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        <title>Frontiers in Imaging | Image Security section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/imaging/sections/image-security</link>
        <description>RSS Feed for Image Security section in the Frontiers in Imaging journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-08-05T07:55:15.157+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2026.1854187</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2026.1854187</link>
        <title><![CDATA[Deep learning for secure imaging and video surveillance in smart cities: from bibliometric mapping to real-world implementations]]></title>
        <pubdate>2026-07-08T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Samad Olalekan Olansile</author><author>Oluwatosin Ahmed Amodu</author><author>Mohamed Sani Adam</author><author>Isaac Oluwafemi Elesemoyo</author><author>Raja Azlina Raja Mahmood</author>
        <description><![CDATA[The integration of imaging sensors, unmanned aerial vehicles (UAVs), and satellite platforms has expanded capabilities in surveillance, smart-city systems, and remote sensing. Advances in deep learning and computer vision enable automated detection, tracking, and scene understanding across diverse environments, including safety-critical settings; however, these systems impose requirements for security, privacy, and adversarial robustness. This paper presents a dual-database bibliometric and science-mapping analysis of deep learning research for secure imaging and video surveillance. Two independently constructed corpora, derived from Scopus (𝒟S) and Web of Science (𝒟W) for the period 2010–early 2026, are analyzed using VOSviewer-based keyword co-occurrence clustering to identify dominant research themes and methodological trends across surveillance, smart cities, remote sensing, and computer vision; annual publication trend interpretation is restricted to complete calendar years from 2010–2025. Quantitative bibliometric analyses (e.g., publication trends, geographic distribution, institutional productivity, and keyword co-occurrence structure) are conducted on both 𝒟S and 𝒟W, with cross-database comparison used for validation. Complementarily, detailed analysis of prominent keywords and inter-cluster connectivity, as well as the review of real-world deployment systems and field-dataset studies, are conducted based on data from the Scopus dataset (𝒟S), which provides detailed author-keyword information for cluster-level interpretation and broader topical coverage. The deployment-oriented review distinguishes physical or edge implementations from studies using real-world datasets. The findings indicate deployment-oriented secure visual intelligence within the mapped literature, with the Scopus cluster structure separating spatio-temporal surveillance understanding, real-time object detection, remote sensing, biometric recognition, edge/cloud smart-city security, re-identification, adversarial robustness, UAV/drone sensing, crowd analytics, event summarization, and low-cost edge implementation. Increased emphasis on UAV-based imaging and adversarial robustness is also observed. This study provides a structured mapping of the research landscape and identifies gaps relevant to the development of reliable imaging systems in connected environments.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2025.1436275</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2025.1436275</link>
        <title><![CDATA[Exploring the correlation of radiomic features of ultrasound images and FNCLCC Grading of soft tissue sarcoma]]></title>
        <pubdate>2025-03-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chenyang Zhao</author><author>Yusen Zhang</author><author>Heng Lv</author><author>Nan Zhuang</author><author>Guangyin Yu</author><author>Yuzhou Shen</author><author>Licong Dong</author><author>Wangjie Wu</author><author>Lu Xie</author><author>Yun Tian</author><author>Zhaoling Yi</author><author>Desheng Sun</author><author>Xingen Wang</author><author>Haiqin Xie</author>
        <description><![CDATA[BackgroundPresurgical evaluation of the histopathological grade of soft tissue sarcoma (STS) is important for enacting treatment strategies. In this study, we plan to investigate the correlation of high-output ultrasound (US) radiomic features and the histopathological grade of STS.MethodsPatients with STS were retrospectively enrolled. The radiomic features were extracted from the US images of the STS lesions. The lesions were graded according to the Fédération Nationale des Centers de Lutte Contre le Cancer (FNCLCC) histopathological grading system. The correlation of the radiomic features and the FNCLCC grades was evaluated. We used the features correlated with the histopathological grades to build a model for predicting high-grade STS (Grade II and III).ResultsA total of 79 patients with STS were enrolled. And 15 radiomic features were found correlated with the FNCLCC grades of STSs, with the correlation coefficient ranging from 0.22 to 0.38. And 8 features showed significant difference among the three grades. The model for predicting high-grade STS based on the 8 radiomic features had an AUC value of 0.80, a sensitivity of 0.73, and a specificity of 0.78.ConclusionThe US radiomic features were correlated with the FNCLCC grade of STS. The radiomic analysis of US imaging could be potentially helpful for identifying the FNCLCC grades of STS pre-surgically.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2024.1393314</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2024.1393314</link>
        <title><![CDATA[Motion history images: a new method for tracking microswimmers in 3D]]></title>
        <pubdate>2024-05-10T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Max Riekeles</author><author>Hadi Albalkhi</author><author>Megan Marie Dubay</author><author>Jay Nadeau</author><author>Christian A. Lindensmith</author>
        <description><![CDATA[Quantitative tracking of rapidly moving micron-scale objects remains an elusive challenge in microscopy due to low signal-to-noise. This paper describes a novel method for tracking micron-sized motile organisms in off-axis Digital Holographic Microscope (DHM) raw holograms and/or reconstructions. We begin by processing the microscopic images with the previously reported Holographic Examination for Life-like Motility (HELM) software, which provides a variety of tracking outputs including motion history images (MHIs). MHIs are stills of videos where the frame-to-frame changes are indicated with color time-coding. This exposes tracks of objects that are difficult to identify in individual frames at a low signal-to-noise ratio. The visible tracks in the MHIs are superior to tracks identified by all tested automated tracking algorithms that start from object identification at the frame level, particularly in low signal-to-noise ratio data, but do not provide quantitative track data. In contrast to other tracking methods, like Kalman filter, where the recording is analyzed frame by frame, MHIs show the whole time span of particle movement at once and eliminate the need to identify objects in individual frames. This feature also enables post-tracking identification of low-SNR objects. We use these tracks, rather than object identification in individual frames, as a basis for quantitative tracking of Bacillus subtilis by first generating MHIs from X, Y, and t stacks (raw holograms or a projection over reconstructed planes), then using a region-tracking algorithm to identify and separate swimming pathways. Subsequently, we identify each object's Z plane of best focus at the corresponding X, Y, and t points, yielding ap full description of the swimming pathways in three spatial dimensions plus time. This approach offers an alternative to object-based tracking for processing large, low signal-to-noise datasets containing highly motile organisms.]]></description>
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