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With the rapid development of synthetic aperture radar (SAR), delivery platforms are gradually becoming diversified and miniaturized. The SAR flight process is susceptible to external influences, resulting in unsatisfactory imaging results, so it is necessary to optimize imaging processing in combination with the SAR imaging quality assessment (IQA) index. Based on the principle of SAR imaging, this paper analyzes the impact of defocusing on imaging results caused by mismatched filters and draws on the assessment algorithm of motion blur, proposing a SAR IQA index based on average blurred edge width (ABEW) in the salient area. In addition, the idea of decomposing the transfer function in the frequency domain and fitting the matched filter with a polynomial is also proposed. The estimation of the flight trajectory is changed to a correction of the matched filter, avoiding the precise estimation of Doppler parameters and complex calculations during the time–frequency conversion process. The effectiveness of ABEW was verified by using SAR images of real scenes, and the results were highly consistent with the actual image quality. The imaging processing was tested using the echo signals generated by the errors introduced during the flight process, and more satisfactory imaging results were obtained by using ABEW with the filter for correction. The imaging process was tested using the echo signal generated by introducing errors during the flight, and the filter was corrected using ABEW as an index, obtaining a comparatively ideal imaging result. [ABSTRACT FROM AUTHOR]

Aims There is lack of agreement on late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) imaging processing for guiding ventricular tachycardia (VT) ablation. We aim at developing and validating a systematic processing approach on LGE-CMR images to identify VT corridors that contain critical VT isthmus sites. Methods and results This is a translational study including 18 pigs with established myocardial infarction and inducible VT undergoing in vivo characterization of the anatomical and functional myocardial substrate associated with VT maintenance. Clinical validation was conducted in a multicentre series of 33 patients with ischaemic cardiomyopathy undergoing VT ablation. Three-dimensional LGE-CMR images were processed using systematic scanning of 15 signal intensity (SI) cut-off ranges to obtain surface visualization of all potential VT corridors. Analysis and comparisons of imaging and electrophysiological data were performed in individuals with full electrophysiological characterization of the isthmus sites of at least one VT morphology. In both the experimental pig model and patients undergoing VT ablation, all the electrophysiologically defined isthmus sites (n = 11 and n = 19, respectively) showed overlapping regions with CMR-based potential VT corridors. Such imaging-based VT corridors were less specific than electrophysiologically guided ablation lesions at critical isthmus sites. However, an optimized strategy using the 7 most relevant SI cut-off ranges among patients showed an increase in specificity compared to using 15 SI cut-off ranges (70 vs. 62%, respectively), without diminishing the capability to detect VT isthmus sites (sensitivity 100%). Conclusion Systematic imaging processing of LGE-CMR sequences using several SI cut-off ranges may improve and standardize procedure planning to identify VT isthmus sites. [ABSTRACT FROM AUTHOR]

Due to signal shielding caused by building structures, conventional mature positioning technologies such as the Global Positioning System (GPS) are only suitable for outdoor navigation and detection. However, there are many scenarios that urgently require high-precision indoor positioning technologies, such as indoor wireless optical communications (OWCs), navigation in large buildings, and warehouse management. Here, we proposed a millimeter-precision indoor positioning technology based on metalens-integrated camera, which determines the position of the device through imaging of beacon LEDs. Thanks to the wide-angle imaging design of our metalens, the camera can accurately capture images of beacon LEDs even when it is situated in distant corner locations. Consequently, our localization scheme achieves millimeter-level positioning accuracy across majority of wide-angle (∼120°) indoor area. Compared to traditional positioning schemes by photodiode (PD), our imaging-based approach demonstrates superior resistance to interference, thereby safeguarding positioning precision from the external signals influence. Furthermore, the compact dimensions and high performances of the positioning device make it suitable for integration into highly portable devices, such as smartphones and drones, revealing its broad potential applications in the future. [ABSTRACT FROM AUTHOR]

[Display omitted] • Optical sensors are highly-promising for seed phenotype digitization. • Spectroscopy, digital imaging, and 3D reconstruction are primarily used digital techniques. • Optical sensors can detect both visible external and invisible internal phenotypes. • Matched optical sensors can effectively reduce resource loss in seed phenotype evaluation. • Future research should focus on phenotype equipment, platform, and data processing algorithms for automatic, integrated, and intelligent evaluation. The breeding of high-quality, high-yield, and disease-resistant varieties is closely related to food security. The investigation of breeding results relies on the evaluation of seed phenotype, which is a key step in the process of breeding. In the global digitalization trend, digital technology based on optical sensors can perform the digitization of seed phenotype in a non-contact, high throughput way, thus significantly improving breeding efficiency. This paper provides a comprehensive overview of the principles, characteristics, data processing methods, and bottlenecks associated with three digital technique types based on optical sensors: spectroscopy, digital imaging, and three-dimensional (3D) reconstruction techniques. In addition, the applicability and adaptability of digital techniques based on the optical sensors of maize seed phenotype traits, namely external visible phenotype (EVP) and internal invisible phenotype (IIP), are investigated. Furthermore, trends in future equipment, platform, phenotype data, and processing algorithms are discussed. This review offers conceptual and practical support for seed phenotype digitization based on optical sensors, which will provide reference and guidance for future research. The digital techniques based on optical sensors can perform non-contact and high-throughput seed phenotype evaluation. Due to the distinct characteristics of optical sensors, matching suitable digital techniques according to seed phenotype traits can greatly reduce resource loss, and promote the efficiency of seed evaluation as well as breeding decision-making. Future research in phenotype equipment and platform, phenotype data, and processing algorithms will make digital techniques better meet the demands of seed phenotype evaluation, and promote automatic, integrated, and intelligent evaluation of seed phenotype, further helping to lessen the gap between digital techniques and seed phenotyping. [ABSTRACT FROM AUTHOR]

Background: The breeding of high-quality, high-yield, and disease-resistant varieties is closely related to food security. The investigation of breeding results relies on the evaluation of seed phenotype, which is a key step in the process of breeding. In the global digitalization trend, digital technology based on optical sensors can perform the digitization of seed phenotype in a non-contact, high throughput way, thus significantly improving breeding efficiency. Aim of review: This paper provides a comprehensive overview of the principles, characteristics, data processing methods, and bottlenecks associated with three digital technique types based on optical sensors: spectroscopy, digital imaging, and three-dimensional (3D) reconstruction techniques. In addition, the applicability and adaptability of digital techniques based on the optical sensors of maize seed phenotype traits, namely external visible phenotype (EVP) and internal invisible phenotype (IIP), are investigated. Furthermore, trends in future equipment, platform, phenotype data, and processing algorithms are discussed. This review offers conceptual and practical support for seed phenotype digitization based on optical sensors, which will provide reference and guidance for future research. Key scientific concepts of review: The digital techniques based on optical sensors can perform non-contact and high-throughput seed phenotype evaluation. Due to the distinct characteristics of optical sensors, matching suitable digital techniques according to seed phenotype traits can greatly reduce resource loss, and promote the efficiency of seed evaluation as well as breeding decision-making. Future research in phenotype equipment and platform, phenotype data, and processing algorithms will make digital techniques better meet the demands of seed phenotype evaluation, and promote automatic, integrated, and intelligent evaluation of seed phenotype, further helping to lessen the gap between digital techniques and seed phenotyping.

Food loss and waste occur throughout the food supply chain and represent food security and environmental, economic, and societal problems. Fresh fruit and vegetables contribute to over 40% of global food loss and waste. A significant portion of fruit and vegetables loss takes place on the farm during postharvest handling in developing countries, which is linked to smallholders' financial and geographic constraints in purchasing modern postharvest handling technologies. While in developed countries, waste is the main problem identified at the retail and consumption levels because of inadequate logistics management, storage, and consumer behavior. The loss and waste deprive the population of a significant quantity of healthy food. To address this challenge, cost-effective, easy-to-use, and affordable approaches could be supplied to stakeholders. These strategies encompass the utilization of shading, low-cost packaging, porous evaporative cooling, zero-energy cooling chambers, and pot-in-pot coolers, for reductions in loss in developing countries. Meanwhile, in developed countries, biosensors, 1-methylcyclopropene, and imaging processing are employed to assess the quality of fresh fruit and vegetables at both retail and consumer levels. By exploring these methods, the review aims to provide smallholders, retailers, and consumers with efficient methods for improving produce operating techniques, resulting in reduced losses and waste and higher income. [ABSTRACT FROM AUTHOR]

Abstract Cancer Stem Cells presumably drive tumor growth and resistance to conventional cancer treatments. From a previous computational model, we inferred that these cells are not uniformly distributed in the bulk of a tumorsphere. To confirm this result, we cultivated tumorspheres enriched in stem cells, and performed immunofluorescent detection of the stemness marker SOX2 using confocal microscopy. In this article, we present an image processing method that reconstructs the amount and location of the Cancer Stem Cells in the spheroids. Its advantage is the use of a statistical criterion to classify the cells in Stem and Differentiated, instead of setting an arbitrary threshold. Moreover, the analysis of the experimental images presented in this work agrees with the results from our computational models, thus enforcing the notion that the distribution of Cancer Stem Cells in a tumorsphere is non-homogeneous. Additionally, the method presented here provides a useful tool for analyzing any image in which different kinds of cells are stained with different markers.

Cancer Stem Cells presumably drive tumor growth and resistance to conventional cancer treatments. From a previous computational model, we inferred that these cells are not uniformly distributed in the bulk of a tumorsphere. To confirm this result, we cultivated tumorspheres enriched in stem cells, and performed immunofluorescent detection of the stemness marker SOX2 using confocal microscopy. In this article, we present an image processing method that reconstructs the amount and location of the Cancer Stem Cells in the spheroids. Its advantage is the use of a statistical criterion to classify the cells in Stem and Differentiated, instead of setting an arbitrary threshold. Moreover, the analysis of the experimental images presented in this work agrees with the results from our computational models, thus enforcing the notion that the distribution of Cancer Stem Cells in a tumorsphere is non-homogeneous. Additionally, the method presented here provides a useful tool for analyzing any image in which different kinds of cells are stained with different markers. [ABSTRACT FROM AUTHOR]

Food loss and waste occur throughout the food supply chain and represent food security and environmental, economic, and societal problems. Fresh fruit and vegetables contribute to over 40% of global food loss and waste. A significant portion of fruit and vegetables loss takes place on the farm during postharvest handling in developing countries, which is linked to smallholders’ financial and geographic constraints in purchasing modern postharvest handling technologies. While in developed countries, waste is the main problem identified at the retail and consumption levels because of inadequate logistics management, storage, and consumer behavior. The loss and waste deprive the population of a significant quantity of healthy food. To address this challenge, cost-effective, easy-to-use, and affordable approaches could be supplied to stakeholders. These strategies encompass the utilization of shading, low-cost packaging, porous evaporative cooling, zero-energy cooling chambers, and pot-in-pot coolers, for reductions in loss in developing countries. Meanwhile, in developed countries, biosensors, 1-methylcyclopropene, and imaging processing are employed to assess the quality of fresh fruit and vegetables at both retail and consumer levels. By exploring these methods, the review aims to provide smallholders, retailers, and consumers with efficient methods for improving produce operating techniques, resulting in reduced losses and waste and higher income.

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Spaceborne Synthetic Aperture Radar (SAR) can achieve various performance combinations of resolution and observation bandwidth by adjusting the working modes. The imaging swath of the traditional spaceborne SAR working mode is along the satellite orbit, and the geographical trend is single; however, the geographical shape and direction of the surface scene are diverse and generally do not match the imaging swath along the orbit, resulting in a long data acquisition period, low azimuth resolution, and storage waste of computing resources. To this end, the spaceborne SAR Non-along-track imaging mode is a new method for spaceborne SAR scene matching that is characterized by an imaging zone that is no longer mechanically along the satellite orbit but is generated according to the actual geographical direction of the scene to achieve “customization” that matches the scene imaging. In this paper, the main opportunities and challenges faced by the new mode of spaceborne SAR scene matching are discussed from the aspects of information acquisition and imaging processing, and the principled verification of the spaceborne SAR Non-along-track imaging mode is provided through a computer simulation.

Optical coherence tomography angiography (OCTA) can provide in vivo three‐dimensional microvasculature information of bio‐tissues, but it is sensitive to motion and time‐consuming. To overcome these limitations, we propose an adaptive multiple time interval correlation mapping OCTA with a time‐efficient scanning protocol and motion compensation algorithms. A spectral‐domain OCT with a center wavelength of 850 nm, A‐scan rate of 120 kHz and spatial resolution of 4.1 μm (axial) × 6.9 μm (lateral) is built to reconstruct the microvascular networks in the human arm. By adaptive optimization of the weights of different time interval B‐scan angiograms, our novel OCTA technique achieves better performance with a visible vascular density increase of ~67% and a signal‐to‐noise ratio enhancement of ~11.6%. [ABSTRACT FROM AUTHOR]

Background: Pulmonary vein (PV) data are commonly measured on multiplanar image reformation (MPR) images and volume rendering (VR) images. Purpose: To compared and analyze the advantages and disadvantages of PV data based on VR images and MPR images. Material and Methods: A total of 94 patients with atrial fibrillation (AF) with imaging data were included in the study. The respective image postprocessing time and the three surgical interventionists' preferences for the two images were recorded. A paired t -test or chi-square test was used to compare their difference, and P < 0.05 was considered statistically significant. Results: There was no statistically significant difference between the data values including the maximal and minimal ostial diameters of the left superior PV (LSPV), the left inferior PV (LIPV), the right superior PV (RSPV), and the right inferior PV (RIPV) obtained by VR and MPR images (P > 0.05). Yet, the mean postprocessing time of VR images (15.10 ± 3.05 min) was shorter compared to MPR images (16.54 ± 2.60 min) (t = 22.84, P < 0.05). All three surgical interventionists preferred VR images (accounted for 85.1%, 86.2%, and 84.0%, respectively), and there was no statistical difference in the degree of image preference among the three (chi-square = 0.596, P = 0.963). Conclusion: PV data measurement could be performed on both VR and MRP images; however, the data on VR images were more intuitive and more accessible for interventional surgeons. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of low real-time, low applicability, and low welding precision of automatic welding system, a seam tracking system based on laser vision is designed. Use the laser vision sensor to collect the weld image and transmits it to the industrial control computer for processing. Using a median filter to eliminate noise impacts such as arc and splash. Then, this paper focuses on the combination of an improved image threshold segmentation algorithm is used to solve the optimal threshold to obtain the binary image. And the information of laser stripe and the background are separated, overcomes the problems that the researchers have encountered before, such as the unrecognized global optimal solution, and the inaccuracy of the segmentation caused by system jitter. Finally, combined with the improved upper and lower average method, least square method, and Hough transform, the weld feature points are identified and more ideal real-time weld tracking is realized. The experimental results show that the method can accurately track the weld feature points, and improve the detection speed. [ABSTRACT FROM AUTHOR]

Rapid, non-destructive, and smart assessment of the maturity levels of fruit facilitates their harvesting and handling operations throughout the supply chain. Recent studies have introduced machine vision systems as a promising candidate for non-destructive evaluations of the ripeness levels of various agricultural and forest products. However, the reported models have been fruit-specific and cannot be applied to other fruit. In this regard, the current study aims to evaluate the feasibility of estimating the ripeness levels of wild pistachio fruit using image processing and artificial intelligence techniques. Images of wild pistachios at four ripeness levels were recorded using a digital camera, and 285 color and texture features were extracted from 160 samples. Using the quadratic sequential feature selection method, 16 efficient features were identified and used to estimate the maturity levels of samples. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and an artificial neural network (ANN) were employed to classify samples into four ripeness levels, including initial unripe, secondary unripe, ripe, and overripe. The developed machine vision system achieved a correct classification rate (CCR) of 93.75, 97.5, and 100%, respectively. The high accuracy of the developed models confirms the capability of the low-cost visible imaging system in assessing the ripeness of wild pistachios in a non-destructive, automated, and rapid manner. [ABSTRACT FROM AUTHOR]

Aims: There is lack of agreement on late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) imaging processing for guiding ventricular tachycardia (VT) ablation. We aim at developing and validating a systematic processing approach on LGE-CMR images to identify VT corridors that contain critical VT isthmus sites., Methods and Results: This is a translational study including 18 pigs with established myocardial infarction and inducible VT undergoing in vivo characterization of the anatomical and functional myocardial substrate associated with VT maintenance. Clinical validation was conducted in a multicentre series of 33 patients with ischaemic cardiomyopathy undergoing VT ablation. Three-dimensional LGE-CMR images were processed using systematic scanning of 15 signal intensity (SI) cut-off ranges to obtain surface visualization of all potential VT corridors. Analysis and comparisons of imaging and electrophysiological data were performed in individuals with full electrophysiological characterization of the isthmus sites of at least one VT morphology. In both the experimental pig model and patients undergoing VT ablation, all the electrophysiologically defined isthmus sites (n = 11 and n = 19, respectively) showed overlapping regions with CMR-based potential VT corridors. Such imaging-based VT corridors were less specific than electrophysiologically guided ablation lesions at critical isthmus sites. However, an optimized strategy using the 7 most relevant SI cut-off ranges among patients showed an increase in specificity compared to using 15 SI cut-off ranges (70 vs. 62%, respectively), without diminishing the capability to detect VT isthmus sites (sensitivity 100%)., Conclusion: Systematic imaging processing of LGE-CMR sequences using several SI cut-off ranges may improve and standardize procedure planning to identify VT isthmus sites., Competing Interests: Conflict of interest: none declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Nondestructive inspection technology based on machine vision can effectively improve the efficiency of fresh fruit quality inspection. However, fruits with smooth skin and less texture are easily affected by specular highlights during the image acquisition, resulting in light spots appearing on the surface of fruits, which severely affects the subsequent quality inspection. Aiming at this issue, we propose a new specular highlight removal algorithm based on multi-band polarization imaging. First of all, we realize real-time image acquisition by designing a new multi-band polarization imager, which can acquire all the spectral and polarization information through single image capture. Then we propose a joint multi-band-polarization characteristic vector constraint to realize the detection of specular highlight, and next we put forward a Max-Min multi-band-polarization differencing scheme combined with an ergodic least-squares separation for specular highlight removal, and finally, the chromaticity consistency regularization is used to compensate the missing details. Experimental results demonstrate that the proposed algorithm can effectively and stably remove the specular highlight and provide more accurate information for subsequent fruit quality inspection. Besides, the comparison of algorithm speed further shows that our proposed algorithm has a good tradeoff between accuracy and complexity. [ABSTRACT FROM AUTHOR]

As an important tool for acquiring remote sensing information, Synthetic Aperture Radar (SAR) has various modes, including high-resolution wide-swath, multi-angle information acquisition, high temporal observation, and three-dimensional topographic mapping. For any spaceborne SAR system, obtaining high-quality images is a prerequisite for improving the performance of SAR applications. In this paper, we analyze the factors affecting spaceborne SAR imaging and image quality with respect to orbit, platform, payload, and signal processing. We describe high-precision data acquisition techniques, including amplitude-phase compensation, the dynamic adjustment of the central electronic equipment, and antenna pattern estimation. We then present imaging compensation methods based on the improved motion model and tropospheric delay correction, which can achieve resolutions better than 0.3 m. Lastly, we summarize and compare SAR image processing techniques such as speckle noise suppression, azimuth ambiguity suppression, and sidelobe suppression, whereby the equivalent number of looks can be increased to more than 25 and the azimuth ambiguity and sidelobes can both be suppressed by 20 dB.

Rapid, non-destructive, and smart assessment of the maturity levels of fruit facilitates their harvesting and handling operations throughout the supply chain. Recent studies have introduced machine vision systems as a promising candidate for non-destructive evaluations of the ripeness levels of various agricultural and forest products. However, the reported models have been fruit-specific and cannot be applied to other fruit. In this regard, the current study aims to evaluate the feasibility of estimating the ripeness levels of wild pistachio fruit using image processing and artificial intelligence techniques. Images of wild pistachios at four ripeness levels were recorded using a digital camera, and 285 color and texture features were extracted from 160 samples. Using the quadratic sequential feature selection method, 16 efficient features were identified and used to estimate the maturity levels of samples. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and an artificial neural network (ANN) were employed to classify samples into four ripeness levels, including initial unripe, secondary unripe, ripe, and overripe. The developed machine vision system achieved a correct classification rate (CCR) of 93.75, 97.5, and 100%, respectively. The high accuracy of the developed models confirms the capability of the low-cost visible imaging system in assessing the ripeness of wild pistachios in a non-destructive, automated, and rapid manner.

Nondestructive inspection technology based on machine vision can effectively improve the efficiency of fresh fruit quality inspection. However, fruits with smooth skin and less texture are easily affected by specular highlights during the image acquisition, resulting in light spots appearing on the surface of fruits, which severely affects the subsequent quality inspection. Aiming at this issue, we propose a new specular highlight removal algorithm based on multi-band polarization imaging. First of all, we realize real-time image acquisition by designing a new multi-band polarization imager, which can acquire all the spectral and polarization information through single image capture. Then we propose a joint multi-band-polarization characteristic vector constraint to realize the detection of specular highlight, and next we put forward a Max-Min multi-band-polarization differencing scheme combined with an ergodic least-squares separation for specular highlight removal, and finally, the chromaticity consistency regularization is used to compensate the missing details. Experimental results demonstrate that the proposed algorithm can effectively and stably remove the specular highlight and provide more accurate information for subsequent fruit quality inspection. Besides, the comparison of algorithm speed further shows that our proposed algorithm has a good tradeoff between accuracy and complexity.

Wireless transmission capacity demands are continuously growing. Infrared optical wireless communication (IR-OWC) is considered as a promising solution for the future high-speed indoor applications because it can easily offer more than 10 GHz bandwidth with commercially available devices of the fiber-optic communication eco-systems. An IR-OWC cell employing narrow beam widths requires powerful beam-steering and localization functions to dynamically pinpoint the optical beams to different user devices (UDs), and typically has a limited service area. Powerful localization functions with high accuracy typically require sophisticated hardware and resource- and time-consuming signal processing, which may cause serious economical burdens to the communication system. Besides, in regard of a system with extended service area, it may be comprehensive and thus expensive to fully cover the whole space by densely spaced IR-OWC cells. In this article, we introduce a hybrid radio-optical wireless system, which offers high speed IR-OWC services at specific hotspots, and covers spatial gaps among the IR-OWC cells through broadcasting of Wi-Fi signals. This balances the requirements among capacity, coverage, and cost. For each IR-OWC cell, image feature matching based on processing with additional differential operations is proposed to more efficiently process the signals. The images are captured by IP cameras, and can be transparently transported over the communication system through Wi-Fi. Based on our proposal, a 10 Gb/s automatically beam-steered IR-OWC system integrated with ∼80 Mb/s WiFi and with localization function is designed and discussed. An independent test showing 0.24-cm localization accuracy for a 1-cm UD aperture over 180-cm distance is performed. Our processing time shows ∼50% efficiency improvement to the speeded up robust features (SURF) method, and has less recognition uncertainties. [ABSTRACT FROM AUTHOR]

Systems developed to work with computational intelligence have become very efficient, and in some cases obtain more accurate results than evaluations by humans. Hence, this work proposes a new online approach based on deep learning tools according to the concept of transfer learning to generate a computational intelligence framework for use with the Internet of Health Things (IoHT) devices. This framework allows the user to add their images and perform platform training almost as easily as creating folders and placing files in regular cloud storage services. The trials carried out with the tool showed that even people with no programming and image processing knowledge were able to set up projects in a few minutes. The proposed approach is validated using three medical databases, which include cerebral vascular accident images for stroke type classification, lung nodule images for malignant classification, and skin images for the classification of melanocytic lesions. The results show the efficiency and reliability of the framework, which reached 91.6% Accuracy in the stroke images and lung nodules databases, and 92% Accuracy in the skin images databases. This prove the immense contribution that this work can bring to assist medical professionals in analyzing complex examinations quickly and accurately, allowing a large medical examination database through a consolidated collaborative IoT platform. [ABSTRACT FROM AUTHOR]

Most ghost imaging reconstruction algorithms require a large measurement time to retrieve the object information clearly. But not all groups of data play a positive role in reconstructing the object image. Abandoning some redundant data can not only enhance the quality of reconstruction images but also speed up the computation process. Here, we propose a method to screen the data using two threshold values set by a proportional parameter during the sampling process. Experimental results show that the reserved data after screening can be used in several reconstruction algorithms, and the reconstruction quality is enhanced or at least remains at the same level. Meanwhile, the computing time costs are greatly reduced, and so is the data storage. [ABSTRACT FROM AUTHOR]

An iterative pseudoinverse ghost imaging (IPGI) method is proposed based on iterative denoising and pseudoinverse ghost imaging (PGI). The background noise in the imaging is eliminated in iterations by setting an appropriate threshold. The IPGI method provides a significantly larger enhancement of the peak signal-to-noise ratio (PSNR) than the PGI technique for binary objects. Experiments and data analyses are performed to evaluate the performance of the proposed method. Compared with conventional GI, differential GI, and PGI methods, the proposed method has the highest performance in visual effects and significantly improves the imaging quality. For a certain PSNR, the proposed method provides satisfactory performance in terms of computing time.

Noise term in the reconstruction matrix in ghost imaging is a major cause of blurring imaging results. To remedy this problem, we propose a new ghost imaging method based on the binomial theorem to reduce the level of noise. In our method, images with low-level noise can be generated by constructing a binomial formula using high-order imaging results that are acquired by reintroducing the reconstruction result back into the imaging formula repeatedly. Experimental and simulation results demonstrate that our method is effective in improving imaging quality and the anti-interference performance and reducing computing time, making it useful for practical applications.

In iterative pseudo-inverse ghost imaging (IPGI), how much the noise interference item of the current iteration approximates the real noise greatly depends on the clarity of initial image. In order to improve IPGI, we propose a method that introduces anisotropic diffusion to construct a more accurate noise interference term, where anisotropic diffusion adapts to both the image and the noise, so that it balances the tradeoff between noise removal and preservation of image details. In our algorithm, the anisotropic diffusion equation is used to denoise the result of each iteration, then the denoised image is used to construct the noise interference term for the next iteration. Compared to IPGI, our method has better performance in visual effects and imaging quality, as the image edges and details are better preserved according to the experimental results. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to evaluate the efficiency of imaging features and texture analysis (TA) based on baseline rectal MRI for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy (nCRT) and tumor recurrence in patients with locally advanced rectal cancer (LARC). Methods: Consecutive patients with LARC who underwent rectal MRI between January 2014 and December 2015 and surgical resection after completing nCRT were retrospectively enrolled. Imaging features were analyzed, and TA parameters were extracted from the tumor volume of interest (VOI) from baseline rectal MRI. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the optimal TA parameter cutoff values to stratify the patients. Logistic and Cox regression analyses were performed to assess the efficacy of each imaging feature and texture parameter in predicting tumor response and disease-free survival. Results: In total, 78 consecutive patients were enrolled. In the logistic regression, good treatment response was associated with lower tumor location (OR = 13.284, p = 0.012), low Conv_Min (OR = 0.300, p = 0.013) and high Conv_Std (OR = 3.174, p = 0.016), Shape_Sphericity (OR = 3.170, p = 0.015), and Shape_Compacity (OR = 2.779, p = 0.032). In the Cox regression, a greater risk of tumor recurrence was related to higher cT stage (HR = 5.374, p = 0.044), pelvic side wall lymph node positivity (HR = 2.721, p = 0.013), and gray-level run length matrix_long-run low gray-level emphasis (HR = 2.268, p = 0.046). Conclusions: Imaging features and TA based on baseline rectal MRI could be valuable for predicting the treatment response to nCRT for rectal cancer and tumor recurrence. Key Points: • Imaging features and texture parameters of T2-weighted MR images of rectal cancer can help to predict treatment response and the risk for tumor recurrence. • Tumor location as well as conventional and shape indices of texture features can help to predict treatment response for rectal cancer. • Clinical T stage, positive pelvic side wall lymph nodes, and the high-order texture parameter, GLRLM_LRLGE, can help to predict tumor recurrence for rectal cancer. [ABSTRACT FROM AUTHOR]

Real-time small infrared (IR) target detection is critical to the performance of the situational awareness system in high-altitude aircraft. However, current IR target detection systems are generally hardware-unfriendly and have difficulty in achieving a robust performance in datasets with clouds occupying a large proportion of the image background. In this paper, we present new results by using an efficient method that extracts the candidate targets in the pre-processing stage and fuses the local scale, blob-based contrast map and gradient map in the detection stage. We also developed mid-wave infrared (MWIR) and long-wave infrared (LWIR) cameras for data collection experiments and algorithm evaluations. Experimental results using both publicly available datasets and image sequences acquired by our cameras clearly demonstrated that the proposed method achieves high detection accuracy with the mean AUC being at least 22.3% higher than comparable methods, and the computational cost beating the other methods by a large margin.

We studied collagen fiber organization in tissue affected by pelvic organ prolapse (POP) and compared it to asymptomatic controls. Both the control and POP tissue biopsies were prepared and measured by a highly sensitive quantitative phase imaging (QPI) system, called spatial light interference microscopy (SLIM). Combined with automatic image processing, this modality provides quantitative, high-throughput assessment of fiber morphology. We found the fiber orientation in prolapsed specimens is less homogeneous, indicating an abnormal organization of collagen in the extracellular matrix (ECM).

Objective . This paper addresses performing inverse radon transform (IRT) with artificial neural network (ANN) or deep learning, simultaneously with cardiac motion correction (MC). The suggested application domain is cardiac image reconstruction in emission or transmission tomography where IRT is relevant. Our main contribution is in proposing an ANN architecture that is particularly suitable for this purpose. Approach . We validate our approach with two types of datasets. First, we use an abstract object that looks like a heart to simulate motion-blurred radon transform. With the known ground truth in hand, we then train our proposed ANN architecture and validate its effectiveness in MC. Second, we used human cardiac gated datasets for training and validation of our approach. The gating mechanism bins data over time using the electro-cardiogram (ECG) signals for cardiac motion correction. Main results . We have shown that trained ANNs can perform motion-corrected image reconstruction directly from a motion-corrupted sinogram. We have compared our model against two other known ANN-based approaches. Significance . Our method paves the way for eliminating any need for hardware gating in medical imaging., (© 2024 Institute of Physics and Engineering in Medicine.)

This paper mainly focuses on development of the smart crack inspection algorithm facilitating the through-thickness crack during the hole expansion test, which makes it possible to calculate the hole expansion ratio, automatically, with the image processing technique. The proposed crack inspection algorithm consists of six steps such as binarization, blob detection, background deletion, ROI selection, image linearization, and crack identification using the C# language. This algorithm is able to capture the various types of the through-thickness and double cracks irrespective of the reflectance and the initial thickness of the applied sheet materials. In addition, it is possible to keep trace of the in-plane crack and its propagation during the HER test. [ABSTRACT FROM AUTHOR]

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Phase wrapping is a crucial issue in Doppler optical coherence tomography (OCT) and restricts its automatic implementation for clinical applications that quantify total retinal blood flow. We propose an automated phaseunwrapping technique that takes advantage of the parabolic profile of blood flow velocity in vessels. Instead of inspecting the phase shift manually, the algorithm calculates the gradient magnitude of the phase shift on the cross-sectional image and automatically detects the presence of phase wrapping. The voxels affected by phase wrapping are corrected according to the determined flow direction adjacent to the vessel walls. We validated this technique in the rodent retina using a prototype visiblelight OCT and in the human retina with a commercial infrared OCT system. We believe this signal processing method may well accelerate clinical applications of Doppler OCT in ophthalmology. [ABSTRACT FROM AUTHOR]

Noise term in the reconstruction matrix in ghost imaging is a major cause of blurring imaging results. To remedy this problem, we propose a new ghost imaging method based on the binomial theorem to reduce the level of noise. In our method, images with low-level noise can be generated by constructing a binomial formula using high-order imaging results that are acquired by reintroducing the reconstruction result back into the imaging formula repeatedly. Experimental and simulation results demonstrate that our method is effective in improving imaging quality and the anti-interference performance and reducing computing time, making it useful for practical applications. [ABSTRACT FROM AUTHOR]

Most ghost imaging reconstruction algorithms require a large measurement time to retrieve the object information clearly. But not all groups of data play a positive role in reconstructing the object image. Abandoning some redundant data can not only enhance the quality of reconstruction images but also speed up the computation process. Here, we propose a method to screen the data using two threshold values set by a proportional parameter during the sampling process. Experimental results show that the reserved data after screening can be used in several reconstruction algorithms, and the reconstruction quality is enhanced or at least remains at the same level. Meanwhile, the computing time costs are greatly reduced, and so is the data storage.

The Editor-in-Chief and the publisher have retracted this article, which was submitted as part of a guest-edited special section. An investigation uncovered evidence of systematic manipulation of the publication process, including compromised peer review. The Editor and publisher no longer have confidence in the results and conclusions of the article. RD, KKH, BPD, and AKV either did not respond directly or could not be reached. [ABSTRACT FROM AUTHOR]

In iterative pseudo-inverse ghost imaging (IPGI), how much the noise interference item of the current iteration approximates the real noise greatly depends on the clarity of initial image. In order to improve IPGI, we propose a method that introduces anisotropic diffusion to construct a more accurate noise interference term, where anisotropic diffusion adapts to both the image and the noise, so that it balances the tradeoff between noise removal and preservation of image details. In our algorithm, the anisotropic diffusion equation is used to denoise the result of each iteration, then the denoised image is used to construct the noise interference term for the next iteration. Compared to IPGI, our method has better performance in visual effects and imaging quality, as the image edges and details are better preserved according to the experimental results.

An iterative pseudoinverse ghost imaging (IPGI) method is proposed based on iterative denoising and pseudoinverse ghost imaging (PGI). The background noise in the imaging is eliminated in iterations by setting an appropriate threshold. The IPGI method provides a significantly larger enhancement of the peak signal-to-noise ratio (PSNR) than the PGI technique for binary objects. Experiments and data analyses are performed to evaluate the performance of the proposed method. Compared with conventional GI, differential GI, and PGI methods, the proposed method has the highest performance in visual effects and significantly improves the imaging quality. For a certain PSNR, the proposed method provides satisfactory performance in terms of computing time. [ABSTRACT FROM AUTHOR]

Complete three-dimensional (3D) land seismic data are often difficult to acquire due to physical or financial limitations. Therefore, in recent years, interpolation techniques with 3D field data have played an important role in seismic data processing. To improve the efficiency of interpolation for insufficient 3D field data, we developed a new interpolation process that applies two-dimensional curvelet transform-based projection onto convex sets (POCS) to the kx-ky transformed data of each time slice of 3D data. Additionally, to acquire accurate interpolation results with the proposed new method, we designed the preparation process to render the input data irregularly distributed with small-sized gaps. We found that the interpolation results of our proposed new method were similar to those using a 3D curvelet transform-based POCS method, with reduced computational cost. The quality of the stacked images of sparsely sampled 3D field data was significantly enhanced by applying our interpolation approach. [ABSTRACT FROM AUTHOR]