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Статті в журналах з теми "Intelligent imaging"

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Автор: Grafiati

Опубліковано: 7 липня 2024

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1

Currie, Geoffrey M. "Intelligent Imaging: Artificial Intelligence Augmented Nuclear Medicine." Journal of Nuclear Medicine Technology 47, no. 3 (August 10, 2019): 217–22. http://dx.doi.org/10.2967/jnmt.119.232462.

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2

Li, Xiang, and Donggang He. "Image Processing and Recognition Algorithm Design in Intelligent Imaging Device System." Security and Communication Networks 2022 (May 19, 2022): 1–10. http://dx.doi.org/10.1155/2022/9669903.

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People may quickly employ imagig devices to acquire and use image data thanks to the rapid development of computer networks and communication technologies. However, imaging devices obtain massive data through real-time acquisition, and a large number of invalid images affect the imaging device system’s endurance on the one hand while also requiring a significant amount of time for analysis on the other hand, so there is a critical need to find a way to automate the mining of valuable information in the data. In this paper, we propose an intelligent imaging device system, which embeds a target intelligent recognition algorithm, improves the YOLOv3 model by using a method based on depth-separable convolutional blocks and inverse feature fusion structure, and finally achieves fast target detection while improving detection accuracy through the design of distance-based nonextreme suppression and loss function. By preprocessing the images and automatically identifying and saving images containing target animals, the range of the imaging device system equipment can be improved and the workload of researchers searching for target animals in images can be reduced. In this paper, we propose a method for intelligent preservation of contained target images by deploying lightweight target recognition algorithms on edge computing hardware with the intelligence of the intelligent imaging device system as the research goal. After simulation experiments, the use of this method can improve the endurance of the imaging device system and reduce the time of manual processing at a later stage.

3

Fyfe, Ian. "Intelligent, mobile stroke imaging." Nature Reviews Neurology 18, no. 2 (December 21, 2021): 66. http://dx.doi.org/10.1038/s41582-021-00614-5.

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4

Griffiths, J., G. Royle, C. Esbrand, G. Hall, R. Turchetta, and R. Speller. "I-ImaS: intelligent imaging sensors." Journal of Instrumentation 5, no. 08 (August 3, 2010): C08001. http://dx.doi.org/10.1088/1748-0221/5/08/c08001.

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5

Kotera, Hiroaki. "Overview Paper: Intelligent color imaging." Journal of the Society for Information Display 14, no. 9 (2006): 745. http://dx.doi.org/10.1889/1.2358568.

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6

Ying, Kui, Xinyu Yu, Jiana Shen, Shilu Zhang, and Yuanyue Guo. "Intelligent Microwave Staring Correlated Imaging." Progress In Electromagnetics Research 176 (2023): 109–28. http://dx.doi.org/10.2528/pier22091907.

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7

Alexander, Alan, Adam Jiang, Cara Ferreira, and Delphine Zurkiya. "An Intelligent Future for Medical Imaging: A Market Outlook on Artificial Intelligence for Medical Imaging." Journal of the American College of Radiology 17, no. 1 (January 2020): 165–70. http://dx.doi.org/10.1016/j.jacr.2019.07.019.

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8

Saigre-Tardif, Chloé, Rashid Faqiri, Hanting Zhao, Lianlin Li, and Philipp del Hougne. "Intelligent meta-imagers: From compressed to learned sensing." Applied Physics Reviews 9, no. 1 (March 2022): 011314. http://dx.doi.org/10.1063/5.0076022.

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Computational meta-imagers synergize metamaterial hardware with advanced signal processing approaches such as compressed sensing. Recent advances in artificial intelligence (AI) are gradually reshaping the landscape of meta-imaging. Most recent works use AI for data analysis, but some also use it to program the physical meta-hardware. The role of “intelligence” in the measurement process and its implications for critical metrics like latency are often not immediately clear. Here, we comprehensively review the evolution of computational meta-imaging from the earliest frequency-diverse compressive systems to modern programmable intelligent meta-imagers. We introduce a clear taxonomy in terms of the flow of task-relevant information that has direct links to information theory: compressive meta-imagers indiscriminately acquire all scene information in a task-agnostic measurement process that aims at a near-isometric embedding; intelligent meta-imagers highlight task-relevant information in a task-aware measurement process that is purposefully non-isometric. The measurement process of intelligent meta-imagers is, thus, simultaneously an analog wave processor that implements a first task-specific inference step “over-the-air.” We provide explicit design tutorials for the integration of programmable meta-atoms as trainable physical weights into an intelligent end-to-end sensing pipeline. This merging of the physical world of metamaterial engineering and the digital world of AI enables the remarkable latency gains of intelligent meta-imagers. We further outline emerging opportunities for cognitive meta-imagers with reverberation-enhanced resolution, and we point out how the meta-imaging community can reap recent advances in the vibrant field of metamaterial wave processors to reach the holy grail of low-energy ultra-fast all-analog intelligent meta-sensors.

9

Wang, Qianqian. "Imaging-Guided Micromachines: Towards Intelligent Systems." Micromachines 13, no. 11 (November 18, 2022): 2016. http://dx.doi.org/10.3390/mi13112016.

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Micromachines with controllable motion, deformation, and collective behaviors provide advanced methods for performing tasks that traditional machines have difficulty completing thanks to the development of small-scale robotics, nanotechnology, biocompatible materials, and imaging techniques [...]

10

Ren, Hongfei. "Optimization of Lung CT Image Processing and Recognition Based on E-SRG Segmentation Algorithm." BIO Web of Conferences 59 (2023): 03002. http://dx.doi.org/10.1051/bioconf/20235903002.

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Intelligent algorithms such as deep learning and parallel processing technologies such as mobile clouds are constantly evolving, heralding a new era of intelligence. In the new historical period, the development of intelligent medicine is facing great challenges and opportunities. In traditional medicine, medical imaging includes medical imaging and pathological imaging, which is an important reference for doctors in disease diagnosis. Image processing and recognition, as one of the key technologies of computer vision, must be improved under the premise of meeting the needs in practical applications. Therefore, according to the unique pathological characteristics of medical images, combined with the real-time and accuracy of images, the auxiliary diagnosis of images is the need of the development of intelligent medicine. The preprocessing technique and E-SRG algorithm used in this paper can improve the quality of images without being limited by the size of the dataset, and realize the complete segmentation of organs and tissues.

11

Randazzo, Andrea. "Swarm Optimization Methods in Microwave Imaging." International Journal of Microwave Science and Technology 2012 (October 24, 2012): 1–12. http://dx.doi.org/10.1155/2012/491713.

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Swarm intelligence denotes a class of new stochastic algorithms inspired by the collective social behavior of natural entities (e.g., birds, ants, etc.). Such approaches have been proven to be quite effective in several applicative fields, ranging from intelligent routing to image processing. In the last years, they have also been successfully applied in electromagnetics, especially for antenna synthesis, component design, and microwave imaging. In this paper, the application of swarm optimization methods to microwave imaging is discussed, and some recent imaging approaches based on such methods are critically reviewed.

12

Do, Hyeonsu, Colin Yoon, Yunbo Liu, Xintao Zhao, John Gregg, Ancheng Da, Younggeun Park, and Somin Eunice Lee. "Intelligent Fusion Imaging Photonics for Real-Time Lighting Obstructions." Sensors 23, no. 1 (December 28, 2022): 323. http://dx.doi.org/10.3390/s23010323.

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Dynamic detection in challenging lighting environments is essential for advancing intelligent robots and autonomous vehicles. Traditional vision systems are prone to severe lighting conditions in which rapid increases or decreases in contrast or saturation obscures objects, resulting in a loss of visibility. By incorporating intelligent optimization of polarization into vision systems using the iNC (integrated nanoscopic correction), we introduce an intelligent real-time fusion algorithm to address challenging and changing lighting conditions. Through real-time iterative feedback, we rapidly select polarizations, which is difficult to achieve with traditional methods. Fusion images were also dynamically reconstructed using pixel-based weights calculated in the intelligent polarization selection process. We showed that fused images by intelligent polarization selection reduced the mean-square error by two orders of magnitude to uncover subtle features of occluded objects. Our intelligent real-time fusion algorithm also achieved two orders of magnitude increase in time performance without compromising image quality. We expect intelligent fusion imaging photonics to play increasingly vital roles in the fields of next generation intelligent robots and autonomous vehicles.

13

Chen, Zhou, Yan Lou, Baoming Wang, Han Lei, and Peiyuan Yang. "Application of Cloud-Driven Intelligent Medical Imaging Analysis in Disease Detection." Journal of Theory and Practice of Engineering Science 4, no. 05 (May 23, 2024): 64–71. http://dx.doi.org/10.53469/jtpes.2024.04(05).09.

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The integration of cloud computing and artificial intelligence (AI) in medical imaging has revolutionized disease detection and diagnosis. This paper explores the application of cloud-driven intelligent medical imaging analysis, highlighting its ability to manage and utilize massive datasets efficiently. By leveraging the vast storage capacities, high computing power, and robust data security of cloud computing, coupled with the precision and speed of AI algorithms, medical imaging processes are significantly enhanced. The development of intelligent systems enables real-time data access, remote consultations, and mobile solutions, which improve the accessibility and availability of medical resources. This synergy not only alleviates the workload on radiologists but also supports better patient outcomes through advanced diagnostic capabilities and seamless collaboration across medical institutions. The ongoing advancements in these technologies promise to drive further innovation and enhance the quality of healthcare services.

14

Totero Gongora, Juan S., Luana Olivieri, Luke Peters, Jacob Tunesi, Vittorio Cecconi, Antonio Cutrona, Robyn Tucker, Vivek Kumar, Alessia Pasquazi, and Marco Peccianti. "Route to Intelligent Imaging Reconstruction via Terahertz Nonlinear Ghost Imaging." Micromachines 11, no. 5 (May 20, 2020): 521. http://dx.doi.org/10.3390/mi11050521.

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Terahertz (THz) imaging is a rapidly emerging field, thanks to many potential applications in diagnostics, manufacturing, medicine and material characterisation. However, the relatively coarse resolution stemming from the large wavelength limits the deployment of THz imaging in micro- and nano-technologies, keeping its potential benefits out-of-reach in many practical scenarios and devices. In this context, single-pixel techniques are a promising alternative to imaging arrays, in particular when targeting subwavelength resolutions. In this work, we discuss the key advantages and practical challenges in the implementation of time-resolved nonlinear ghost imaging (TIMING), an imaging technique combining nonlinear THz generation with time-resolved time-domain spectroscopy detection. We numerically demonstrate the high-resolution reconstruction of semi-transparent samples, and we show how the Walsh–Hadamard reconstruction scheme can be optimised to significantly reduce the reconstruction time. We also discuss how, in sharp contrast with traditional intensity-based ghost imaging, the field detection at the heart of TIMING enables high-fidelity image reconstruction via low numerical-aperture detection. Even more striking—and to the best of our knowledge, an issue never tackled before—the general concept of “resolution” of the imaging system as the “smallest feature discernible” appears to be not well suited to describing the fidelity limits of nonlinear ghost-imaging systems. Our results suggest that the drop in reconstruction accuracy stemming from non-ideal detection conditions is complex and not driven by the attenuation of high-frequency spatial components (i.e., blurring) as in standard imaging. On the technological side, we further show how achieving efficient optical-to-terahertz conversion in extremely short propagation lengths is crucial regarding imaging performance, and we propose low-bandgap semiconductors as a practical framework to obtain THz emission from quasi-2D structures, i.e., structure in which the interaction occurs on a deeply subwavelength scale. Our results establish a comprehensive theoretical and experimental framework for the development of a new generation of terahertz hyperspectral imaging devices.

15

Currie, Geoff, K. Elizabeth Hawk, Eric Rohren, Alanna Vial, and Ran Klein. "Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging." Journal of Medical Imaging and Radiation Sciences 50, no. 4 (December 2019): 477–87. http://dx.doi.org/10.1016/j.jmir.2019.09.005.

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16

Park, Jihwan, Mi Jung Rho, Yong Hyun Park, Chan Kwon Jung, Yosep Chong, Choung-Soo Kim, Heounjeong Go, et al. "PROMISE CLIP Project: A Retrospective, Multicenter Study for Prostate Cancer that Integrates Clinical, Imaging and Pathology Data." Applied Sciences 9, no. 15 (July 25, 2019): 2982. http://dx.doi.org/10.3390/app9152982.

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There are many medical demands that still need to be resolved for prostate cancer (PCa), including better diagnosis and predictive medicine. For this to be accomplished, diverse medical data need to be integrated with the development of intelligent software (SW) based on various types of medical data. Various types of information technology have been used to address these medical demands of PCa. We initiated the PROstate Medical Intelligence System Enterprise-Clinical, Imaging, and Pathology (PROMISE CLIP) and a multicenter, big data study to develop PCa SW for patients with PCa and clinicians. We integrated the clinical data of 7257 patients, 610 patients’ imaging data, and 39,000 cores of pathology digital scanning data from four tertiary hospitals in South Korea. We developed the PROMISE CLIP registry based on integrated clinical, imaging, and pathology data. Related intelligent SW has been developed for helping patients and clinicians decide on the best treatment option. The PROMISE CLIP study directs guidelines for intelligent SW development to solve medical demands for PCa. The PROMISE CLIP registry plays an important role in advancing PCa research and care.

17

Hwang, Dosik, and DaeEun Kim. "Special Features on Intelligent Imaging and Analysis." Applied Sciences 9, no. 22 (November 10, 2019): 4804. http://dx.doi.org/10.3390/app9224804.

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18

Satyanarayan Kanungo, Tolu Adedoja, Sourabh Sharma, Suresh Dodda, Suman Narne, Sathishkumar Chintala,. "Exploring AI-driven Innovations in Image Communication Systems for Enhanced Medical Imaging Applications." Journal of Electrical Systems 20, no. 3s (April 4, 2024): 949–59. http://dx.doi.org/10.52783/jes.1409.

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Artificial intelligence (AI) has emerged as a promising avenue for enhancing medical imaging systems and improving clinical workflows. This research explores innovative applications of AI and deep learning for image communication networks in healthcare. Specifically, we develop an intelligent image compression framework that optimizes data transmission and speeds interpretation of radiology scans. Our approach combines convolutional neural networks, generative adversarial networks, and specialized image filters to balance communication efficiency, diagnostic accuracy, and system latency. Rigorous experiments validate superior performance over traditional methods and commercial products across modalities including MRI, CT, and ultrasound. Crucially, the proposed methods demonstrate expert-level precision in anatomy labeling and pathology detection. By intelligently streamlining image transfer and analytics, this AI-powered system could facilitate ubiquitous, real-time diagnostics via telemedicine. Enhanced connectivity between imaging devices and clinical specialists can improve patient outcomes and reduce healthcare costs. Our solutions set the stage for more advanced AI integration in imaging networks and data-intensive medicine

19

Currie, Geoffrey M. "Intelligent Imaging: Developing a Machine Learning Project." Journal of Nuclear Medicine Technology 49, no. 1 (December 24, 2020): 44–48. http://dx.doi.org/10.2967/jnmt.120.256628.

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20

Berg, Hans E. "Will intelligent machine learning revolutionize orthopedic imaging?" Acta Orthopaedica 88, no. 6 (November 2, 2017): 577. http://dx.doi.org/10.1080/17453674.2017.1387732.

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21

Lindop, Joel E., Graham M. Treece, Andrew H. Gee, and Richard W. Prager. "An Intelligent Interface for Freehand Strain Imaging." Ultrasound in Medicine & Biology 34, no. 7 (July 2008): 1117–28. http://dx.doi.org/10.1016/j.ultrasmedbio.2007.12.012.

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22

李, 洪涛. "Ophthalmic Imaging Method Based on Intelligent Terminal." Computer Science and Application 08, no. 08 (2018): 1224–38. http://dx.doi.org/10.12677/csa.2018.88134.

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23

van Moolenbroek, Guido T., Tania Patiño, Jordi Llop, and Samuel Sánchez. "Engineering Intelligent Nanosystems for Enhanced Medical Imaging." Advanced Intelligent Systems 2, no. 10 (July 21, 2020): 2000087. http://dx.doi.org/10.1002/aisy.202000087.

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24

Hao, Jiaxing, Xuetian Wang, Sen Yang, and Hongmin Gao. "Intelligent Simulation Technology Based on RCS Imaging." Applied Sciences 13, no. 18 (September 8, 2023): 10119. http://dx.doi.org/10.3390/app131810119.

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The target simulation of airplanes is an important research topic. It is particularly important to find the right balance between high performance and low cost. In order to balance the contradictions between realistic target simulations and controllable costs, the scientific formulation of the performance parameters of target simulation is the key to achieving high performance. This paper proposes an intelligent simulation technology based on RCS imaging simulation through the combination of 60° variation corner reflector and a Luneberg lens reflector. It is designed to simulate several important RCS characteristics of the aircraft. At the same time, the different RCS images are automatically shifted to the corresponding gear position to achieve the purpose of simulation, and the price is low and the performance is good. It can be used for the training of radar target searching.

25

Jiao, Jingjie, Lixing Zhao, Wenhao Pan, and Xiaoyan Li. "Development and Core Technologies for Intelligent SWaP3 Infrared Cameras: A Comprehensive Review and Analysis." Sensors 23, no. 9 (April 22, 2023): 4189. http://dx.doi.org/10.3390/s23094189.

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With the development of infrared detection and imaging technology, infrared cameras (IRCs) play an important role in many fields, such as military, industry, and civilian. Additionally, the requirements for the size, performance, and intelligence of IRCs are becoming more and more strict. Consequently, the associated research and development (R&D) of IRCs is gradually focused on the aspects of miniaturization, high performance, intelligence, low power consumption, and low cost, involving many frontier fields, including artificial intelligence, new materials, new optical systems, and electronics systems. In fact, there are continual studies on intelligent SWaP3 IRCs, but unfortunately, a systematic arrangement and analysis are lacking. Therefore, a systematical and comprehensive review for the developments and core technologies of the intelligent SWaP3 IRCs is really needed. In this paper, in terms of the aforementioned requirements, we conduct a review and analysis of current intelligent SWaP3 IRCs based on 90 literature and statistics in recent decades to provide the relevant developers with a helpful reference for facilitating the indicator optimization of intelligent SWaP3 IRCs with new developed technologies. We analyze the development of SWaP3 IRCs in the aspects of lightweight, miniaturization, low price, and high performance, including hyperspectral resolution, high spatial resolution, large field of view (FOV), and wide dynamic elaborately. Moreover, the development in low power consumption and intelligence is also discussed in detail. Additionally, we briefly summarize the primary applications of intelligent SWaP3 IRCs in military, scientific, and civil. Then, the core technologies comprising high-integration, lightweight, hyperspectral imaging (HSI), low-power consumption, as well as the realization of high performance such as high-resolution, high-frame, and wide-dynamic range of SWaP3 IRCs are discussed and analyzed in detail. Finally, we prospect for the intelligent SWaP3 IRCs that it is necessary to continuously expand the concept of SWaP3 by reliability, stability, extensibility, and safety. In addition, it is useful to embed cutting-edge technologies such as small pixel pitch array, multi-sensors fusion, and deploy intelligent algorithms to IRCs. Additionally, the improvement of the whole machine from multi-dimension such as chip, camera, and system is expected and needs to be taken more seriously. It is hoped that this paper can provide a reference for the R&D of intelligent SWaP3 IRCs in the future.

26

Clark, Natalie. "Intelligent Optical Systems Using Adaptive Optics." Advances in Science and Technology 82 (September 2012): 64–74. http://dx.doi.org/10.4028/www.scientific.net/ast.82.64.

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Until recently, the phrase adaptive optics generally conjured images of large deformable mirrors being integrated into telescopes to compensate for atmospheric turbulence. However, the development of smaller, cheaper devices has sparked interest for other aerospace and commercial applications. Variable focal length lenses, liquid crystal spatial light modulators, tunable filters, phase compensators, polarization compensation, and deformable mirrors are becoming increasingly useful for other imaging applications included guidance navigation and control (GNC), coronagraphs, foveated imaging, situational awareness, autonomous rendezvous and docking, non-mechanical zoom, phase diversity, and enhanced multi-spectral imaging. Active components presented allow flexibility in the optical design, increasing performance. In addition, the intelligent optical systems presented offer advantages in size and weight and radiation tolerance.

27

Kim, Sung, Mitsuru Hattori, and Takeaki Ozawa. "Intelligent Design of Nano-Scale Molecular Imaging Agents." International Journal of Molecular Sciences 13, no. 12 (December 12, 2012): 16986–7005. http://dx.doi.org/10.3390/ijms131216986.

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28

Mehedi, Ibrahim M., K. Prahlad Rao, Ubaid M. Al-Saggaf, Hadi Mohsen Alkanfery, Maamar Bettayeb, and Rahtul Jannat. "Intelligent Tomographic Microwave Imaging for Breast Tumor Localization." Mathematical Problems in Engineering 2022 (May 25, 2022): 1–9. http://dx.doi.org/10.1155/2022/4090351.

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Researchers are continuously exploring the potential use of microwave imaging in the early detection of breast cancer. The technique offers a promising alternative to mammography, a standard clinical imaging procedure today. The contrast in dielectric properties between normal and cancerous tissues makes microwave imaging a viable technique for detecting breast cancer. Experimental results are presented in this paper that demonstrate the detection of breast cancer using microwaves operating at 2.4 GHz. The procedure involves antenna fabrication, phantom tissue development, and image reconstruction. Design and fabrication of patch antenna are used in the study, described in detail. The patch antenna pair is used for transmitting and receiving source waves. Tissue mimicking models were developed from paraffin wax and glycerin for the dielectric constants of 9 and 47, respectively, representing the tissue and tumor. Further, AI-based tomographic images were obtained by implementing a filtered back-projection algorithm in the computer. In the results, the presence of the tumor is quantitatively analyzed.

29

赵, 伟. "Design of Light and Small Intelligent Imaging System." Optoelectronics 08, no. 04 (2018): 158–67. http://dx.doi.org/10.12677/oe.2018.84021.

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30

Marques Godinho, Tiago, Carlos Costa, and José Luís Oliveira. "Intelligent generator of big data medical imaging repositories." IET Software 11, no. 3 (June 2017): 100–104. http://dx.doi.org/10.1049/iet-sen.2016.0191.

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31

Allinson, N., T. Anaxagoras, J. Aveyard, C. Arvanitis, R. Bates, A. Blue, S. Bohndiek, et al. "The Multidimensional Integrated Intelligent Imaging project (MI-3)." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 604, no. 1-2 (June 2009): 196–98. http://dx.doi.org/10.1016/j.nima.2009.01.112.

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32

Turner, Philip. "Selective and intelligent imaging using digital evidence bags." Digital Investigation 3 (September 2006): 59–64. http://dx.doi.org/10.1016/j.diin.2006.06.003.

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33

Andoga, Rudolf, Ladislav Főző, Martin Schrötter, Marek Češkovič, Stanislav Szabo, Róbert Bréda, and Michal Schreiner. "Intelligent Thermal Imaging-Based Diagnostics of Turbojet Engines." Applied Sciences 9, no. 11 (May 31, 2019): 2253. http://dx.doi.org/10.3390/app9112253.

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There are only a few applications of infrared thermal imaging in aviation. In the area of turbojet engines, infrared imaging has been used to detect temperature field anomalies in order to identify structural defects in the materials of engine casings or other engine parts. In aviation applications, the evaluation of infrared images is usually performed manually by an expert. This paper deals with the design of an automatic intelligent system which evaluates the technical state and diagnoses a turbojet engine during its operation based on infrared thermal (IRT) images. A hybrid system interconnecting a self-organizing feature map and an expert system is designed for this purpose. A Kohonen neural network (the self-organizing feature map) is successfully applied to segment IRT images of a turbojet engine with high precision, and the expert system is then used to create diagnostic information from the segmented images. This paper represents a proof of concept of this hybrid system using data from a small iSTC-21v turbojet engine operating in laboratory conditions.

34

Karim, Karim S., Mohammed Hadi Izadi, Farhad Taghibakhsh, and Golnaz Sanaie. "Intelligent Pixel Architectures for Digital Medical Imaging Applications." ECS Transactions 8, no. 1 (December 19, 2019): 289–93. http://dx.doi.org/10.1149/1.2767322.

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35

Zhang, Fangzheng, Cheng Lei, Chun‐Jung Huang, Hirofumi Kobayashi, Chia‐Wei Sun, and Keisuke Goda. "Intelligent Image De‐Blurring for Imaging Flow Cytometry." Cytometry Part A 95, no. 5 (April 22, 2019): 549–54. http://dx.doi.org/10.1002/cyto.a.23771.

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36

Chen, ZhiQiang, and Jianfei Chen. "Mobile Imaging and Computing for Intelligent Structural Damage Inspection." Advances in Civil Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/483729.

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Optical imaging is a commonly used technique in civil engineering for aiding the archival of damage scenes and more recently for image analysis-based damage quantification. However, the limitations are evident when applying optical imaging in the field. The most significant one is the lacking of computing and processing capability in the real time. The advancement of mobile imaging and computing technologies provides a promising opportunity to change this norm. This paper first provides a timely introduction of the state-of-the-art mobile imaging and computing technologies for the purpose of engineering application development. Further we propose a mobile imaging and computing (MIC) framework for conducting intelligent condition assessment for constructed objects, which features in situ imaging and real-time damage analysis. This framework synthesizes advanced mobile technologies with three innovative features: (i) context-enabled image collection, (ii) interactive image preprocessing, and (iii) real-time image analysis and analytics. Through performance evaluation and field experiments, this paper demonstrates the feasibility and efficiency of the proposed framework.

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Santacruz-Yaah, Xaira Elena. "Application of Artificial Intelligence to Ultrasonography." Science Insights 41, no. 1 (June 30, 2022): 577–81. http://dx.doi.org/10.15354/si.22.re069.

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The use of artificial intelligence (AI) technology in medicine has gained considerable attention, although its application in ultrasound medicine is still in its infancy. Deep learning, the main algorithm of AI technology, can be applied to intelligent ultrasound picture detection and classification. Describe the application status of AI in ultrasound imaging, including thyroid, breast, and liver disease applications. The merging of AI and ultrasound imaging can increase the accuracy and specificity of ultrasound diagnosis and decrease the percentage of incorrect diagnoses.

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Wang, Pin, Zhijian Gao, Yimin Li, Lingyu Zeng, and Hongmei Zhong. "Design and Implementation of a Radioactive Source Intelligent Search Robot Based on Artificial Intelligence Edge Computing." Wireless Communications and Mobile Computing 2022 (May 17, 2022): 1–12. http://dx.doi.org/10.1155/2022/3940348.

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Artificial intelligence is a very broad science, which consists of different fields, such as machine learning, and computer vision. In recent years, the world nuclear industry has developed vigorously. At the same time, incidents of loss of radioactive sources also occur from time to time. At present, most of the search for radioactive sources adopt manual search, which is inefficient, and the searchers are vulnerable to radiation damage. Sending a robot to the search an area where there may be an uncontrolled radioactive source is different. Not only does it improve efficiency, it also protects people from radiation. Therefore, it is of great practical significance to design a radioactive source search robot. This paper mainly introduces the design and implementation of a radioactive source intelligent search robot based on artificial intelligence edge computing, aiming to provide some ideas and directions for the research of radioactive source intelligent search robot. In this paper, a research method for the design and implementation of a radioactive source intelligent search robot based on artificial intelligence edge computing is proposed, including intelligent edge computing and gamma-ray imaging algorithms, which are used to carry out related experiments on the design and implementation of radioactive sources, an intelligent search robot based on edge computing. The experimental results of this paper show that the average resolution of the radioactive source search robot is 90.55%, and the resolution results are more prominent.

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Wen, Qiaonong, and Shuang Xu. "Applications of Ultrasound Targeted Micro/Nano Probes and Intelligent Ultrasound Molecular Imaging Technology." Journal of Biomedical Nanotechnology 19, no. 5 (May 1, 2023): 689–705. http://dx.doi.org/10.1166/jbn.2023.3587.

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Targeted ultrasound molecular probes are the core technology of ultrasound molecular imaging, which connect molecular specific antibodies or ligands of the target tissue to the surface of ultrasound contrast agents, enabling ultrasound microbubbles to actively bind to the target tissue, thereby observing the specific imaging of the target tissue at the cellular or molecular level, reflecting the changes in the diseased tissue at the cellular or molecular level. Ultrasound molecular imaging has rapidly developed and applied in the diagnosis and treatment of breast, thyroid, cardiovascular and other diseases, as well as targeted drug delivery and targeted physical therapy of tumors. This article focuses on the theoretical innovation and technological progress of ultrasound micro/nano targeted probes, key technologies of ultrasound molecular imaging, new ultrasound imaging technologies, and the application of ultrasound micro/nano target bubbles in recent years. The integration of multifunctional micro/nano bubbles and multimodal molecular imaging, as well as diagnosis and treatment, is the development trend of ultrasound molecular probes. Artificial intelligence technology will serve as a basic tool to provide technical support for intelligent ultrasound molecular probes and molecular imaging.

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Bahl, Manisha. "Artificial Intelligence: A Primer for Breast Imaging Radiologists." Journal of Breast Imaging 2, no. 4 (June 19, 2020): 304–14. http://dx.doi.org/10.1093/jbi/wbaa033.

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Abstract Artificial intelligence (AI) is a branch of computer science dedicated to developing computer algorithms that emulate intelligent human behavior. Subfields of AI include machine learning and deep learning. Advances in AI technologies have led to techniques that could increase breast cancer detection, improve clinical efficiency in breast imaging practices, and guide decision-making regarding screening and prevention strategies. This article reviews key terminology and concepts, discusses common AI models and methods to validate and evaluate these models, describes emerging AI applications in breast imaging, and outlines challenges and future directions. Familiarity with AI terminology, concepts, methods, and applications is essential for breast imaging radiologists to critically evaluate these emerging technologies, recognize their strengths and limitations, and ultimately ensure optimal patient care.

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Zhao, Jing Jing, Ji Xiang Sun, Shi Lin Zhou, and Lei Hu. "Imaging of Transmission Equipment Based on Block Compressed Sensing." Applied Mechanics and Materials 190-191 (July 2012): 998–1001. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.998.

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Imaging the overhead transmission equipment with high-resolution is very important to intelligent inspection, which is the prerequisites for fault diagnose. The intelligent inspection system often takes traditional imaging process of data acquisition followed by compression, which leads to the waste of image data and memory resources. We adopt an imaging method based on block compressed sensing to image the transmission equipment, the simulation results show that even if we only compressively sampled with 12.5% of the fully acquired image data, the image still can be recovered with high quality.

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Zhao, Junling. "Robot Structural Optimization Based on Computer Intelligent Image." Wireless Communications and Mobile Computing 2022 (October 12, 2022): 1–6. http://dx.doi.org/10.1155/2022/3328986.

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In order to solve the problems of low degree of automation, difficult identification of picking objects, and large picking damage in traditional fruit and vegetable picking operations, the author proposes a robot structure optimization method based on computer intelligent images. This method introduces the computer imaging technology, examines the principle of imaging the computer imaging technology as the in-depth study of the computer imaging technology, completes the mechanical design of the selected robot, and optimizes all hardware models of choice robot. At the same time, the computer image acquisition system, image acquisition module, and execution module are designed; finally, the computer image information processing flow design of the picking robot is completed, and the simulation experiment of the picking robot is carried out. Experimental results show that in the experiments with 166, 142, and 165 tomato identification numbers, the identification accuracy rates were all over 96%. Conclusion. The picking robot based on computer images has a simple structure, high recognition accuracy of picking targets, less damage to the picking targets, high safety and stability, and great promotion value.

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Hardy, Maryann, and Hugh Harvey. "Artificial intelligence in diagnostic imaging: impact on the radiography profession." British Journal of Radiology 93, no. 1108 (April 2020): 20190840. http://dx.doi.org/10.1259/bjr.20190840.

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The arrival of artificially intelligent systems into the domain of medical imaging has focused attention and sparked much debate on the role and responsibilities of the radiologist. However, discussion about the impact of such technology on the radiographer role is lacking. This paper discusses the potential impact of artificial intelligence (AI) on the radiography profession by assessing current workflow and cross-mapping potential areas of AI automation such as procedure planning, image acquisition and processing. We also highlight the opportunities that AI brings including enhancing patient-facing care, increased cross-modality education and working, increased technological expertise and expansion of radiographer responsibility into AI-supported image reporting and auditing roles.

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Москаленко, В’ячеслав Васильович, Артем Геннадійович Коробов, and Юлія Вікторівна Завгородня. "Intelligent system for classification analysis of myocardial perfusion imaging." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 28 (September 7, 2017): 28–33. http://dx.doi.org/10.20998/2079-0023.2017.28.04.

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Currie, Geoff. "Intelligent Imaging: Anatomy of Machine Learning and Deep Learning." Journal of Nuclear Medicine Technology 47, no. 4 (August 10, 2019): 273–81. http://dx.doi.org/10.2967/jnmt.119.232470.

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Ogunleye, O., D. Koffa, and T. Ahmed. "Preliminary Research for Enabling Intelligent Focus for 3D Imaging." Physical Science International Journal 10, no. 3 (January 10, 2016): 1–9. http://dx.doi.org/10.9734/psij/2016/24645.

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Stayton, P. S., A. S. Hoffman, M. El-Sayed, S. Kulkarni, T. Shimoboji, N. Murthy, V. Bulmus, and C. Lackey. "Intelligent Biohybrid Materials for Therapeutic and Imaging Agent Delivery." Proceedings of the IEEE 93, no. 4 (April 2005): 726–36. http://dx.doi.org/10.1109/jproc.2005.844619.

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Rao, B. P. C., Baldev Raj, T. Jayakumar, and P. Kalyanasundaram. "AN INTELLIGENT IMAGING SCHEME FOR AUTOMATED EDDY CURRENT TESTING." Nondestructive Testing and Evaluation 17, no. 1 (January 2001): 41–57. http://dx.doi.org/10.1080/10589750108953101.

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Wu, Yunzhao, Yuqi Zhou, Chun-Jung Huang, Hirofumi Kobayashi, Sheng Yan, Yasuyuki Ozeki, Yingli Wu, et al. "Intelligent frequency-shifted optofluidic time-stretch quantitative phase imaging." Optics Express 28, no. 1 (January 2, 2020): 519. http://dx.doi.org/10.1364/oe.380679.

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Jazayeri-Rad, H., and M. A. Browne. "Intelligent instrumentation for selective pulse excitation in NMR imaging." Journal of Physics E: Scientific Instruments 20, no. 6 (June 1987): 643–48. http://dx.doi.org/10.1088/0022-3735/20/6/014.

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