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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>
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        <pubDate>2026-04-11T14:59:37.132+00:00</pubDate>
        <ttl>60</ttl>
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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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