<?xml version="1.0" encoding="utf-8"?>
    <rss version="2.0">
      <channel xmlns:content="http://purl.org/rss/1.0/modules/content/">
        <title>Frontiers in Imaging | Image Retrieval and Analysis section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/imaging/sections/image-retrieval-and-analysis</link>
        <description>RSS Feed for Image Retrieval and Analysis section in the Frontiers in Imaging journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-05-13T11:29:01.21+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2026.1752625</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2026.1752625</link>
        <title><![CDATA[Enhancement of multi-objective Darwinian particle swarm optimization for neural-network-based multimodal medical image fusion]]></title>
        <pubdate>2026-02-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chisom E. Ogbuanya</author>
        <description><![CDATA[The purpose of this research is to develop a multimodal medical image fusion method that will provide high-performance fusion images at a speed high enough for efficient real-time image-guided surgeries. This paper therefore proposes an improved multi-objective Darwinian particle swarm optimization method that incorporates a fractional calculus operator for effective multimodal medical image fusion. This is because multimodal medical image fusion is essential in many clinical diagnoses, and it represents a multi-objective problem due to the important objective indicators for measuring its efficiencies, such as the parameters of the neural network and the speed of the fusion process. The proposed method aims to optimize the Tsallis cross-entropy as a stimulating input to the pulse-coupled neural network (PCNN) for multimodal image fusion. In this work, multi-objective Darwinian particle swarm optimization (MODPSO) is utilized due to its ability to escape local optima more effectively than classical multi-objective particle swarm optimization (MOPSO). The approach uses the fact that the convergence rate of MODPSO is improved by introducing a fractional calculus operator, which is incorporated into the updating formulas for the velocity and position of the particles. The PCNN output serves as an optimal parameter for fusing the high-frequency coefficients of decomposed source images, which are initially decomposed into low- and high-frequency subbands. The low-frequency coefficients are fused using an averaging method. Results obtained in this paper show that the proposed method yields the highest average accuracy of 90.7% after a three-fold cross-validation was carried out with a small dataset extracted from a larger available dataset. In conclusion, the experimental results demonstrate the superiority of the proposed method over comparative methods in terms of both visual quality and quantitative evaluation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2026.1725794</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2026.1725794</link>
        <title><![CDATA[Aluminum impairs cellular ultrastructure and bone microarchitecture in newborn rats]]></title>
        <pubdate>2026-02-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mara Rubia Marques</author><author>Anderson Camargo Moreira</author><author>Iara Frangiotti Mantovani</author><author>Pedro Vale de Azevedo Brito</author><author>Isabela Cristina Gomes de Souza Nascimento</author><author>Celso Peres Fernandes</author><author>Fernanda Cristina Alcantara dos Santos</author>
        <description><![CDATA[Modern lifestyle is strongly marked by the presence of aluminum (Al) in practically all human consumer products. Bone tissue is one of the main sites of Al accumulation, and its toxic effects are well known in individuals subjected to chronic exposure. However, there is still a gap in knowledge regarding the effects of Al on bone formation in the neonatal period. This study evaluated the effect of Al ingestion on rat tibiae during the neonatal period. Wistar rats were divided into control and Al groups. The Al group received AlCl3 (2.02 mg/kg/day) via gavage for fifteen days, then, the right tibiae were used to evaluate osteoblast and osteocyte ultrastructure and bone microarchitecture using transmission electron microscopy and computed X-ray microtomography, respectively. Al promoted swelling and altered mitochondrial crests in osteoblasts. Osteocytes showed accumulation of electron-dense lysosomes and absence of the osmiophilic lamina in the lacunae, showing characteristics similar to osteocytic osteolysis. Cortical Thickness (Ct.Th), Trabecular thickness (Tb.th) and trabecular number (Tb.N) decreased whilst trabecular spacing (Tb.Sp) increased. These results suggest that Al intake during the neonatal period may affect the function of osteoblasts and osteocytes besides compromising bone formation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2024.1339770</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2024.1339770</link>
        <title><![CDATA[Self-supervised representation learning of filtration barrier in kidney]]></title>
        <pubdate>2024-03-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>German Sergei</author><author>David Unnersjö-Jess</author><author>Linus Butt</author><author>Thomas Benzing</author><author>Katarzyna Bozek</author>
        <description><![CDATA[While the advance of deep learning has allowed to automate many tasks in bioimage analysis, quantifying key visual features of biological objects in an image, such as cells, organs, or tissues, is still a multi-step and laborious task. It requires image segmentation and definition of features of interest, which often might be image- and problem-specific. This approach requires image labeling and training of the segmentation method as well as manual feature design and implementation of dedicated procedures for their quantification. Here we propose a self-supervised learning (SSL) approach to encoding in microscopy images morphological features of molecular structures that play role in disease phenotype and patient clinical diagnosis. We encode super-resolution images of slit diaphragm (SD)—a specialized membrane between podocyte cells in kidney—in a high-dimensional embedding space in an unsupervised manner, without the need of image segmentation and feature quantification. We inspect the embedding space and demonstrate its relationship to the morphometric parameters of the SD estimated with a previously published method. The SSL-derived image representations additionally reflect the level of albuminuria—a key marker of advancement of kidney disease in a cohort of chronic kidney disease patients. Finally, the embeddings allow for distinguishing mouse model of kidney disease from the healthy subjects with a comparable accuracy to classification based on SD morphometric features. In a one step and label-free manner the SSL approach offers possibility to encode meaningful details in biomedical images and allow for their exploratory, unsupervised analysis as well as further fine-tuning for specialized supervised tasks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2023.1271885</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2023.1271885</link>
        <title><![CDATA[Height reverse perspective transformation for crowd counting]]></title>
        <pubdate>2023-10-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaomei Zhao</author><author>Honggang Li</author><author>Zhan Zhao</author><author>Shuo Li</author>
        <description><![CDATA[IntroductionCrowd counting plays a critical role in the intelligent video surveillance of public areas. A significant challenge to this task is the perspective effect on human heads, which causes serious scale variations. Height reverse perspective transformation (HRPT) alleviates this problem by narrowing the height gap among human heads.MethodsIt employs depth maps to calculate the rescaling factors of image rows, and then it performs image transformation accordingly. HRPT enlarges small human heads in far areas to make them more noticeable and shrinks large human heads in closer areas to reduce redundant information. Then, convolutional neural networks can be used for crowd counting. Previous crowd-counting methods mainly solve the scale variation problem by designing specific networks, such as multi-scale or perspective-aware networks. These networks cannot be conveniently employed by other methods. In contrast, HRPT solves the scale variation problem through image transformation. It can be used as a preprocessing step and easily employed by other crowd-counting methods without changing their original structures.Results and discussionExperimental results show that HRPT successfully narrows the height gap among human heads and achieves state-of-the-art performance on a large crowd-counting RGB-D dataset.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fimag.2022.951934</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fimag.2022.951934</link>
        <title><![CDATA[Challenges and opportunities of image and video retrieval]]></title>
        <pubdate>2022-08-04T00:00:00Z</pubdate>
        <category>Specialty Grand Challenge</category>
        <author>Guoping Qiu</author>
        <description></description>
      </item>
      </channel>
    </rss>