Journal of Imaging

15 pages, 8775 KB

Open AccessArticle

Assessing Change in Stone Burden on Baseline and Follow-Up CT: Radiologist and Radiomics Evaluations

by Parisa Kaviani, Matthias F. Froelich, Bernardo Bizzo, Andrew Primak, Giridhar Dasegowda, Emiliano Garza-Frias, Lina Karout, Anushree Burade, Seyedehelaheh Hosseini, Javier Eduardo Contreras Yametti, Keith Dreyer, Sanjay Saini and Mannudeep Kalra

J. Imaging 2026, 12(1), 13; https://doi.org/10.3390/jimaging12010013 - 27 Dec 2025

Abstract

This retrospective diagnostic accuracy study compared radiologist-based qualitative assessments and radiomics-based analyses with an automated artificial intelligence (AI)–based volumetric approach for evaluating changes in kidney stone burden on follow-up CT examinations. With institutional review board approval, 157 patients (mean age, 61 ± 13 [...] Read more.

This retrospective diagnostic accuracy study compared radiologist-based qualitative assessments and radiomics-based analyses with an automated artificial intelligence (AI)–based volumetric approach for evaluating changes in kidney stone burden on follow-up CT examinations. With institutional review board approval, 157 patients (mean age, 61 ± 13 years; 99 men, 58 women) who underwent baseline and follow-up non-contrast abdomen–pelvis CT for kidney stone evaluation were included. The index test was an automated AI-based whole-kidney and stone segmentation radiomics prototype (Frontier, Siemens Healthineers), which segmented both kidneys and isolated stone volumes using a fixed threshold of 130 Hounsfield units, providing stone volume and maximum diameter per kidney. The reference standard was a threshold-defined volumetric assessment of stone burden change between baseline and follow-up CTs. The radiologist’s performance was assessed using (1) interpretations from clinical radiology reports and (2) an independent radiologist’s assessment of stone burden change (stable, increased, or decreased). Diagnostic accuracy was evaluated using multivariable logistic regression and receiver operating characteristic (ROC) analysis. Automated volumetric assessment identified stable (n = 44), increased (n = 109), and decreased (n = 108) stone burden across the evaluated kidneys. Qualitative assessments from radiology reports demonstrated weak diagnostic performance (AUC range, 0.55–0.62), similar to the independent radiologist (AUC range, 0.41–0.72) for differentiating changes in stone burden. A model incorporating higher-order radiomics features achieved an AUC of 0.71 for distinguishing increased versus decreased stone burdens compared with the baseline CT (p < 0.001), but did not outperform threshold-based volumetric assessment. The automated threshold-based volumetric quantification of kidney stone burdens provides higher diagnostic accuracy than qualitative radiologist assessments and radiomics-based analyses for identifying a stable, increased, or decreased stone burden on follow-up CT examinations. Full article

(This article belongs to the Section Medical Imaging)

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23 pages, 69885 KB

Open AccessArticle

Patched-Based Swin Transformer Hyperprior for Learned Image Compression

by Sibusiso B. Buthelezi and Jules R. Tapamo

J. Imaging 2026, 12(1), 12; https://doi.org/10.3390/jimaging12010012 - 26 Dec 2025

Abstract

We present a hybrid end-to-end learned image compression framework that combines a CNN-based variational autoencoder (VAE) with an efficient hierarchical Swin Transformer to address the limitations of existing entropy models in capturing global dependencies under computational constraints. Traditional VAE-based codecs typically rely on [...] Read more.

We present a hybrid end-to-end learned image compression framework that combines a CNN-based variational autoencoder (VAE) with an efficient hierarchical Swin Transformer to address the limitations of existing entropy models in capturing global dependencies under computational constraints. Traditional VAE-based codecs typically rely on CNN-based priors with localized receptive fields, which are insufficient for modelling the complex, high-dimensional dependencies of the latent space, thereby limiting compression efficiency. While fully global transformer-based models can capture long-range dependencies, their high computational complexity makes them impractical for high-resolution image compression. To overcome this trade-off, our approach couples a CNN-based VAE with a patch-based hierarchical Swin Transformer hyperprior that employs shifted window self-attention to effectively model both local and global contextual information while maintaining computational efficiency. The proposed framework tightly integrates this expressive entropy model with an end-to-end differentiable quantization module, enabling joint optimization of the complete rate-distortion objective. By learning a more accurate probability distribution of the latent representation, the model achieves improved bitrate estimation and a more compact latent representation, resulting in enhanced compression performance. We validate our approach on the widely used Kodak, JPEG AI, and CLIC datasets, demonstrating that the proposed hybrid architecture achieves superior rate-distortion performance, delivering higher visual quality at lower bitrates compared to methods relying on simpler CNN-based entropy priors. This work demonstrates the effectiveness of integrating efficient transformer architectures into learned image compression and highlights their potential for advancing entropy modelling beyond conventional CNN-based designs. Full article

(This article belongs to the Section Image and Video Processing)

12 pages, 13049 KB

Open AccessArticle

A Hybrid Vision Transformer-BiRNN Architecture for Direct k-Space to Image Reconstruction in Accelerated MRI

by Changheun Oh

J. Imaging 2026, 12(1), 11; https://doi.org/10.3390/jimaging12010011 - 26 Dec 2025

Abstract

Long scan times remain a fundamental challenge in Magnetic Resonance Imaging (MRI). Accelerated MRI, which undersamples k-space, requires robust reconstruction methods to solve the ill-posed inverse problem. Recent methods have shown promise by processing image-domain features to capture global spatial context. However, these [...] Read more.

Long scan times remain a fundamental challenge in Magnetic Resonance Imaging (MRI). Accelerated MRI, which undersamples k-space, requires robust reconstruction methods to solve the ill-posed inverse problem. Recent methods have shown promise by processing image-domain features to capture global spatial context. However, these approaches are often limited, as they fail to fully leverage the unique, sequential characteristics of the k-space data themselves, which are critical for disentangling aliasing artifacts. This study introduces a novel, hybrid, dual-domain deep learning architecture that combines a ViT-based autoencoder with Bidirectional Recurrent Neural Networks (BiRNNs). The proposed architecture is designed to synergistically process information from both domains: it uses the ViT to learn features from image patches and the BiRNNs to model sequential dependencies directly from k-space data. We conducted a comprehensive comparative analysis against a standard ViT with only an MLP head (Model 1), a ViT autoencoder operating solely in the image domain (Model 2), and a competitive UNet baseline. Evaluations were performed on retrospectively undersampled neuro-MRI data using R = 4 and R = 8 acceleration factors with both regular and random sampling patterns. The proposed architecture demonstrated superior performance and robustness, significantly outperforming all other models in challenging high-acceleration and random-sampling scenarios. The results confirm that integrating sequential k-space processing via BiRNNs is critical for superior artifact suppression, offering a robust solution for accelerated MRI. Full article

(This article belongs to the Topic New Challenges in Image Processing and Pattern Recognition)

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17 pages, 4323 KB

Open AccessArticle

Render-Rank-Refine: Accurate 6D Indoor Localization via Circular Rendering

by Haya Monawwar and Guoliang Fan

J. Imaging 2026, 12(1), 10; https://doi.org/10.3390/jimaging12010010 - 25 Dec 2025

Abstract

Accurate six-degree-of-freedom (6-DoF) camera pose estimation is essential for augmented reality, robotics navigation, and indoor mapping. Existing pipelines often depend on detailed floorplans, strict Manhattan-world priors, and dense structural annotations, which lead to failures in ambiguous room layouts where multiple rooms appear in [...] Read more.

Accurate six-degree-of-freedom (6-DoF) camera pose estimation is essential for augmented reality, robotics navigation, and indoor mapping. Existing pipelines often depend on detailed floorplans, strict Manhattan-world priors, and dense structural annotations, which lead to failures in ambiguous room layouts where multiple rooms appear in a query image and their boundaries may overlap or be partially occluded. We present Render-Rank-Refine, a two-stage framework operating on coarse semantic meshes without requiring textured models or per-scene fine-tuning. First, panoramas rendered from the mesh enable global retrieval of coarse pose hypotheses. Then, perspective views from the top-k candidates are compared to the query via rotation-invariant circular descriptors, which re-ranks the matches before final translation and rotation refinement. Our method increases camera localization accuracy compared to the state-of-the-art SPVLoc baseline by reducing the translation error by 40.4% and the rotation error by 29.7% in ambiguous layouts, as evaluated on the Zillow Indoor Dataset. In terms of inference throughput, our method achieves 25.8–26.4 QPS, (Queries Per Second) which is significantly faster than other recent comparable methods, while maintaining accuracy comparable to or better than the SPVLoc baseline. These results demonstrate robust, near-real-time indoor localization that overcomes structural ambiguities and heavy geometric assumptions. Full article

(This article belongs to the Section Computer Vision and Pattern Recognition)

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16 pages, 4316 KB

Open AccessFeature PaperArticle

Accurate Segmentation of Vegetation in UAV Desert Imagery Using HSV-GLCM Features and SVM Classification

by Thani Jintasuttisak, Patompong Chabplan, Sasitorn Issaro, Orawan Saeung and Thamasan Suwanroj

J. Imaging 2026, 12(1), 9; https://doi.org/10.3390/jimaging12010009 - 25 Dec 2025

Abstract

Segmentation of vegetation from images is an important task in precision agriculture applications, particularly in challenging desert environments where sparse vegetation, varying soil colors, and strong shadows pose significant difficulties. In this paper, we present a machine learning approach to robust green-vegetation segmentation [...] Read more.

Segmentation of vegetation from images is an important task in precision agriculture applications, particularly in challenging desert environments where sparse vegetation, varying soil colors, and strong shadows pose significant difficulties. In this paper, we present a machine learning approach to robust green-vegetation segmentation in drone imagery captured over desert farmlands. The proposed method combines HSV color-space representation with Gray-Level Co-occurrence Matrix (GLCM) texture features and employs Support Vector Machine (SVM) as the learning algorithm. To enhance robustness, we incorporate comprehensive preprocessing, including Gaussian filtering, illumination normalization, and bilateral filtering, followed by morphological post-processing to improve segmentation quality. The method is evaluated against both traditional spectral index methods (ExG and CIVE) and a modern deep learning baseline using comprehensive metrics including accuracy, precision, recall, F1-score, and Intersection over Union (IoU). Experimental results on 120 high-resolution drone images from UAE desert farmlands demonstrate that the proposed method achieves superior performance with an accuracy of 0.91, F1-score of 0.88, and IoU of 0.82, showing significant improvement over baseline methods in handling challenging desert conditions, including shadows, varying soil colors, and sparse vegetation patterns. The method provides practical computational performance with a processing time of 25 s per image and a training time of 28 min, making it suitable for agricultural applications where accuracy is prioritized over processing speed. Full article

(This article belongs to the Section Computer Vision and Pattern Recognition)

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18 pages, 11359 KB

Open AccessArticle

A Terrain-Constrained TIN Approach for High-Precision DEM Reconstruction Using UAV Point Clouds

by Ziye He, Shu Gan and Xiping Yuan

J. Imaging 2026, 12(1), 8; https://doi.org/10.3390/jimaging12010008 - 25 Dec 2025

Abstract

To address the decline in self-consistency and limited spatial adaptability of traditional interpolation methods in complex terrain, this study proposes a terrain-constrained Triangulated Irregular Network (TIN) interpolation method based on UAV point clouds. The method was tested in the southern margin of the [...] Read more.

To address the decline in self-consistency and limited spatial adaptability of traditional interpolation methods in complex terrain, this study proposes a terrain-constrained Triangulated Irregular Network (TIN) interpolation method based on UAV point clouds. The method was tested in the southern margin of the Lufeng Dinosaur National Geopark, Yunnan Province, using ground points at different sampling densities (90%, 70%, 50%, 30%, and 10%), and compared with Spline, Kriging, ANUDEM, and IDW methods. Results show that the proposed method maintains the lowest RMSE and MAE across all densities, demonstrating higher stability and self-consistency and better preserving terrain undulations. This provides technical support for high-precision DEM reconstruction from UAV point clouds in complex terrain. Full article

(This article belongs to the Special Issue Intelligent Processing and Analysis of Multi-Spectral UAV Remote Sensing Images)

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23 pages, 3212 KB

Open AccessArticle

AKAZE-GMS-PROSAC: A New Progressive Framework for Matching Dynamic Characteristics of Flotation Foam

by Zhen Peng, Zhihong Jiang, Pengcheng Zhu, Gaipin Cai and Xiaoyan Luo

J. Imaging 2026, 12(1), 7; https://doi.org/10.3390/jimaging12010007 - 25 Dec 2025

Abstract

The dynamic characteristics of flotation foam, such as velocity and breakage rate, are critical factors that influence mineral separation efficiency. However, challenges inherent in foam images, including weak textures, severe deformations, and motion blur, present significant technical hurdles for dynamic monitoring. These issues [...] Read more.

The dynamic characteristics of flotation foam, such as velocity and breakage rate, are critical factors that influence mineral separation efficiency. However, challenges inherent in foam images, including weak textures, severe deformations, and motion blur, present significant technical hurdles for dynamic monitoring. These issues lead to a fundamental conflict between the efficiency and accuracy of traditional feature matching algorithms. This paper introduces a novel progressive framework for dynamic feature matching in flotation foam images, termed “stable extraction, efficient coarse screening, and precise matching.” This framework first employs the Accelerated-KAZE (AKAZE) algorithm to extract robust, scale- and rotation-invariant feature points from a non-linear scale-space, effectively addressing the challenge of weak textures. Subsequently, it innovatively incorporates the Grid-based Motion Statistics (GMS) algorithm to perform efficient coarse screening based on motion consistency, rapidly filtering out a large number of obvious mismatches. Finally, the Progressive Sample and Consensus (PROSAC) algorithm is used for precise matching, eliminating the remaining subtle mismatches through progressive sampling and geometric constraints. This framework enables the precise analysis of dynamic foam characteristics, including displacement, velocity, and breakage rate (enhanced by a robust “foam lifetime” mechanism). Comparative experimental results demonstrate that, compared to ORB-GMS-RANSAC (with a Mean Absolute Error, MAE of 1.20 pixels and a Mean Relative Error, MRE of 9.10%) and ORB-RANSAC (MAE: 3.53 pixels, MRE: 27.36%), the proposed framework achieves significantly lower error rates (MAE: 0.23 pixels, MRE: 2.13%). It exhibits exceptional stability and accuracy, particularly in complex scenarios involving low texture and minor displacements. This research provides a high-precision, high-robustness technical solution for the dynamic monitoring and intelligent control of the flotation process. Full article

(This article belongs to the Section Image and Video Processing)

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12 pages, 529 KB

Open AccessArticle

Long-Term Prognostic Value in Nuclear Cardiology: Expert Scoring Combined with Automated Measurements vs. Angiographic Score

by George Angelidis, Stavroula Giannakou, Varvara Valotassiou, Emmanouil Panagiotidis, Ioannis Tsougos, Chara Tzavara, Dimitrios Psimadas, Evdoxia Theodorou, Charalampos Ziangas, John Skoularigis, Filippos Triposkiadis and Panagiotis Georgoulias

J. Imaging 2026, 12(1), 6; https://doi.org/10.3390/jimaging12010006 - 25 Dec 2025

Abstract

The evaluation of myocardial perfusion imaging (MPI) studies is based on the visual interpretation of the reconstructed images, while the measurements obtained through software packages may contribute to the investigation, mainly in cases of ambiguous scintigraphic findings. We aimed to investigate the long-term [...] Read more.

The evaluation of myocardial perfusion imaging (MPI) studies is based on the visual interpretation of the reconstructed images, while the measurements obtained through software packages may contribute to the investigation, mainly in cases of ambiguous scintigraphic findings. We aimed to investigate the long-term prognostic value of expert reading of Summed Stress Score (SSS), Summed Rest Score (SRS), and Summed Difference Score (SDS), combined with the automated measurements of these parameters, in comparison to the prognostic ability of the angiographic score for soft and hard cardiac events. The study was conducted at the Nuclear Medicine Laboratory of the University of Thessaly, in Larissa, Greece. Overall, 378 consecutive patients with known or suspected coronary artery disease (CAD) were enrolled. Automated measurements of SSS, SRS, and SDS were obtained using the Emory Cardiac Toolbox, Myovation, and Quantitative Perfusion SPECT software packages. Coronary angiographies were scored according to a four-point scoring system (angiographic score). Follow-up data were recorded after phone contact, as well as through review of hospital records. All participants were followed up for at least 36 months. Soft and hard cardiac events were recorded in 31.7% and 11.6% of the sample, respectively, while any cardiac event was recorded in 36.5%. For hard cardiac events, the prognostic value of expert scoring, combined with the prognostic value of the automated measurements, was significantly greater compared to the prognostic ability of the angiographic score (p < 0.001). As far as any cardiac event, the prognostic value of expert scoring, combined with the prognostic value of the automated analyses, was significantly greater compared to the prognostic ability of the angiographic score (p < 0.001). According to our results, in patients with known or suspected CAD, the combination of expert reading and automated measurements of SSS, SRS, and SDS shows a superior prognostic ability in comparison to the angiographic score. Full article

(This article belongs to the Topic Applications of Image and Video Processing in Medical Imaging)

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12 pages, 1687 KB

Open AccessArticle

Bone Changes in Mandibular Condyle of Temporomandibular Dysfunction Patients Recognized on Magnetic Resonance Imaging

by Fumi Mizuhashi, Ichiro Ogura, Ryo Mizuhashi, Yuko Watarai, Tatsuhiro Suzuki, Momoka Kawana, Kotono Nagata, Tomonori Niitsuma and Makoto Oohashi

J. Imaging 2026, 12(1), 5; https://doi.org/10.3390/jimaging12010005 - 24 Dec 2025

Abstract

We aimed to investigate the type of bone changes in temporomandibular disorder patients with disc displacement. The subjects were 117 temporomandibular joints that were diagnosed with anterior disc displacement using magnetic resonance imaging (MRI). Temporomandibular joint (TMJ) pain and opening dysfunction were examined. [...] Read more.

We aimed to investigate the type of bone changes in temporomandibular disorder patients with disc displacement. The subjects were 117 temporomandibular joints that were diagnosed with anterior disc displacement using magnetic resonance imaging (MRI). Temporomandibular joint (TMJ) pain and opening dysfunction were examined. Disc displacement with and without reduction, joint effusion, and bone changes in the mandibular condyle were assessed on MRI. The types of bone changes were classified into erosion, flattening, osteophyte, and atrophy on the MR images. Fisher’s exact test and χ² test were performed for analyses. Bone changes were found on 30.8% of subjects with erosion, flattening, osteophyte, and atrophy types (p < 0.001). The occurrence of joint effusion appearance (p < 0.001), TMJ pain (p = 0.027), and opening dysfunction (p = 0.002) differed among the types of bone changes. Gender differences were also found among the types of bone changes (p < 0.001). The rate of disc displacement with reduction was significantly smaller than that of disc displacement without reduction on flattening and osteophyte (p < 0.001). The results made it clear that the symptoms, gender, and presence or absence of disc reduction differed among the types of bone changes. Full article

(This article belongs to the Section Medical Imaging)

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23 pages, 6281 KB

Open AccessArticle

Empirical Mode Decomposition-Based Deep Learning Model Development for Medical Imaging: Feasibility Study for Gastrointestinal Endoscopic Image Classification

by Mou Deb, Mrinal Kanti Dhar, Poonguzhali Elangovan, Keerthy Gopalakrishnan, Divyanshi Sood, Aaftab Sethi, Sabah Afroze, Sourav Bansal, Aastha Goudel, Charmy Parikh, Avneet Kaur, Swetha Rapolu, Gianeshwaree Alias Rachna Panjwani, Rabiah Aslam Ansari, Naghmeh Asadimanesh, Shiva Sankari Karuppiah, Scott A. Helgeson, Venkata S. Akshintala and Shivaram P. Arunachalam

J. Imaging 2026, 12(1), 4; https://doi.org/10.3390/jimaging12010004 - 22 Dec 2025

Abstract

This study proposes a novel two-dimensional Empirical Mode Decomposition (2D EMD)-based deep learning framework to enhance model performance in multi-class image classification tasks and potential early detection of diseases in healthcare using medical imaging. To validate this approach, we apply it to gastrointestinal [...] Read more.

This study proposes a novel two-dimensional Empirical Mode Decomposition (2D EMD)-based deep learning framework to enhance model performance in multi-class image classification tasks and potential early detection of diseases in healthcare using medical imaging. To validate this approach, we apply it to gastrointestinal (GI) endoscopic image classification using the publicly available Kvasir dataset, which contains eight GI image classes with 1000 images each. The proposed 2D EMD-based design procedure decomposes images into a full set of intrinsic mode functions (IMFs) to enhance image features beneficial for AI model development. Integrating 2D EMD into a deep learning pipeline, we evaluate its impact on four popular models (ResNet152, VGG19bn, MobileNetV3L, and SwinTransformerV2S). The results demonstrate that subtracting IMFs from the original image consistently improves accuracy, F1-score, and AUC for all models. The study reveals a notable enhancement in model performance, with an approximately 9% increase in accuracy compared to counterparts without EMD integration for ResNet152. Similarly, there is an increase of around 18% for VGG19L, 3% for MobileNetV3L, and 8% for SwinTransformerV2. Additionally, explainable AI (XAI) techniques, such as Grad-CAM, illustrate that the model focuses on GI regions for predictions. This study highlights the efficacy of 2D EMD in enhancing deep learning model performance for GI image classification, with potential applications in other domains. Full article

(This article belongs to the Special Issue Clinical and Pathological Imaging in the Era of Artificial Intelligence: New Insights and Perspectives—2nd Edition)

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15 pages, 1303 KB

Open AccessSystematic Review

Adjunct Automated Breast Ultrasound in Mammographic Screening: A Systematic Review and Meta-Analysis

by Ghufran Jassim, Fahad AlZayani and Suchita Dsilva

J. Imaging 2026, 12(1), 3; https://doi.org/10.3390/jimaging12010003 - 22 Dec 2025

Abstract

Mammographic sensitivity is reduced in women with dense breasts, leading to missed cancers and a higher burden of interval cancers. Automated breast ultrasound (ABUS) and ultrasound tomography (UST) have been introduced as supplemental breast imaging modalities, but primary studies are heterogeneous, and previous [...] Read more.

Mammographic sensitivity is reduced in women with dense breasts, leading to missed cancers and a higher burden of interval cancers. Automated breast ultrasound (ABUS) and ultrasound tomography (UST) have been introduced as supplemental breast imaging modalities, but primary studies are heterogeneous, and previous reviews have not focused on screening settings or on head-to-head comparisons with handheld ultrasound (HHUS). We systematically searched PubMed, Embase, Web of Science and the Cochrane Library for studies from 1 January 2000 to 31 May 2025 evaluating ABUS or UST as adjuncts to mammographic screening. Two reviewers independently selected studies and assessed risk of bias. When at least two clinically comparable studies were available, we pooled sensitivity and specificity using random-effects bivariate meta-analysis. Eighteen studies (just over 20,000 screening or recall episodes) met the inclusion criteria; 16 evaluated ABUS/ABVS and 2 UST. Adding ABUS to mammography increased sensitivity by 6–35 percentage points and improved cancer detection by 2.4–4.3 per 1000 women with dense breasts, with higher recall rates and modest reductions in specificity. When ABUS was compared directly with HHUS, pooled sensitivity was 0.90 and specificity 0.89, with HHUS showing slightly lower sensitivity and slightly higher specificity. Only two studies had an overall low risk of bias, and heterogeneity (particularly for specificity) was substantial. ABUS is a practical and scalable adjunct to mammography that increases cancer detection in women with dense breasts, with an expected trade-off of higher recall and modest specificity loss. Its comparative diagnostic accuracy appears broadly non-inferior to HHUS. However, the predominance of high-risk-of-bias studies and between-study heterogeneity means that high-quality population-based trials and standardised reporting are still required before widespread implementation in organised screening programmes. Full article

(This article belongs to the Section Medical Imaging)

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12 pages, 2202 KB

Open AccessArticle

Development of a Multispectral Image Database in Visible–Near–Infrared for Demosaicking and Machine Learning Applications

by Vahid Mohammadi, Sovi Guillaume Sodjinou and Pierre Gouton

J. Imaging 2026, 12(1), 2; https://doi.org/10.3390/jimaging12010002 - 20 Dec 2025

Abstract

The use of Multispectral (MS) imaging is growing fast across many research fields. However, one of the obstacles researchers face is the limited availability of multispectral image databases. This arises from two factors: multispectral cameras are a relatively recent technology, and they are [...] Read more.

The use of Multispectral (MS) imaging is growing fast across many research fields. However, one of the obstacles researchers face is the limited availability of multispectral image databases. This arises from two factors: multispectral cameras are a relatively recent technology, and they are not widely available. Hence, the development of an image database is crucial for research on multispectral images. This study takes advantage of two high-end MS cameras in visible and near-infrared based on filter array technology developed in the PImRob platform, the University of Burgundy, to provide a freely accessible database. The database includes high-resolution MS images taken from different plants and weeds, along with annotated images and masks. The original raw images and the demosaicked images have been provided. The database has been developed for research on demosaicking techniques, segmentation algorithms, or deep learning for crop/weed discrimination. Full article

(This article belongs to the Special Issue Imaging Applications in Agriculture)

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22 pages, 26190 KB

Open AccessArticle

Non-Destructive Mangosteen Volume Estimation via Multi-View Instance Segmentation and Hybrid Geometric Modeling

by Wattanapong Kurdthongmee, Arsanchai Sukkuea, Md Eshrat E Alahi and Qi Zeng

J. Imaging 2026, 12(1), 1; https://doi.org/10.3390/jimaging12010001 - 19 Dec 2025

Abstract

In precision agriculture, accurate, non-destructive estimation of fruit volume is crucial for quality grading, yield prediction, and post-harvest management. While vision-based methods provided some usefulness, fruits with complex geometry—such as mangosteen (Garcinia mangostana L.)—are difficult due to their large calyx, which may [...] Read more.

In precision agriculture, accurate, non-destructive estimation of fruit volume is crucial for quality grading, yield prediction, and post-harvest management. While vision-based methods provided some usefulness, fruits with complex geometry—such as mangosteen (Garcinia mangostana L.)—are difficult due to their large calyx, which may lead to difficulties in solving using traditional form-modeling methods. Traditional geometric solutions such as ellipsoid approximations, diameter–height estimation, and shape-from-silhouette reconstruction often fail because the irregular calyx generates asymmetric protrusions that violate their basic form assumptions. We offer a novel study framework employing both multi-view instance segmentation and hybrid geometrical feature modeling to quantitatively model mangosteen volume with traditional 2D imaging. A You Only Look Once (YOLO)-based segmentation model was employed to explicitly separate the fruit body from the calyx. Calyx inclusion resulted in dense geometric noise and reduced model performance (

R^{2} < 0.40

). We trained eight regression models on a curated and augmented 900 image dataset (

N = 720

, test

N = 180

). The models used single-view and multi-view geometric regressors (

V \propto A^{1.5}

), polynomial hybrid configurations, ellipsoid-based approximations, as well as hybrid feature formulations. Multi-view models consistently outperformed single-view models, and the average predictive accuracy improved from

R^{2} = 0.6493

to

R^{2} = 0.7290

. The best model is indeed a hybrid linear regression model with side- and bottom-area features—(

A_{s}^{1.5}

,

A_{b}^{1.5}

)—combined with ellipsoid-derived volume estimation—(

V_{ellipsoid}

)—which resulted in

R^{2} = 0.7290

, a Mean Absolute Percentage Error (MAPE) of 16.04%, and a Root Mean Square Error (RMSE) of 31.9

{cm}^{3}

on the test set. These results confirm the proposed model as a low-cost, interpretable, and flexible model for real-time fruit volume estimation, ready for incorporation into automated sorting and grading systems integrated in post-harvest processing pipelines. Full article

(This article belongs to the Section Computer Vision and Pattern Recognition)

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29 pages, 9041 KB

Open AccessReview

A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Development

by Minh Sao Khue Luu, Margaret V. Benedichuk, Ekaterina I. Roppert, Roman M. Kenzhin and Bair N. Tuchinov

J. Imaging 2025, 11(12), 454; https://doi.org/10.3390/jimaging11120454 - 18 Dec 2025

Abstract

The development of foundation models for brain MRI depends critically on the scale, diversity, and consistency of available data, yet systematic assessments of these factors remain scarce. In this study, we analyze 54 publicly accessible brain MRI datasets encompassing over 538,031 scans to [...] Read more.

The development of foundation models for brain MRI depends critically on the scale, diversity, and consistency of available data, yet systematic assessments of these factors remain scarce. In this study, we analyze 54 publicly accessible brain MRI datasets encompassing over 538,031 scans to provide a structured, multi-level overview tailored to foundation model development. At the dataset level, we characterize modality composition, disease coverage, and dataset scale, revealing strong imbalances between large healthy cohorts and smaller clinical populations. At the image level, we quantify voxel spacing, orientation, and intensity distributions across 14 representative datasets, demonstrating substantial heterogeneity that can influence representation learning. We then perform a quantitative evaluation of preprocessing variability, examining how intensity normalization, bias field correction, skull stripping, spatial registration, and interpolation alter voxel statistics and geometry. While these steps improve within-dataset consistency, residual differences persist between datasets. Finally, a feature-space case study using a 3D DenseNet121 shows measurable residual covariate shift after standardized preprocessing, confirming that harmonization alone cannot eliminate inter-dataset bias. Together, these analyses provide a unified characterization of variability in public brain MRI resources and emphasize the need for preprocessing-aware and domain-adaptive strategies in the design of generalizable brain MRI foundation models. Full article

(This article belongs to the Special Issue Self-Supervised Learning and Multimodal Foundation Models for AI-Driven Medical Imaging)

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18 pages, 1564 KB

Open AccessArticle

Salient Object Detection in Optical Remote Sensing Images Based on Hierarchical Semantic Interaction

by Jingfan Xu, Qi Zhang, Jinwen Xing, Mingquan Zhou and Guohua Geng

J. Imaging 2025, 11(12), 453; https://doi.org/10.3390/jimaging11120453 - 17 Dec 2025

Abstract

Existing salient object detection methods for optical remote sensing images still face certain limitations due to complex background variations, significant scale discrepancies among targets, severe background interference, and diverse topological structures. On the one hand, the feature transmission process often neglects the constraints [...] Read more.

Existing salient object detection methods for optical remote sensing images still face certain limitations due to complex background variations, significant scale discrepancies among targets, severe background interference, and diverse topological structures. On the one hand, the feature transmission process often neglects the constraints and complementary effects of high-level features on low-level features, leading to insufficient feature interaction and weakened model representation. On the other hand, decoder architectures generally rely on simple cascaded structures, which fail to adequately exploit and utilize contextual information. To address these challenges, this study proposes a Hierarchical Semantic Interaction Module to enhance salient object detection performance in optical remote sensing scenarios. The module introduces foreground content modeling and a hierarchical semantic interaction mechanism within a multi-scale feature space, reinforcing the synergy and complementarity among features at different levels. This effectively highlights multi-scale and multi-type salient regions in complex backgrounds. Extensive experiments on multiple optical remote sensing datasets demonstrate the effectiveness of the proposed method. Specifically, on the EORSSD dataset, our full model integrating both CA and PA modules improves the max F-measure from 0.8826 to 0.9100 (↑2.74%), increases maxE from 0.9603 to 0.9727 (↑1.24%), and enhances the S-measure from 0.9026 to 0.9295 (↑2.69%) compared with the baseline. These results clearly demonstrate the effectiveness of the proposed modules and verify the robustness and strong generalization capability of our method in complex remote sensing scenarios. Full article

(This article belongs to the Special Issue AI-Driven Remote Sensing Image Processing and Pattern Recognition)

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20 pages, 4545 KB

Open AccessArticle

SRE-FMaps: A Sinkhorn-Regularized Elastic Functional Map Framework for Non-Isometric 3D Shape Matching

by Dan Zhang, Yue Zhang, Ning Wang and Dong Zhao

J. Imaging 2025, 11(12), 452; https://doi.org/10.3390/jimaging11120452 - 16 Dec 2025

Abstract

Precise 3D shape correspondence is a fundamental prerequisite for critical applications ranging from medical anatomical modeling to visual recognition. However, non-isometric 3D shape matching remains a challenging task due to the limited sensitivity of traditional Laplace–Beltrami (LB) bases to local geometric deformations such [...] Read more.

Precise 3D shape correspondence is a fundamental prerequisite for critical applications ranging from medical anatomical modeling to visual recognition. However, non-isometric 3D shape matching remains a challenging task due to the limited sensitivity of traditional Laplace–Beltrami (LB) bases to local geometric deformations such as stretching and bending. To address these limitations, this paper proposes a Sinkhorn-Regularized Elastic Functional Map framework (SRE-FMaps) that integrates entropy-regularized optimal transport with an elastic thin-shell energy basis. First, a sparse Sinkhorn transport plan is adopted to initialize a bijective correspondence with linear computational complexity. Then, a non-orthogonal elastic basis, derived from the Hessian of thin-shell deformation energy, is introduced to enhance high-frequency feature perception. Finally, correspondence stability is quantified through a cosine-based elastic distance metric, enabling retrieval and classification. Experiments on the SHREC2015, McGill, and Face datasets demonstrate that SRE-FMaps reduces the correspondence error by a maximum of 32% and achieves an average of 92.3% classification accuracy (with a peak of 94.74% on the Face dataset). Moreover, the framework exhibits superior robustness, yielding a recall of up to 91.67% and an F1-score of 0.94, effectively handling bending, stretching, and folding deformations compared with conventional LB-based functional map pipelines. The proposed framework provides a scalable solution for non-isometric shape correspondence in medical modeling, 3D reconstruction, and visual recognition. Full article

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27 pages, 20790 KB

Open AccessArticle

Application of Generative Adversarial Networks to Improve COVID-19 Classification on Ultrasound Images

by Pedro Sérgio Tôrres Figueiredo Silva, Antonio Mauricio Ferreira Leite Miranda de Sá, Wagner Coelho de Albuquerque Pereira, Leonardo Bonato Felix and José Manoel de Seixas

J. Imaging 2025, 11(12), 451; https://doi.org/10.3390/jimaging11120451 - 15 Dec 2025

Abstract

COVID-19 screening is crucial for the early diagnosis and treatment of the disease, with lung ultrasound posing as a cost-effective alternative to other imaging techniques. Given the dependency on medical expertise and experience to accurately identify patterns in ultrasound exams, deep learning techniques [...] Read more.

COVID-19 screening is crucial for the early diagnosis and treatment of the disease, with lung ultrasound posing as a cost-effective alternative to other imaging techniques. Given the dependency on medical expertise and experience to accurately identify patterns in ultrasound exams, deep learning techniques have been explored for automatically classifying patients’ conditions. However, the limited availability of public medical databases remains a significant obstacle to the development of more advanced models. To address the data scarcity problem, this study proposes a method that leverages generative adversarial networks (GANs) to generate synthetic lung ultrasound images, which are subsequently used to train frame-based classification models. Two types of GANs are considered: Wasserstein GANs (WGAN) and Pix2Pix. Specific tools are used to show that the synthetic data produced present a distribution close to the original data. The classification models trained with synthetic data achieved a peak accuracy of 96.32% ± 4.17%, significantly outperforming the maximum accuracy of 82.69% ± 10.42% obtained when training only with the original data. Furthermore, the best results are comparable to, and in some cases surpass, those reported in recent related studies. Full article

(This article belongs to the Section Medical Imaging)

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14 pages, 2526 KB

Open AccessArticle

Applying Radiomics to Predict Outcomes in Patients with High-Grade Retroperitoneal Sarcoma Treated with Preoperative Radiotherapy

by Adel Shahnam, Nicholas Hardcastle, David E. Gyorki, Katrina M. Ingley, Krystel Tran, Catherine Mitchell, Sarat Chander, Julie Chu, Michael Henderson, Alan Herschtal, Mathias Bressel and Jeremy Lewin

J. Imaging 2025, 11(12), 450; https://doi.org/10.3390/jimaging11120450 - 15 Dec 2025

Abstract

Retroperitoneal sarcomas (RPS) are rare tumours, primarily treated with surgical resection. However, recurrences are frequent. Combining clinical factors with CT-derived radiomic features could enhance treatment stratification and personalization. This study aims to assess whether radiomic features provide additional prognostic value beyond clinicopathological features [...] Read more.

Retroperitoneal sarcomas (RPS) are rare tumours, primarily treated with surgical resection. However, recurrences are frequent. Combining clinical factors with CT-derived radiomic features could enhance treatment stratification and personalization. This study aims to assess whether radiomic features provide additional prognostic value beyond clinicopathological features in patients with high-risk RPS treated with preoperative radiotherapy. This retrospective study included patients aged 18 or older with non-recurrent and non-metastatic RPS treated with preoperative radiotherapy between 2008 and 2016. Hazard ratios (HR) were calculated using Cox proportional hazards regression to assess the impact of clinical and radiomic features on time to event outcomes. Predictive accuracy was assessed with c-statistics. Radiomic analysis was performed on the high-risk group (undifferentiated pleomorphic sarcoma, well-differentiated/de-differentiated liposarcoma or grade 2/3 leiomyosarcoma). Seventy-two patients were included, with a median follow-up of 3.7 years, the 5-year overall survival (OS) was 67%. Multivariable analysis showed older age (HR: 1.3 per 5-year increase, p = 0.04), grade 3 (HR: 180.3, p = 0.02), and larger tumours (HR: 4.0 per 10 cm increase, p = 0.02) predicted worse OS. In the higher-risk group, the c-statistic for the clinical model was 0.59 (time to distant metastasis (TDM)) and 0.56 (OS). Among 27 radiomic features, kurtosis improved OS prediction (c-statistic 0.69, p = 0.013), and Neighbourhood Gray-Tone Difference Matrix (NGTDM) busyness improved it to 0.73 (p = 0.036). Kurtosis also improved TDM prediction (c-statistic 0.72, p = 0.023). Radiomic features may complement clinicopathological factors in predicting overall survival and time to distant metastasis in high-risk retroperitoneal sarcoma. These exploratory findings warrant validation in larger, multi-institutional studies. Full article

(This article belongs to the Section Medical Imaging)

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19 pages, 3468 KB

Open AccessArticle

Sensory Representation of Neural Networks Using Sound and Color for Medical Imaging Segmentation

by Irenel Lopo Da Silva, Nicolas Francisco Lori and José Manuel Ferreira Machado

J. Imaging 2025, 11(12), 449; https://doi.org/10.3390/jimaging11120449 - 15 Dec 2025

Abstract

This paper introduces a novel framework for sensory representation of brain imaging data, combining deep learning-based segmentation with multimodal visual and auditory outputs. Structural magnetic resonance imaging (MRI) predictions are converted into color-coded maps and stereophonic/MIDI sonifications, enabling intuitive interpretation of cortical activation [...] Read more.

This paper introduces a novel framework for sensory representation of brain imaging data, combining deep learning-based segmentation with multimodal visual and auditory outputs. Structural magnetic resonance imaging (MRI) predictions are converted into color-coded maps and stereophonic/MIDI sonifications, enabling intuitive interpretation of cortical activation patterns. High-precision U-Net models efficiently generate these outputs, supporting clinical decision-making, cognitive research, and creative applications. Spatial, intensity, and anomalous features are encoded into perceivable visual and auditory cues, facilitating early detection and introducing the concept of “auditory biomarkers” for potential pathological identification. Despite current limitations, including dataset size, absence of clinical validation, and heuristic-based sonification, the pipeline demonstrates technical feasibility and robustness. Future work will focus on clinical user studies, the application of functional MRI (fMRI) time-series for dynamic sonification, and the integration of real-time emotional feedback in cinematic contexts. This multisensory approach offers a promising avenue for enhancing the interpretability of complex neuroimaging data across medical, research, and artistic domains. Full article

(This article belongs to the Section Medical Imaging)

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