeepPyramid+ consistently outperforms state-of-the-art networks across diverse modalities considering different backbone networks, showcasing its versatility. Accordingly, DeepPyramid+ emerges as a robust and effective solution, successfully overcoming the intricate challenges associated with relevant content segmentation in medical images and surgical videos. Its consistent performance and adaptability indicate its potential to enhance precision in computerized medical image and surgical video analysis applications.

Predicting stone composition via machine-learning models trained on intra-operative endoscopic digital images

ObjectivesThe aim of this study was to use deep learning (DL) of intraoperative images of urinary stones to predict the composition of urinary stones. In this way, the laser frequency and intensity can be adjusted in real time to reduce operation time and surgical trauma.Materials and methodsA total of 490 patients who underwent holmium laser surgery during the two-year period from March 2021 to March 2023 and had stone analysis results were collected by the stone laboratory. A total of 1658 intraoperative stone images were obtained. The eight stone categories with the highest number of stones were selected by sorting. Single component stones include calcium oxalate monohydrate (W1), calcium oxalate dihydrate (W2), magnesium ammonium phosphate hexahydrate, apatite carbonate (CH) and anhydrous uric acid (U). Mixed stones include W2 + U, W1 + W2 and W1 + CH. All stones have intraoperative videos. More than 20 intraoperative high-resolution images of the stones, including the surface and core of the stones, were available for each patient via FFmpeg command screenshots. The deep convolutional neural network (CNN) ResNet-101 (ResNet, Microsoft) was applied to each image as a multiclass classification model.ResultsThe composition prediction rates for each component were as follows: calcium oxalate monohydrate 99% (n = 142), calcium oxalate dihydrate 100% (n = 29), apatite carbonate 100% (n = 131), anhydrous uric acid 98% (n = 57), W1 + W2 100% (n = 82), W1 + CH 100% ( n = 20) and W2 + U 100% (n = 24). The overall weighted recall of the cellular neural network component analysis for the entire cohort was 99%.ConclusionThis preliminary study suggests that DL is a promising method for identifying urinary stone components from intraoperative endoscopic images. Compared to intraoperative identification of stone components by the human eye, DL can discriminate single and mixed stone components more accurately and quickly. At the same time, based on the training of stone images in vitro, it is closer to the clinical application of stone images in vivo. This technology can be used to identify the composition of stones in real time and to adjust the frequency and energy intensity of the holmium laser in time. The prediction of stone composition can significantly shorten the operation time, improve the efficiency of stone surgery and prevent the risk of postoperative infection.

Application of fluorescence image guided cholangiography for the assessment of biliary anatomy in patients with acute cholecystitis

Application of fluorescence image guided cholangiography for the assessment of biliary anatomy in patients with acute cholecystitis

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.

High-Fidelity and Efficient Pluralistic Image Completion with Transformers.

Image completion has made tremendous progress with convolutional neural networks (CNNs), because of their powerful texture modeling capacity. However, due to some inherent properties (e.g., local inductive prior, spatial-invariant kernels), CNNs do not perform well in understanding global structures or naturally support pluralistic completion. Recently, transformers demonstrate their power in modeling the long-term relationship and generating diverse results, but their computation complexity is quadratic to input length, thus hampering the application in processing high-resolution images. This paper brings the best of both worlds to pluralistic image completion: appearance prior reconstruction with transformer and texture replenishment with CNN. The former transformer recovers pluralistic coherent structures together with some coarse textures, while the latter CNN enhances the local texture details of coarse priors guided by the high-resolution masked images. To decode diversified outputs from transformers, auto-regressive sampling is the most common method, but with extremely low efficiency. We further overcome this issue by proposing a new decoding strategy, temperature annealing probabilistic sampling (TAPS), which firstly achieves more than 70× speedup of inference at most, meanwhile maintaining the high quality and diversity of the sampled global structures. Moreover, we find the full CNN architecture will lead to suboptimal solutions for guided upsampling. To render more realistic and coherent contents, we design a novel module, named texture-aware guided attention, to concurrently consider the procedures of texture copy and generation, meanwhile raising several important modifications to solve the boundary artifacts. Through dense experiments, we found the proposed method vastly outperforms state-of-the-art methods in terms of four aspects: 1) large performance boost on image fidelity even compared to deterministic completion methods; 2) better diversity and higher fidelity for pluralistic completion; 3) exceptional generalization ability on large masks and generic dataset, like ImageNet. 4) Much higher decoding efficiency over previous auto-regressive based methods.

Significant results of Serbian military topographical and cartographical activities

Serbian military topographic and cartographic activity has existed for almost 150 years. During that time, a relatively small country like Serbia faced difficult temptations but successfully produced topographic maps. During this period, Serbia was affected by numerous wars and other trials. First, there was a war for liberation with the Turkish Empire, then came the Balkan Wars, the First and Second World Wars and the transformation of Yugoslavia from a kingdom into a socialist state, as well as the final disintegration of Yugoslavia. During all that time, the Serbian army managed to create maps that were a necessary tool used by many civilian institutions. Serbian military topography developed solutions related to geodetic surveying, and cartography provided and developed many maps of different scales with appropriate cartographic keys. Although it often faced difficult situations, the Military Geographical Institute - MGI managed to recover, train new personnel, and continue with topographic surveying, map making, aerial photogrammetry, application of satellite images and implementation of digital technology. In this paper, we provide primary data on the development of military cartographic and topographic activity through a presentation of results and activities from establishing the Military Geographical Institute to the present day. The topographic activity, in addition to topography, includes trigonometric and levelling surveying.

Cross-cultural application of archetypal images in art therapy in China: A case study of Mandala images

Due to their ancient healing genes and significant modern therapeutic implications, Archetypal symbols have been widely employed in art therapy practices. However, research on their application effectiveness still lacks discussion in cross-cultural contexts. This study focuses on the use of archetypal symbols in art therapy—particularly Mandala images—within the cultural context of China. The research focuses on exploring the cultural adaptability of Mandala art therapy in China, along with the cultural challenges and opportunities faced in empirical studies. It further investigates the efforts made by Chinese therapists to optimize Mandala art therapy techniques, enabling better integration into Chinese cultural and therapeutic practices. The results indicate that although research and application in this field are still in the exploratory stage in China, Mandala drawing therapy has demonstrated effective healing and evaluative roles in empirical studies in China. It confronts cultural challenges with localized attempts, revealing the universality and localization trends of archetypal symbols in cross-cultural art therapy practices.

The Application of Cloud Images for Heavy Precipitation Weather in Sichuan Region at the Last Spring of 2023

The Application of Cloud Images for Heavy Precipitation Weather in Sichuan Region at the Last Spring of 2023

Classification evaluation and improvement of airborne PolSAR images for land use mapping using deep learning

Polarimetric synthetic aperture radar (PolSAR) images have been widely used in many fields due to its advantage in obtaining full polarization information, especially in land use classification. To evaluate the performance of airborne PolSAR images in land use classification, this paper systematically evaluated the potential value of PolSAR images in land use classification by using machine learning and deep learning algorithms, and improved the classification performance of airborne PolSAR images by constructing a multi-structural feature aware attention network (MSFA-Net). The classification performance of different algorithms was evaluated by the overall accuracy. The results showed that the machine learning algorithm Random Forest (RF) produced the lowest overall accuracy of 71.93%. Using RF as the baseline accuracy, the deep learning-based classification algorithm significantly improved the classification accuracy. Specifically, Wide Contextual Residual Network achieved an overall accuracy improvement of 8.87% to 80.80%, and Two-branch CNN generated an overall accuracy of 84.49%. Multi-source CNN produced the second highest overall accuracy of 87.25%, an improvement of 15.32%. The proposed MSFA-Net algorithm achieved the highest classification accuracy of 89.29% with an improvement of 17.36%, which further improves the land use mapping capability of airborne PolSAR images. A moderate reduction in resolution helps to improve classification accuracy, with 84.36% accuracy after 4-fold downsampling compared with the original resolution accuracy of 69.30%. On the other hand, noise, polarization error and motion error had a negative impact on the classification performance. This paper highlights the application of airborne SAR images in land use classification, and strengthens the understanding of deep learning and the influence of different factors on PolSAR images in land use classification.