Imaging Algorithm Research Articles

Hidden corrosion monitoring via phase shift evaluation of Lamb wave modes

Lamb wave inspection is a promising candidate for real-time monitoring of hidden corrosion in thin-walled metallic structures. The time-of-flight (ToF) which represents the variation of group velocity due to thickness reduction is commonly used, but its sensitivity is limited at the early stage of corrosion. To address this issue, the phase shift as a function of the phase velocity is defined and evaluated for corrosion detection in this paper. Specifically, the analytic cross-correlation method is applied, which estimates the group delay and the phase shift between two Lamb wave responses before and after the existence of corrosion simultaneously. The simulated examples show that the phase shift performs an obvious improvement on corrosion sensitivity compared to the ToF, and its value increases nearly linearly as the corrosion gets deeper. Ultimately, an experimental example is also introduced, where an active sensor network is deployed and a probabilistic imaging algorithm is introduced. The imaging results demonstrate that the proposed method could successfully identify and accurately localize the hidden corrosion.

Application of Attention-Enhanced 1D-CNN Algorithm in Hyperspectral Image and Spectral Fusion Detection of Moisture Content in Orah Mandarin (Citrus reticulata Blanco)

A method fusing spectral and image information with a one-dimensional convolutional neural network(1D-CNN) for the detection of moisture content in Orah mandarin (Citrus reticulata Blanco) was proposed. The 1D-CNN model integrated with three different attention modules (SEAM, ECAM, CBAM) and machine learning models were applied to individual spectrum and fused information by passing the traditional feature extraction stage. Additionally, the dimensionality reduction of hyperspectral images and extraction of one-dimensional color and textural features from the reduced images were performed, thus avoiding the large parameter volumes and efficiency decline inherent in the direct modeling of two-dimensional images. The results indicated that the 1D-CNN model with integrated attention modules exhibited clear advantages over machine learning models in handling multi-source information. The optimal machine learning model was determined to be the random forest (RF) model under the fusion information, with a correlation coefficient (R) of 0.8770 and a root mean square error (RMSE) of 0.0188 on the prediction set. The CBAM-1D-CNN model under the fusion information exhibited the best performance, with an R of 0.9172 and an RMSE of 0.0149 on the prediction set. The 1D-CNN models utilizing fusion information exhibited superior performance compared to single spectrum, and 1D-CNN with the fused information based on SEAM, ECAM, and CBAM respectively improved Rp by 4.54%, 0.18%, and 10.19% compared to the spectrum, with the RMSEP decreased by 11.70%, 14.06%, and 31.02%, respectively. The proposed approach of 1D-CNN integrated attention can obtain excellent regression results by only using one-dimensional data and without feature pre-extracting, reducing the complexity of the models, simplifying the calculation process, and rendering it a promising practical application.

High-speed wafer surface defect detection with edge enhancement via optical spatial filtering in serial time-encoded imaging

With the rapid development of the chip and metasurface industries, high-speed defect detection of wafer during semiconductor batch manufacturing has become increasingly important for yield control. Here, we propose a high-speed, macro-scale wafer surface defect detection method with enhanced image edges, via optical spatial filtering in serial time-encoded imaging. By using blazed gratings to spatially project different wavelength components of pulsed light into a one-dimensional line, together with dispersive Fourier time stretching technology, a high-speed serial time-encoded imaging system based on a single pixel detector is constructed for testing the wafer surface defects, and optical spatial filtering is introduced to improve the imaging quality. Experiments show high-pass spatial filtering effectively restrains environmental interference and enhances image edge detection. Small target detection is achieved without a complex image algorithm. Our solution extends optical spatial filtering to serial time-encoded imaging, not only permitting wafer macro scale defect detection with edge enhancement, but also having potential significance for imaging lidar, imaging spectrometers, ghost imaging technology, and more.

Review of Microwave Near-Field Sensing and Imaging Devices in Medical Applications.

Microwaves can safely and non-destructively illuminate and penetrate dielectric materials, making them an attractive solution for various medical tasks, including detection, diagnosis, classification, and monitoring. Their inherent electromagnetic properties, portability, cost-effectiveness, and the growth in computing capabilities have encouraged the development of numerous microwave sensing and imaging systems in the medical field, with the potential to complement or even replace current gold-standard methods. This review aims to provide a comprehensive update on the latest advances in medical applications of microwaves, particularly focusing on the near-field ones working within the 1-15 GHz frequency range. It specifically examines significant strides in the development of clinical devices for brain stroke diagnosis and classification, breast cancer screening, and continuous blood glucose monitoring. The technical implementation and algorithmic aspects of prototypes and devices are discussed in detail, including the transceiver systems, radiating elements (such as antennas and sensors), and the imaging algorithms. Additionally, it provides an overview of other promising cutting-edge microwave medical applications, such as knee injuries and colon polyps detection, torso scanning and image-based monitoring of thermal therapy intervention. Finally, the review discusses the challenges of achieving clinical engagement with microwave-based technologies and explores future perspectives.

Fair AI-powered orthopedic image segmentation: addressing bias and promoting equitable healthcare

AI-powered segmentation of hip and knee bony anatomy has revolutionized orthopedics, transforming pre-operative planning and post-operative assessment. Despite the remarkable advancements in AI algorithms for medical imaging, the potential for biases inherent within these models remains largely unexplored. This study tackles these concerns by thoroughly re-examining AI-driven segmentation for hip and knee bony anatomy. While advanced imaging modalities like CT and MRI offer comprehensive views, plain radiographs (X-rays) predominate the standard initial clinical assessment due to their widespread availability, low cost, and rapid acquisition. Hence, we focused on plain radiographs to ensure the utilization of our contribution in diverse healthcare settings, including those with limited access to advanced imaging technologies. This work provides insights into the underlying causes of biases in AI-based knee and hip image segmentation through an extensive evaluation, presenting targeted mitigation strategies to alleviate biases related to sex, race, and age, using an automatic segmentation that is fair, impartial, and safe in the context of AI. Our contribution can enhance inclusivity, ethical practices, equity, and an unbiased healthcare environment with advanced clinical outcomes, aiding decision-making and osteoarthritis research. Furthermore, we have made all the codes and datasets publicly and freely accessible to promote open scientific research.

Review of Research on Feature Extraction Algorithms for Finger Vein Images

Review of Research on Feature Extraction Algorithms for Finger Vein Images

Comparative analysis of different supervised methods for satellite-based land-use classification: a case study of Reyhanlı

Satellite-based land-use classification plays a crucial role in various Earth observation applications, ranging from environmental monitoring to disaster management. This study presents a comparative analysis of machine learning techniques applied to land cover classification using Landsat-9 and Sentinel-2 satellite imagery in the Reyhanlı district in southern Türkiye. Three different classification algorithms, Random Forest (RF), Support Vector Machine (SVM), and Maximum Likelihood Classification (MLC), were evaluated for their ability to distinguish different land cover classes. High resolution multispectral satellite imagery processed under the same conditions using Geographic Information System (GIS) software was utilized in this study. Visual inspection and statistical evaluation, including overall accuracy and kappa coefficient, were employed to assess classification performance. The classification of Sentinel-2 and Landsat-9 satellite imagery using different machine learning algorithms resulted in the highest overall accuracy (OA = 0.911, Kappa = 0.879) for Sentinel 2 imagery with the RF algorithm. These findings highlight the importance of satellite image selection and algorithm optimization for accurate land cover mapping. This study provides valuable insights for local planners and authorities and underscores the potential of Sentinel-2 imagery combined with machine learning techniques for effective land-use classification and monitoring.

Reversible data hiding in encrypted image based on key-controlled balanced Huffman coding

To achieve privacy protection and effective management in cloud computing, and solve the problem of existing reversible data hiding in encrypted image (RDH-EI) algorithms being unable to resist existing various attacks, an RDH-EI algorithm based on key-controlled balanced Huffman coding (KBHC) is proposed. The novelty lies in KBHC and variable-length bit scrambling. KBHC possesses non-preset, balanced, and key-controlled characteristics, providing the proposed algorithm with high capacity and enhanced security. The non-preset allows coding tables to be adaptively generated based on prediction error maps, resulting in shorter encoded streams for higher embedding capacity. The balanced characteristic is achieved by adjusting the subtrees, so that the balance rate in the encoded stream is 0.014, and can also reach 0.065 for particularly smooth images, achieving uniform distribution of the encoded stream, thereby improving the ability to resist statistical analysis attacks. The random key controls the leaf nodes scrambling in the Huffman tree, which realizes the variability of the encoded stream and avoids the potential security risks caused by timestamp reconstruction, laying the foundation to achieve differential attack security. Variable-length bit scrambling determines the pseudo-random extension length and scrambling sequence by both the encryption key and coding table information, effectively resists brute force attacks and ensures up to 100 % difference rate between scrambling sequences generated in each run. Experimental results demonstrate that compared to several RDH-EI methods, the proposed algorithm achieves higher embedding capacity and security under acceptable complexity. The average embedding rate of three databases reaches 3.897 bpp, and the proposed algorithm effectively resists statistical analysis attacks, COA, KPA, and differential attack.

Res2-UNet++: a deep learning image post-processing method for electrical resistance tomography

Abstract The monitoring of multiphase flow distribution in industrial processes in order to optimize production presents a challenge. Electrical resistance tomography (ERT) is a technique used to visualize the inner distribution of multiphase flow. Image reconstruction plays a vital role in ERT. However, the nonlinearity and ill-posedness of inverse problems make image reconstruction in ERT difficult. The development of advanced imaging algorithms has attracted much interest for this purpose. In this work, an improved U-shaped deep learning model is proposed, which combines the advantages of the multi-scale feature extraction of UNet++ and the residual feature fusion of Res2Net. The network is designed to post-process the pre-reconstruction results of traditional ERT image-reconstruction methods, combining the generalization ability of the model-based methods and the flexible feature-extraction advantage of deep learning methods. The post-processing includes super-resolution, image denoising and artifact removal. Simulations and experiments are designed to verify the generalization ability and effectiveness of the proposed post-processing model. Both simulation and experimental results show that the proposed U-shaped network approach outperforms other deep learning methods, and the proposed deep learning model is fit for post-processing in ERT, making it a robust solution for applications.

One-step inverse generation network for sparse-view dual-energy CT reconstruction and material imaging

Objective. Sparse-view dual-energy spectral computed tomography (DECT) imaging is a challenging inverse problem. Due to the incompleteness of the collected data, the presence of streak artifacts can result in the degradation of reconstructed spectral images. The subsequent material decomposition task in DECT can further lead to the amplification of artifacts and noise. Approach. To address this problem, we propose a novel one-step inverse generation network (OIGN) for sparse-view dual-energy CT imaging, which can achieve simultaneous imaging of spectral images and materials. The entire OIGN consists of five sub-networks that form four modules, including the pre-reconstruction module, the pre-decomposition module, and the following residual filtering module and residual decomposition module. The residual feedback mechanism is introduced to synchronize the optimization of spectral CT images and materials. Main results. Numerical simulation experiments show that the OIGN has better performance on both reconstruction and material decomposition than other state-of-the-art spectral CT imaging algorithms. OIGN also demonstrates high imaging efficiency by completing two high-quality imaging tasks in just 50 seconds. Additionally, anti-noise testing is conducted to evaluate the robustness of OIGN. Significance. These findings have great potential in high-quality multi-task spectral CT imaging in clinical diagnosis.

TROG: a fast and robust scene-matching algorithm for geo-referenced images

ABSTRACT In a GNSS-denied/challenged environment, the scene-matching navigation system (SMNS) is a vital autonomous technology for unmanned aerial vehicles (UAV). This paper proposes a novel template-matching framework for multimodal images since UAV navigation requires precision and real-time performance while utilizing onboard computers with low computational power. Specifically, first, the local descriptor is extracted to form a pixel-wise feature representation of an image. Then, the Fast Fourier Transform is applied to measure the similarity based on the feature representation. In the feature extraction part, a gradient-like pixel-level feature descriptor is designed, which is reconstructed by weighting it according to the gradient-like angles, named the three-dimensional reconstruction oriented gradient-like (TROG) descriptor. An optimized similarity measurement template is introduced in the matching part, which improves the traditional feature-based similarity measurement algorithm defined using Fast Fourier Transform (FFT) in the frequency domain. This strategy eliminates redundant computations during the matching process. To verify the effectiveness of the proposed algorithm, satellite imagery data from Google Earth are used as a reference images, and sensed images (including optical, IR, SAR, and Hyperspectral) are captured by UAV and satellites for image matching to testify to the registration accuracy, robustness, and computational efficiency. The experiment demonstrates that TROG is accurate, robust, and attains high real-time performance, making it applicable to UAV navigation and positioning. Additionally, field-flight experiments evaluate scene-matching navigation under satellite-denied conditions and low computational power conditions for UAVs, demonstrating that our scene-matching navigation system can achieve precise positioning with a positioning error of less than 1.637 m, which is comparable to satellite/inertial navigation systems. The experimental results from outdoor flight experiments highlight the value of our proposed algorithm in engineering applications under satellite denial conditions.

Statistical shape modeling of the geometric morphology of the canine femur, tibia, and patella.

Bone morphometry varies among dogs of different sizes and breeds. Studying these differences may help understand the predisposition of certain breeds for specific orthopedic pathologies. This study aimed to develop a statistical shape model (SSM) of the femur, patella, and tibia of dogs without any clinical orthopeadic abnormalities to analyze and compare morphological variations based on body weight and breed. A total of 97 CT scans were collected from different facilities and divided based on breed and body weight. The 3D models of the bones were obtained and aligned to a coordinate system. The SSM was created using principal component analysis (PCA) to analyze shape variations. The study found that the first few modes of variation accounted for a significant percentage of the total variation, with size/scale being the most prominent factor. The results provide valuable insights into normal anatomical variations and can be used for future research in understanding pathological bone morphologies and developing 3D imaging algorithms in veterinary medicine.

Ultrasonic full guided wavefield for damage detection in curved CFRP parts

Guided waves can propagate from flat plates to curved structures and carry structural health information. In this paper, an ultrasonic guided wave inspection method is proposed for detecting damages in curved parts made of carbon fibre reinforced plastics (CFRPs), such as hat-stringer-stiffened panel. By collecting full wavefield information, we obtain the standing wave information formed by the interaction between guided waves and damage. The interaction mechanism between guided waves and damage is simulated through finite element simulation, and the wavefield curvature imaging algorithm is used to generate a damage image in the curved area. Furthermore, a specimen with delamination damages is fabricated, and full wavefield information is collected through a scanning laser Doppler vibrometer (SLDV). We then use the proposed imaging algorithm to detect delamination damages of different sizes. The experimental results were consistent with the simulation results, confirming the effectiveness of the proposed method. Notably, the technology combines the advantages of guided waves and SLDV, which can obtain full wavefield information without contact, including damage-related characteristics. This work provides a convenient and comprehensive method for collecting wavefield information on complex curved CFRP parts for structural health monitoring, which would significantly improve quality control for CFRP components in various fields.

Efficient factored gradient descent algorithm for quantum state tomography

Reconstructing the state of quantum many-body systems is of fundamental importance in quantum information tasks, but extremely challenging due to the curse of dimensionality. In this work, we present an efficient quantum tomography protocol that combines the state-factored with eigenvalue mapping to address the rank-deficient issue and incorporates a momentum-accelerated gradient descent algorithm to speed up the optimization process. We implement extensive numerical experiments to demonstrate that our factored gradient descent algorithm efficiently mitigates the rank-deficient problem and admits orders of magnitude better tomography accuracy and faster convergence. We also find that our method can accomplish the full-state tomography of random 11-qubit mixed states within one minute. Published by the American Physical Society 2024

LSB Embedding with Hillbert Curve

Information security has pulled in open consideration within the past decade, effective strategies for information security assurance are in request. This method inserts the secret information into the pixels and the pixel order for embedding is by Hillbert Curve. With standard petitcola images many experiments are carried out. The prevalence of ciphertext and encrypted images in cyberspace has led to increased interest from academics towards RDH algorithms in encrypted images

Analysis on stable imaging and inverse algorithm for artificial source EM data

Abstract The inversion of artificial source electromagnetic (EM) method data fundamentally involves constructing a mathematical relationship between observable data and geological structures. The aim of imaging and inversion is to construct a geophysical model that matches the observable results, thereby realizing the identification of subsurface targets. The results of EM data inversion, due to the simplicity of geophysical models, limit inversion computing efficiency. Moreover, complexity of actual geological structures, and lack of onsite observable data, are often hindered by non-uniqueness. The challenge in the interpretation of artificial source EM data is in enhancing both the precision and expeditiousness of the inversion process. It can be classified into three main types for EM data inversion: direct imaging inversion, deterministic inversion, and stochastic inversion. To enhance computational efficiency and reduce non-uniqueness in the results, effective inversion methods, prior geological information, geophysical data, and comprehensive analysis can help mitigate the issue of non-uniqueness in EM data inversion, thereby leading to more rational geophysical interpretation results. With the progress of technology such as computing centers and the development of artificial intelligence methods, future inversion techniques will become faster, more efficient, and more intelligent, and will be applied to the interpretation of artificial source EM data.

Historical evolution of sex and age estimation techniques through the skull

The estimation of sex and age through cranial measurements is a fundamental practice in various fields such as physical anthropology, archaeology, and forensic medicine. Over the centuries, techniques and methodologies have been developed and refined to increase the accuracy of these estimates, reflecting advances in anatomical knowledge, imaging technology, and statistical analysis. This article aims to explore the historical evolution of these techniques, highlighting the major changes and innovations from the rudimentary methods used in early anthropological investigations to the sophisticated contemporary approaches incorporating cutting-edge technologies like computed tomography and machine learning algorithms. In addition to reviewing traditional methods, this study examines how the application of new technologies has contributed to greater accuracy and reliability in sex and age estimations, as well as the implications of these improvements for population studies and forensic investigations. By tracing this evolution, it seeks not only to provide a comprehensive overview of the historical development of these techniques but also to contextualize future trends and the challenges that still need to be addressed.

SOP Leading symptom: flank pain

In addition to triggers such as ureteral stones or pyelonephritis, the common symptom of flank pain can be associated with a whole range of conditions. This SOP is intended to give doctors in the emergency department an overview of the possible causes. Based on medical history, clincal examination including sonography and laboratory diagnostics, important differential diagnoses are addressed and an imaging algorithm is presented.

Iterative Decomposition Algorithm for Electrical Impedance Tomography to Image Large Resistivity Changes

Iterative Decomposition Algorithm for Electrical Impedance Tomography to Image Large Resistivity Changes

Novel method of image analysis that combines Radon and Fourier image transformations for the purpose of differentiating between malignant and benign colonic biopsies.

Colorectal cancer is the third most common type of cancer. It develops slowly as a polyp that can turn into a cancerous tumor. This study aimed to develop a decision-making algorithm of microscopic images using texture analysis that is orientation free, to be used for automated classification of normal and neoplastic (malignant or premalignant) colonic biopsies. Forty-nine colonic adenocarcinomas, 41 adenomas and a control group of adjacent normal colonic mucosa were included in the texture analysis. Radon transform followed by the Fast Fourier transform were applied to the images. Subsequently, the gray level co-occurrence matrix (GLCM) transform was applied allowing the extraction of four textural variables (homogeneity, contrast, correlation, and entropy). For classification and prediction of the diagnosis, a statistical multivariate regression model and a neural network (NNET) model were used and compared. The statistical model provided a sensitivity of 71.3% and a specificity of 50% (Area under the ROC curve: 0.67) for classifying the neoplastic and the normal images, respectively. The NNET model was superior to the statistical model and produced a sensitivity of 97.9% and specificity of 88% (Area under the ROC curve: 0.92). To our knowledge, this is the first study that used a combination of Radon, FFT, and GLCM transformations in order to overcome the tissue orientation problem in texture analysis of microscopic images of colonic biopsies. The NNET classifier trained by the extracted textural features proved to be superior to the statistical classifier, thus predicting colonic neoplasia with high accuracy. RESEARCH HIGHLIGHTS: We propose a novel decision-making algorithm of orientation invariant image texture analysis, fast and easily implemented for automated differentiation between benign and neoplastic epithelial tumors of the colon. This method can reduce the turnaround time allowing to prioritize the biopsies during their examination and diagnosis by the pathologist.