Journal of Imaging

15 pages, 85946 KiB

Open AccessArticle

by Junjie Wei and Ying Song

J. Imaging 2025, 11(5), 158; https://doi.org/10.3390/jimaging11050158 (registering DOI) - 16 May 2025

The real-time rendering of large-scale curve-based surfaces (e.g., hair, fabrics) requires efficient handling of bidirectional curve-scattering distribution functions (BCSDFs). While curve-based material models are essential for capturing anisotropic reflectance characteristics, conventional prefiltering techniques encounter challenges in jointly resolving micro-scale BCSDFs variations with tangent [...] Read more.

The real-time rendering of large-scale curve-based surfaces (e.g., hair, fabrics) requires efficient handling of bidirectional curve-scattering distribution functions (BCSDFs). While curve-based material models are essential for capturing anisotropic reflectance characteristics, conventional prefiltering techniques encounter challenges in jointly resolving micro-scale BCSDFs variations with tangent distribution functions (TDFs) at pixel-level accuracy. This paper presents a real-time BCSDF filtering framework that achieves high-fidelity rendering without precomputation. Our key insight lies in formulating each pixel’s scattering response as a mixture of von Mises–Fisher (vMF) distributions, enabling analytical convolution between micro-scale BCSDFs and TDFs. Furthermore, we derive closed-form expressions for the integral of TDF-BCSDF products, avoiding the need for numerical approximation and heavy precomputation. Our method demonstrates state-of-the-art performance, achieving results comparable to 1000 spp Monte Carlo simulations under parallax-free conditions, where it improves the mean squared error (MSE) by one to two orders of magnitude over baseline methods. Qualitative comparisons and error analysis confirm both visual fidelity and computational efficiency. Full article

(This article belongs to the Section Visualization and Computer Graphics)

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28 pages, 7048 KiB

Open AccessArticle

Learning AI-Driven Automated Blood Cell Anomaly Detection: Enhancing Diagnostics and Telehealth in Hematology

by Sami Naouali and Oussama El Othmani

J. Imaging 2025, 11(5), 157; https://doi.org/10.3390/jimaging11050157 - 16 May 2025

Abstract

Hematology plays a critical role in diagnosing and managing a wide range of blood-related disorders. The manual interpretation of blood smear images, however, is time-consuming and highly dependent on expert availability. Moreover, it is particularly challenging in remote and resource-limited settings. In this [...] Read more.

Hematology plays a critical role in diagnosing and managing a wide range of blood-related disorders. The manual interpretation of blood smear images, however, is time-consuming and highly dependent on expert availability. Moreover, it is particularly challenging in remote and resource-limited settings. In this study, we present an AI-driven system for automated blood cell anomaly detection, combining computer vision and machine learning models to support efficient diagnostics in hematology and telehealth contexts. Our architecture integrates segmentation (YOLOv11), classification (ResNet50), transfer learning, and zero-shot learning to identify and categorize cell types and abnormalities from blood smear images. Evaluated on real annotated samples, the system achieved high performance, with a precision of 0.98, recall of 0.99, and F1 score of 0.98. These results highlight the potential of the proposed system to enhance remote diagnostic capabilities and support clinical decision making in underserved regions. Full article

(This article belongs to the Special Issue Advances in Medical Imaging and Machine Learning)

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26 pages, 3942 KiB

Open AccessArticle

Unleashing the Potential of Residual and Dual-Stream Transformers for the Remote Sensing Image Analysis

by Priya Mittal, Vishesh Tanwar, Bhisham Sharma and Dhirendra Prasad Yadav

J. Imaging 2025, 11(5), 156; https://doi.org/10.3390/jimaging11050156 - 15 May 2025

Abstract

The categorization of remote sensing satellite imagery is crucial for various applications, including environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have exhibited exceptional performance among deep learning techniques, excelling in feature extraction and representational learning. [...] Read more.

The categorization of remote sensing satellite imagery is crucial for various applications, including environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have exhibited exceptional performance among deep learning techniques, excelling in feature extraction and representational learning. This paper presents a hybrid dual-stream ResV2ViT model that combines the advantages of ResNet50 V2 and Vision Transformer (ViT) architectures. The dual-stream approach allows the model to extract both local spatial features and global contextual information by processing data through two complementary pathways. The ResNet50V2 component is utilized for hierarchical feature extraction and captures short-range dependencies, whereas the ViT module efficiently models long-range dependencies and global contextual information. After position embedding in the hybrid model, the tokens are bifurcated into two parts: q1 and q2. q1 is passed into the convolutional block to refine local spatial details, and q2 is given to the Transformer to provide global attention to the spatial feature. Combining these two architectures allows the model to acquire low-level and high-level feature representations, improving classification performance. We assess the proposed ResV2ViT model using the RSI-CB256 dataset and another dataset with 21 classes. The proposed model attains an average accuracy of 99.91%, with precision and F1 score of 99.90% for the first dataset and 98.75% accuracy for the second dataset, illustrating its efficacy in satellite image classification. The findings demonstrate that the dual-stream hybrid ResV2ViT model surpasses traditional CNN and Transformer-based models, establishing it as a formidable framework for remote sensing applications. Full article

(This article belongs to the Special Issue Advanced Hyperspectral Imaging: Techniques, Data Analysis, and Applications)

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17 pages, 5792 KiB

Open AccessArticle

Beyond Handcrafted Features: A Deep Learning Framework for Optical Flow and SLAM

by Kamran Kazi, Arbab Nighat Kalhoro, Farida Memon, Azam Rafique Memon and Muddesar Iqbal

J. Imaging 2025, 11(5), 155; https://doi.org/10.3390/jimaging11050155 - 15 May 2025

Abstract

This paper presents a novel approach for visual Simultaneous Localization and Mapping (SLAM) using Convolution Neural Networks (CNNs) for robust map creation. Traditional SLAM methods rely on handcrafted features, which are susceptible to viewpoint changes, occlusions, and illumination variations. This work proposes a [...] Read more.

This paper presents a novel approach for visual Simultaneous Localization and Mapping (SLAM) using Convolution Neural Networks (CNNs) for robust map creation. Traditional SLAM methods rely on handcrafted features, which are susceptible to viewpoint changes, occlusions, and illumination variations. This work proposes a method that leverages the power of CNNs by extracting features from an intermediate layer of a pre-trained model for optical flow estimation. We conduct an extensive search for optimal features by analyzing the offset error across thousands of combinations of layers and filters within the CNN. This analysis reveals a specific layer and filter combination that exhibits minimal offset error while still accounting for viewpoint changes, occlusions, and illumination variations. These features, learned by the CNN, are demonstrably robust to environmental challenges that often hinder traditional handcrafted features in SLAM tasks. The proposed method is evaluated on six publicly available datasets that are widely used for bench-marking map estimation and accuracy. Our method consistently achieved the lowest offset error compared to traditional handcrafted feature-based approaches on all six datasets. This demonstrates the effectiveness of CNN-derived features for building accurate and robust maps in diverse environments. Full article

(This article belongs to the Section Visualization and Computer Graphics)

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16 pages, 9488 KiB

Open AccessArticle

A Multitask Network for the Diagnosis of Autoimmune Gastritis

by Yuqi Cao, Yining Zhao, Xinao Jin, Jiayuan Zhang, Gangzhi Zhang, Pingjie Huang, Guangxin Zhang and Yuehua Han

J. Imaging 2025, 11(5), 154; https://doi.org/10.3390/jimaging11050154 - 15 May 2025

Abstract

Autoimmune gastritis (AIG) has a strong correlation with gastric neuroendocrine tumors (NETs) and gastric cancer, making its timely and accurate diagnosis crucial for tumor prevention. The endoscopic manifestations of AIG differ from those of gastritis caused by Helicobacter pylori (H. pylori) [...] Read more.

Autoimmune gastritis (AIG) has a strong correlation with gastric neuroendocrine tumors (NETs) and gastric cancer, making its timely and accurate diagnosis crucial for tumor prevention. The endoscopic manifestations of AIG differ from those of gastritis caused by Helicobacter pylori (H. pylori) infection in terms of the affected gastric anatomical regions and the pathological characteristics observed in biopsy samples. Therefore, when diagnosing AIG based on endoscopic images, it is essential not only to distinguish between normal and atrophic gastric mucosa but also to accurately identify the anatomical region in which the atrophic mucosa is located. In this study, we propose a patient-based multitask gastroscopy image classification network that analyzes all images obtained during the endoscopic procedure. First, we employ the Scale-Invariant Feature Transform (SIFT) algorithm for image registration, generating an image similarity matrix. Next, we use a hierarchical clustering algorithm to group images based on this matrix. Finally, we apply the RepLKNet model, which utilizes large-kernel convolution, to each image group to perform two tasks: anatomical region classification and lesion recognition. Our method achieves an accuracy of 93.4 ± 0.5% (95% CI) and a precision of 92.6 ± 0.4% (95% CI) in the anatomical region classification task, which categorizes images into the fundus, body, and antrum. Additionally, it attains an accuracy of 90.2 ± 1.0% (95% CI) and a precision of 90.5 ± 0.8% (95% CI) in the lesion recognition task, which identifies the presence of gastric mucosal atrophic lesions in gastroscopy images. These results demonstrate that the proposed multitask patient-based gastroscopy image analysis method holds significant practical value for advancing computer-aided diagnosis systems for atrophic gastritis and enhancing the diagnostic accuracy and efficiency of AIG. Full article

(This article belongs to the Section Medical Imaging)

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28 pages, 32576 KiB

Open AccessArticle

Machine Learning Algorithms of Remote Sensing Data Processing for Mapping Changes in Land Cover Types over Central Apennines, Italy

by Polina Lemenkova

J. Imaging 2025, 11(5), 153; https://doi.org/10.3390/jimaging11050153 - 12 May 2025

Viewed by 263

Abstract

This work presents the use of remote sensing data for land cover mapping with a case of Central Apennines, Italy. The data include 8 Landsat 8-9 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) satellite images in six-year period (2018–2024). The operational workflow included satellite [...] Read more.

This work presents the use of remote sensing data for land cover mapping with a case of Central Apennines, Italy. The data include 8 Landsat 8-9 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) satellite images in six-year period (2018–2024). The operational workflow included satellite image processing which were classified into raster maps with automatically detected 10 classes of land cover types over the tested study. The approach was implemented by using a set of modules in Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). To classify remote sensing (RS) data, two types of approaches were carried out. The first is unsupervised classification based on the MaxLike approach and clustering which extracted Digital Numbers (DN) of landscape feature based on the spectral reflectance of signals, and the second is supervised classification performed using several methods of Machine Learning (ML), technically realised in GRASS GIS scripting software. The latter included four ML algorithms embedded from the Python’s Scikit-Learn library. These classifiers have been implemented to detect subtle changes in land cover types as derived from the satellite images showing different vegetation conditions in spring and autumn periods in central Apennines, northern Italy. Full article

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16 pages, 3511 KiB

Open AccessArticle

Frequency-Aware Diffusion Model for Multi-Modal MRI Image Synthesis

by Mingfeng Jiang, Peihang Jia, Xin Huang, Zihan Yuan, Dongsheng Ruan, Feng Liu and Ling Xia

J. Imaging 2025, 11(5), 152; https://doi.org/10.3390/jimaging11050152 - 11 May 2025

Viewed by 118

Abstract

Magnetic Resonance Imaging (MRI) is a widely used, non-invasive imaging technology that plays a critical role in clinical diagnostics. Multi-modal MRI, which combines images from different modalities, enhances diagnostic accuracy by offering comprehensive tissue characterization. Meanwhile, multi-modal MRI enhances downstream tasks, like brain [...] Read more.

Magnetic Resonance Imaging (MRI) is a widely used, non-invasive imaging technology that plays a critical role in clinical diagnostics. Multi-modal MRI, which combines images from different modalities, enhances diagnostic accuracy by offering comprehensive tissue characterization. Meanwhile, multi-modal MRI enhances downstream tasks, like brain tumor segmentation and image reconstruction, by providing richer features. While recent advances in diffusion models (DMs) show potential for high-quality image translation, existing methods still struggle to preserve fine structural details and ensure accurate image synthesis in medical imaging. To address these challenges, we propose a Frequency-Aware Diffusion Model (FADM) for generating high-quality target modality MRI images from source modality images. The FADM incorporates a discrete wavelet transform within the diffusion model framework to extract both low- and high-frequency information from MRI images, enhancing the capture of tissue structural and textural features. Additionally, a wavelet downsampling layer and supervision module are incorporated to improve frequency awareness and optimize high-frequency detail extraction. Experimental results on the BraTS 2021 dataset and a 1.5T–3T MRI dataset demonstrate that the FADM outperforms existing generative models, particularly in preserving intricate brain structures and tumor regions while generating high-quality MRI images. Full article

(This article belongs to the Special Issue Advances in Medical Imaging and Machine Learning)

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25 pages, 6904 KiB

Open AccessArticle

A Weighted Facial Expression Analysis for Pain Level Estimation

by Parkpoom Chaisiriprasert and Nattapat Patchsuwan

J. Imaging 2025, 11(5), 151; https://doi.org/10.3390/jimaging11050151 - 9 May 2025

Viewed by 142

Abstract

Accurate assessment of pain intensity is critical, particularly for patients who are unable to verbally express their discomfort. This study proposes a novel weighted analytical framework that integrates facial expression analysis through action units (AUs) with a facial feature-based weighting mechanism to enhance [...] Read more.

Accurate assessment of pain intensity is critical, particularly for patients who are unable to verbally express their discomfort. This study proposes a novel weighted analytical framework that integrates facial expression analysis through action units (AUs) with a facial feature-based weighting mechanism to enhance the estimation of pain intensity. The proposed method was evaluated on a dataset comprising 4084 facial images from 25 individuals and demonstrated an average accuracy of 92.72% using the weighted pain level estimation model, in contrast to 83.37% achieved using conventional approaches. The observed improvements are primarily attributed to the strategic utilization of AU zones and expression-based weighting, which enable more precise differentiation between pain-related and non-pain-related facial movements. These findings underscore the efficacy of the proposed model in enhancing the accuracy and reliability of automated pain detection, especially in contexts where verbal communication is impaired or absent. Full article

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

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16 pages, 6530 KiB

Open AccessArticle

Reduction of Aerial Image Misalignment in Face-to-Face 3D Aerial Display

by Atsutoshi Kurihara and Yue Bao

J. Imaging 2025, 11(5), 150; https://doi.org/10.3390/jimaging11050150 - 9 May 2025

Viewed by 173

Abstract

A Micromirror Array Plate (MMAP) has been proposed as a type of aerial display that allows users to directly touch the floating image. However, the aerial images generated by this optical element have a limited viewing angle, making them difficult to use in [...] Read more.

A Micromirror Array Plate (MMAP) has been proposed as a type of aerial display that allows users to directly touch the floating image. However, the aerial images generated by this optical element have a limited viewing angle, making them difficult to use in face-to-face interactions. Conventional methods enable face-to-face usability by displaying multiple aerial images corresponding to different viewpoints. However, because these images are two-dimensional, they cannot be displayed at the same position due to the inherent characteristics of MMAP. An omnidirectional 3D autostereoscopic aerial display has been developed to address this issue, but it requires multiple expensive and specially shaped MMAPs to generate aerial images. To overcome this limitation, this study proposes a method that combines a single MMAP with integral photography (IP) to produce 3D aerial images with depth while reducing image misalignment. The experimental results demonstrate that the proposed method successfully displays a 3D aerial image using a single MMAP and reduces image misalignment to 1.1 mm. Full article

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26 pages, 17670 KiB

Open AccessArticle

Adaptive High-Precision 3D Reconstruction of Highly Reflective Mechanical Parts Based on Optimization of Exposure Time and Projection Intensity

by Ci He, Rong Lai, Jin Sun, Kazuhiro Izui, Zili Wang, Xiaojian Liu and Shuyou Zhang

J. Imaging 2025, 11(5), 149; https://doi.org/10.3390/jimaging11050149 - 8 May 2025

Viewed by 211

Abstract

This article is used to reconstruct mechanical parts with highly reflective surfaces. Three-dimensional reconstruction based on Phase Measuring Profilometry (PMP) is a key technology in non-contact optical measurement and is widely applied in the intelligent inspection of mechanical components. Due to the high [...] Read more.

This article is used to reconstruct mechanical parts with highly reflective surfaces. Three-dimensional reconstruction based on Phase Measuring Profilometry (PMP) is a key technology in non-contact optical measurement and is widely applied in the intelligent inspection of mechanical components. Due to the high reflectivity of metallic parts, direct utilization of the captured high-dynamic-range images often results in significant information loss in the oversaturated areas and excessive noise in the dark regions, leading to geometric defects and reduced accuracy in the reconstructed point clouds. Many image-fusion-based solutions have been proposed to solve these problems. However, unknown geometric structures and reflection characteristics of mechanical parts lead to the lack of effective guidance for the design of important imaging parameters. Therefore, an adaptive high-precision 3D reconstruction method of highly reflective mechanical parts based on optimization of exposure time and projection intensity is proposed in this article. The projection intensity is optimized to adapt the captured images to the linear dynamic range of the hardware. Image sequence under the obtained optimal intensities is fused using an integration of Genetic Algorithm and Stochastic Adam optimizer to maximize the image information entropy. Then, histogram-based analysis is employed to segment regions with similar reflective properties and determine the optimal exposure time. Experimental validation was carried out on three sets of typical mechanical components with diverse geometric characteristics and varying complexity. Compared with both non-saturated single-exposure techniques and conventional image fusion methods employing fixed attenuation steps, the proposed method reduced the average whisker range of reconstruction error by 51.18% and 25.09%, and decreased the median error by 42.48% and 25.42%, respectively. These experimental results verified the effectiveness and precision performance of the proposed method. Full article

(This article belongs to the Special Issue Geometry Reconstruction from Images (2nd Edition))

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17 pages, 3971 KiB

Open AccessArticle

3D-NASE: A Novel 3D CT Nasal Attention-Based Segmentation Ensemble

by Alessandro Pani, Luca Zedda, Davide Antonio Mura, Andrea Loddo and Cecilia Di Ruberto

J. Imaging 2025, 11(5), 148; https://doi.org/10.3390/jimaging11050148 - 7 May 2025

Viewed by 76

Abstract

Accurate segmentation of the nasal cavity and paranasal sinuses in CT scans is crucial for disease assessment, treatment planning, and surgical navigation. It also facilitates the advanced computational modeling of airflow dynamics and enhances endoscopic surgery preparation. This work presents a novel ensemble [...] Read more.

Accurate segmentation of the nasal cavity and paranasal sinuses in CT scans is crucial for disease assessment, treatment planning, and surgical navigation. It also facilitates the advanced computational modeling of airflow dynamics and enhances endoscopic surgery preparation. This work presents a novel ensemble framework for 3D nasal CT segmentation that synergistically combines CNN-based and transformer-based architectures, 3D-NASE. By integrating 3D U-Net, UNETR, Swin UNETR, SegResNet, DAF3D, and V-Net with majority and soft voting strategies, our approach leverages both local details and global context to improve segmentation accuracy and robustness. Results on the NasalSeg dataset demonstrate that the proposed ensemble method surpasses previous state-of-the-art results by achieving a

35.88 %

improvement in the DICE score and reducing the standard deviation by

4.53 %

. These promising results highlight the potential of our method to advance clinical workflows in diagnosis, treatment planning, and surgical navigation while also promoting further research into computationally efficient and highly accurate segmentation techniques. Full article

(This article belongs to the Special Issue Advances in Biomedical Image Processing and Artificial Intelligence for Computer-Aided Diagnosis in Medicine)

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30 pages, 25530 KiB

Open AccessArticle

Towards the Performance Characterization of a Robotic Multimodal Diagnostic Imaging System

by George Papaioannou, Christos Mitrogiannis, Mark Schweitzer, Nikolaos Michailidis, Maria Pappa, Pegah Khosravi, Apostolos Karantanas, Sean Starling and Christian Ruberg

J. Imaging 2025, 11(5), 147; https://doi.org/10.3390/jimaging11050147 - 7 May 2025

Viewed by 119

Abstract

Characterizing imaging performance requires a multidisciplinary approach that evaluates various interconnected parameters, including dosage optimization and dynamic accuracy. Radiation dose and dynamic accuracy are challenged by patient motion that results in poor image quality. These challenges are more prevalent in the brain/cardiac pediatric [...] Read more.

Characterizing imaging performance requires a multidisciplinary approach that evaluates various interconnected parameters, including dosage optimization and dynamic accuracy. Radiation dose and dynamic accuracy are challenged by patient motion that results in poor image quality. These challenges are more prevalent in the brain/cardiac pediatric patient imaging, as they relate to excess radiation dose that may be associated with various complications. Scanning vulnerable pediatric patients ought to eliminate anesthesia due to critical risks associated in some cases with intracranial hemorrhages, brain strokes, and congenital heart disease. Some pediatric imaging, however, requires prolonged scanning under anesthesia. It can often be a laborious, suboptimal process, with limited field of view and considerable dose. High dynamic accuracy is also necessary to diagnose tissue’s dynamic behavior beyond its static structural morphology. This study presents several performance characterization experiments from a new robotic multimodal imaging system using specially designed calibration methods at different system configurations. Additional musculoskeletal imaging and imaging from a pediatric brain stroke patient without anesthesia are presented for comparisons. The findings suggest that the system’s large dynamically controlled gantry enables scanning at full patient movement and with important improvements in scan times, accuracy, radiation dose, and the ability to image brain structures without anesthesia. This could position the system as a potential transformative tool in the pediatric interventional imaging landscape. Full article

(This article belongs to the Special Issue Celebrating the 10th Anniversary of the Journal of Imaging)

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17 pages, 2467 KiB

Open AccessArticle

Quantitative Ultrasound Texture Analysis of Breast Tumors: A Comparison of a Cart-Based and a Wireless Ultrasound Scanner

by David Alberico, Lakshmanan Sannachi, Maria Lourdes Anzola Pena, Joyce Yip, Laurentius O. Osapoetra, Schontal Halstead, Daniel DiCenzo, Sonal Gandhi, Frances Wright, Michael Oelze and Gregory J. Czarnota

J. Imaging 2025, 11(5), 146; https://doi.org/10.3390/jimaging11050146 - 6 May 2025

Viewed by 211

Abstract

Previous work has demonstrated quantitative ultrasound (QUS) analysis techniques for extracting features and texture features from ultrasound radiofrequency data which can be used to distinguish between benign and malignant breast masses. It is desirable that there be good agreement between estimates of such [...] Read more.

Previous work has demonstrated quantitative ultrasound (QUS) analysis techniques for extracting features and texture features from ultrasound radiofrequency data which can be used to distinguish between benign and malignant breast masses. It is desirable that there be good agreement between estimates of such features acquired using different ultrasound devices. Handheld ultrasound imaging systems are of particular interest as they are compact, relatively inexpensive, and highly portable. This study investigated the agreement between QUS parameters and texture features estimated from clinical ultrasound images of breast tumors acquired using two different ultrasound scanners: a traditional cart-based system and a wireless handheld ultrasound system. The 28 patients who participated were divided into two groups (benign and malignant). The reference phantom technique was used to produce functional estimates of the normalized power spectra and backscatter coefficient for each image. Root mean square differences of feature estimates were calculated for each cohort to quantify the level of feature variation attributable to tissue heterogeneity and differences in system imaging parameters. Cross-system statistical testing using the Mann–Whitney U test was performed on benign and malignant patient cohorts to assess the level of feature estimate agreement between systems, and the Bland–Altman method was employed to assess feature sets for systematic bias introduced by differences in imaging method. The range of p-values was 1.03 × 10⁻⁴ to 0.827 for the benign cohort and 3.03 × 10⁻¹⁰ to 0.958 for the malignant cohort. For both cohorts, all five of the primary QUS features (MBF, SS, SI, ASD, AAC) were found to be in agreement at the 5% confidence level. A total of 13 of the 20 QUS texture features (65%) were determined to exhibit statistically significant differences in the sample medians of estimates between systems at the 5% confidence level, with the remaining 7 texture features being in agreement. The results showed a comparable magnitude of feature variation between tissue heterogeneity and system effects, as well as a moderate level of statistical agreement between feature sets. Full article

(This article belongs to the Section Medical Imaging)

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8 pages, 1238 KiB

Open AccessArticle

Shear Wave Elastography for Parotid Glands: Quantitative Analysis of Shear Elastic Modulus in Relation to Age, Gender, and Internal Architecture in Patients with Oral Cancer

by Yuka Tanabe, Ai Shirai and Ichiro Ogura

J. Imaging 2025, 11(5), 145; https://doi.org/10.3390/jimaging11050145 - 4 May 2025

Viewed by 168

Abstract

Background: Recently, shear wave elastography (SWE) has been recognized as an effective tool for evaluating Sjögren’s syndrome (SS) patients. The purpose of this study was to assess the parotid glands with SWE, especially for quantitative analysis of shear elastic modulus in relation to [...] Read more.

Background: Recently, shear wave elastography (SWE) has been recognized as an effective tool for evaluating Sjögren’s syndrome (SS) patients. The purpose of this study was to assess the parotid glands with SWE, especially for quantitative analysis of shear elastic modulus in relation to age, gender, and internal architecture in patients with oral cancer to collect control data for SS. Methods: In total, 124 parotid glands of 62 patients with oral cancer were evaluated with SWE. The parotid glands were examined for the internal architecture (homogeneous or heterogeneous) on B-mode. The SWE allowed the operator to place regions of interest (ROIs) for parotid glands, and displayed automatically shear elastic modulus data (kPa) for each ROI. Gender and internal architecture were compared with the shear elastic modulus of the parotid glands by Mann–Whitney U-test. The comparison of age and shear elastic modulus was assessed using Spearman’s correlation coefficient. p < 0.05 was considered statistically significant. Results: The shear elastic modulus of the parotid glands was not significantly different for according to gender (males, 7.70 ± 2.22 kPa and females, 7.67 ± 2.41 kPa, p = 0.973) or internal architecture (homogeneous: 7.69 ± 2.25 kPa and heterogeneous: 7.72 ± 2.74 kPa, p = 0.981). Furthermore, the shear elastic modulus was not correlated with age (n = 124, R = −0.133, p = 0.139). Conclusion: Our study showed the control data of the shear elastic modulus of the parotid glands for SS. SWE is useful for the quantitative evaluation of the parotid glands. Full article

(This article belongs to the Section Medical Imaging)

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28 pages, 8775 KiB

Open AccessArticle

Motion-Perception Multi-Object Tracking (MPMOT): Enhancing Multi-Object Tracking Performance via Motion-Aware Data Association and Trajectory Connection

by Weijun Meng, Shuaipeng Duan, Sugang Ma and Bin Hu

J. Imaging 2025, 11(5), 144; https://doi.org/10.3390/jimaging11050144 - 3 May 2025

Viewed by 366

Abstract

Multiple Object Tracking (MOT) aims to detect and track multiple targets across consecutive video frames while preserving consistent object identities. While appearance-based approaches have achieved notable success, they often struggle in challenging conditions such as occlusions, motion blur, and the presence of visually [...] Read more.

Multiple Object Tracking (MOT) aims to detect and track multiple targets across consecutive video frames while preserving consistent object identities. While appearance-based approaches have achieved notable success, they often struggle in challenging conditions such as occlusions, motion blur, and the presence of visually similar objects, resulting in identity switches and fragmented trajectories. To address these limitations, we propose Motion-Perception Multi-Object Tracking (MPMOT), a motion-aware tracking framework that emphasizes robust motion modeling and adaptive association. MPMOT incorporates three core components: (1) a Gain Kalman Filter (GKF) that adaptively adjusts detection noise based on confidence scores, stabilizing motion prediction during uncertain observations; (2) an Adaptive Cost Matrix (ACM) that dynamically fuses motion and appearance cues during track–detection association, improving robustness under ambiguity; and (3) a Global Connection Model (GCM) that reconnects fragmented tracklets by modeling spatio-temporal consistency. Extensive experiments on the MOT16, MOT17, and MOT20 benchmarks demonstrate that MPMOT consistently outperforms state-of-the-art trackers, achieving IDF1 scores of 72.8% and 72.6% on MOT16 and MOT17, respectively, surpassing the widely used FairMOT baseline by 1.1% and 1.3%. Additionally, rigorous statistical validation through post hoc analysis confirms that MPMOT’s improvements in tracking accuracy and identity preservation are statistically significant across all datasets. MPMOT delivers these gains while maintaining real-time performance, making it a scalable and reliable solution for multi-object tracking in dynamic and crowded environments. Full article

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

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18 pages, 4899 KiB

Open AccessReview

Cardiac Magnetic Resonance in the Assessment of Atrial Cardiomyopathy and Pulmonary Vein Isolation Planning for Atrial Fibrillation

by Nicola Pegoraro, Serena Chiarello, Riccardo Bisi, Giuseppe Muscogiuri, Matteo Bertini, Aldo Carnevale, Melchiore Giganti and Alberto Cossu

J. Imaging 2025, 11(5), 143; https://doi.org/10.3390/jimaging11050143 - 2 May 2025

Viewed by 261

Abstract

Atrial fibrillation (AF) is the most frequently observed type of arrhythmia among adults, and its absolute prevalence is steadily rising in close association with the aging of the population, with its prevalence varying from 2% in the general population to 10–12% among the [...] Read more.

Atrial fibrillation (AF) is the most frequently observed type of arrhythmia among adults, and its absolute prevalence is steadily rising in close association with the aging of the population, with its prevalence varying from 2% in the general population to 10–12% among the elderly. The relatively new concepts of ‘atrial cardiomyopathy’ and “AF-related atrial cardiomyopathy”, along with the growing body of knowledge regarding remodeling, function, and tissue characterization, highlight the need for novel approaches to the diagnostic process as well as in the therapeutic guidance and monitoring of atrial arrhythmias. Advanced imaging techniques, particularly cardiac magnetic resonance (CMR) imaging, have emerged as pivotal in the detailed assessment of atrial structure and function. CMR facilitates the precise measurement of left atrial volume and morphology, which are critical predictors of AF recurrence post-intervention. Furthermore, it enables the evaluation of atrial fibrosis using late gadolinium enhancement (LGE), offering a non-invasive method to assess the severity and distribution of fibrotic tissue. The possibility of an accurate CMR pulmonary vein anatomy mapping enhances the precision of pulmonary vein isolation procedures, potentially improving outcomes in AF management. This review underlines the integration of novel diagnostic tools in enhancing the understanding and management of AF, advocating for a shift towards more personalized and effective therapeutic programs. 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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16 pages, 1139 KiB

Open AccessArticle

ARAN: Age-Restricted Anonymized Dataset of Children Images and Body Measurements

by Hezha H. MohammedKhan, Cascha Van Wanrooij, Eric O. Postma, Çiçek Güven, Marleen Balvert, Heersh Raof Saeed and Chenar Omer Ali Al Jaf

J. Imaging 2025, 11(5), 142; https://doi.org/10.3390/jimaging11050142 - 30 Apr 2025

Viewed by 259

Abstract

Precisely estimating a child’s body measurements and weight from a single image is useful in pediatrics for monitoring growth and detecting early signs of malnutrition. The development of estimation models for this task is hampered by the unavailability of a labeled image dataset [...] Read more.

Precisely estimating a child’s body measurements and weight from a single image is useful in pediatrics for monitoring growth and detecting early signs of malnutrition. The development of estimation models for this task is hampered by the unavailability of a labeled image dataset to support supervised learning. This paper introduces the “Age-Restricted Anonymized” (ARAN) dataset, the first labeled image dataset of children with body measurements approved by an ethics committee under the European General Data Protection Regulation guidelines. The ARAN dataset consists of images of 512 children aged 16 to 98 months, each captured from four different viewpoints, i.e., 2048 images in total. The dataset is anonymized manually on the spot through a face mask and includes each child’s height, weight, age, waist circumference, and head circumference measurements. The dataset is a solid foundation for developing prediction models for various tasks related to these measurements; it addresses the gap in computer vision tasks related to body measurements as it is significantly larger than any other comparable dataset of children, along with diverse viewpoints. To create a suitable reference, we trained state-of-the-art deep learning algorithms on the ARAN dataset to predict body measurements from the images. The best results are obtained by a DenseNet121 model achieving competitive estimates for the body measurements, outperforming state-of-the-art results on similar tasks. The ARAN dataset is developed as part of a collaboration to create a mobile app to measure children’s growth and detect early signs of malnutrition, contributing to the United Nations Sustainable Development Goals. Full article

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21 pages, 827 KiB

Open AccessReview

AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction

by Abdussalam Elhanashi, Sergio Saponara, Qinghe Zheng, Nawal Almutairi, Yashbir Singh, Shiba Kuanar, Farzana Ali, Orhan Unal and Shahriar Faghani

J. Imaging 2025, 11(5), 141; https://doi.org/10.3390/jimaging11050141 - 30 Apr 2025

Viewed by 374

Abstract

Artificial intelligence (AI)-based object detection in radiology can assist in clinical diagnosis and treatment planning. This article examines the AI-based object detection models currently used in many imaging modalities, including X-ray Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasound (US). The key [...] Read more.

Artificial intelligence (AI)-based object detection in radiology can assist in clinical diagnosis and treatment planning. This article examines the AI-based object detection models currently used in many imaging modalities, including X-ray Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasound (US). The key models from the convolutional neural network (CNN) as well as the contemporary transformer and hybrid models are analyzed based on their ability to detect pathological features, such as tumors, lesions, and tissue abnormalities. In addition, this review offers a closer look at the strengths and weaknesses of these models in terms of accuracy, robustness, and speed in real clinical settings. The common issues related to these models, including limited data, annotation quality, and interpretability of AI decisions, are discussed in detail. Moreover, the need for strong applicable models across different populations and imaging modalities are addressed. The importance of privacy and ethics in general data use as well as safety and regulations for healthcare data are emphasized. The future potential of these models lies in their accessibility in low resource settings, usability in shared learning spaces while maintaining privacy, and improvement in diagnostic accuracy through multimodal learning. This review also highlights the importance of interdisciplinary collaboration among artificial intelligence researchers, radiologists, and policymakers. Such cooperation is essential to address current challenges and to fully realize the potential of AI-based object detection in radiology. Full article

(This article belongs to the Special Issue Learning and Optimization for Medical Imaging)

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20 pages, 17085 KiB

Open AccessArticle

Research on Digital Orthophoto Production Technology for Indoor Murals in the Context of Climate Change and Environmental Protection

by Xiwang Zhou, Yongming Yang and Dingfei Yan

J. Imaging 2025, 11(5), 140; https://doi.org/10.3390/jimaging11050140 - 30 Apr 2025

Viewed by 156

Abstract

In response to the urgent need for the sustainable conservation of cultural heritage against the backdrop of climate change and environmental degradation, this study proposes a low-cost, non-destructive digital recording method for murals based on close-range photogrammetry. By integrating non-metric digital cameras, total [...] Read more.

In response to the urgent need for the sustainable conservation of cultural heritage against the backdrop of climate change and environmental degradation, this study proposes a low-cost, non-destructive digital recording method for murals based on close-range photogrammetry. By integrating non-metric digital cameras, total stations, and spatial coordinate transformation models, high-precision digital orthophoto generation for indoor murals was achieved. Experimental results show that the resolution error of this method is 0.02 mm, with root mean square errors (RMSE) of 3.51 mm and 2.77 mm in the X and Y directions, respectively, meeting the precision requirements for cultural heritage conservation. Compared to traditional laser scanning technology, the energy consumption of the equipment in this study is significantly reduced, and the use of chemical reagents is avoided, thereby minimizing the carbon footprint and environmental impact during the recording process. This provides a green technological solution to address climate change. Additionally, the low-cost nature of non-metric cameras offers a feasible option for cultural heritage conservation institutions with limited resources, promoting equity and accessibility in heritage protection amid global climate challenges. This technology provides sustainable data support for long-term monitoring, virtual restoration, and public digital display of murals while also offering rich data resources for virtual cultural tourism, public education, and scientific research. It demonstrates broad application potential in the context of climate change and environmental protection, contributing to the green transformation and sustainable development of cultural tourism. Full article

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21 pages, 9110 KiB

Open AccessArticle

SwinTCS: A Swin Transformer Approach to Compressive Sensing with Non-Local Denoising

by Xiuying Li, Haoze Li, Hongwei Liao, Zhufeng Suo, Xuesong Chen and Jiameng Han

J. Imaging 2025, 11(5), 139; https://doi.org/10.3390/jimaging11050139 - 29 Apr 2025

Viewed by 219

Abstract

In the era of the Internet of Things (IoT), the rapid growth of interconnected devices has intensified the demand for efficient data acquisition and processing techniques. Compressive Sensing (CS) has emerged as a promising approach for simultaneous signal acquisition and dimensionality reduction, particularly [...] Read more.

In the era of the Internet of Things (IoT), the rapid growth of interconnected devices has intensified the demand for efficient data acquisition and processing techniques. Compressive Sensing (CS) has emerged as a promising approach for simultaneous signal acquisition and dimensionality reduction, particularly in multimedia applications. In response to the challenges presented by traditional CS reconstruction methods, such as boundary artifacts and limited robustness, we propose a novel hierarchical deep learning framework, SwinTCS, for CS-aware image reconstruction. Leveraging the Swin Transformer architecture, SwinTCS integrates a hierarchical feature representation strategy to enhance global contextual modeling while maintaining computational efficiency. Moreover, to better capture local features of images, we introduce an auxiliary convolutional neural network (CNN). Additionally, for suppressing noise and improving reconstruction quality in high-compression scenarios, we incorporate a Non-Local Means Denoising module. The experimental results on multiple public benchmark datasets indicate that SwinTCS surpasses State-of-the-Art (SOTA) methods across various evaluation metrics, thereby confirming its superior performance. Full article

(This article belongs to the Topic Intelligent Image Processing Technology)

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19 pages, 6412 KiB

Open AccessArticle

Design of a Novel Conditional Noise Predictor for Image Super-Resolution Reconstruction Based on DDPM

by Jiyan Zhang, Hua Sun, Haiyang Fan, Yujie Xiong and Jiaqi Zhang

J. Imaging 2025, 11(5), 138; https://doi.org/10.3390/jimaging11050138 - 29 Apr 2025

Viewed by 283

Abstract

Image super-resolution (SR) reconstruction is a critical task aimed at enhancing low-quality images to obtain high-quality counterparts. Existing denoising diffusion models have demonstrated commendable performance in handling image SR reconstruction tasks; however, they often require thousands—or even more—diffusion sampling steps, significantly prolonging the [...] Read more.

Image super-resolution (SR) reconstruction is a critical task aimed at enhancing low-quality images to obtain high-quality counterparts. Existing denoising diffusion models have demonstrated commendable performance in handling image SR reconstruction tasks; however, they often require thousands—or even more—diffusion sampling steps, significantly prolonging the training duration for the denoising diffusion model. Conversely, reducing the number of diffusion steps may lead to the loss of intricate texture features in the generated images, resulting in overly smooth outputs despite improving the training efficiency. To address these challenges, we introduce a novel diffusion model named RapidDiff. RapidDiff uses a state-of-the-art conditional noise predictor (CNP) to predict the noise distribution at a level that closely resembles the real noise properties, thereby reducing the problem of high-variance noise produced by U-Net decoders during the noise prediction stage. Additionally, RapidDiff enhances the efficiency of image SR reconstruction by focusing on the residuals between high-resolution (HR) and low-resolution (LR) images. Experimental analyses confirm that our proposed RapidDiff model achieves performance that is either superior or comparable to that of the most advanced models that are currently available, as demonstrated on both the ImageNet dataset and the Alsat-2b dataset. Full article

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

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15 pages, 2054 KiB

Open AccessArticle

Deep-Learning Approaches for Cervical Cytology Nuclei Segmentation in Whole Slide Images

by Andrés Mosquera-Zamudio, Sandra Cancino, Guillermo Cárdenas-Montoya, Juan D. Garcia-Arteaga, Carlos Zambrano-Betancourt and Rafael Parra-Medina

J. Imaging 2025, 11(5), 137; https://doi.org/10.3390/jimaging11050137 - 29 Apr 2025

Viewed by 419

Abstract

Whole-slide imaging (WSI) in cytopathology poses challenges related to segmentation accuracy, computational efficiency, and image acquisition artifacts. This study aims to evaluate the performance of deep-learning models for instance segmentation in cervical cytology, benchmarking them against state-of-the-art methods on both public and institutional [...] Read more.

Whole-slide imaging (WSI) in cytopathology poses challenges related to segmentation accuracy, computational efficiency, and image acquisition artifacts. This study aims to evaluate the performance of deep-learning models for instance segmentation in cervical cytology, benchmarking them against state-of-the-art methods on both public and institutional datasets. We tested three architectures—U-Net, vision transformer (ViT), and Detectron2—and evaluated their performance on the ISBI 2014 and CNseg datasets using panoptic quality (PQ), dice similarity coefficient (DSC), and intersection over union (IoU). All models were trained on CNseg and tested on an independent institutional dataset. Data preprocessing involved manual annotation using QuPath, patch extraction guided by GeoJSON files, and exclusion of regions containing less than 60% cytologic material. Our models achieved superior segmentation performance on public datasets, reaching up to 98% PQ. Performance decreased on the institutional dataset, likely due to differences in image acquisition and the presence of blurred nuclei. Nevertheless, the models were able to detect blurred nuclei, highlighting their robustness in suboptimal imaging conditions. In conclusion, the proposed models offer an accurate and efficient solution for instance segmentation in cytology WSI. These results support the development of reliable AI-powered tools for digital cytology, with potential applications in automated screening and diagnostic workflows. Full article

(This article belongs to the Special Issue Deep Learning in Biomedical Image Segmentation and Classification: Advancements, Challenges and Applications)

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29 pages, 63247 KiB

Open AccessArticle

Minimizing Bleed-Through Effect in Medieval Manuscripts with Machine Learning and Robust Statistics

by Adriano Ettari, Massimo Brescia, Stefania Conte, Yahya Momtaz and Guido Russo

J. Imaging 2025, 11(5), 136; https://doi.org/10.3390/jimaging11050136 - 28 Apr 2025

Viewed by 234

Abstract

Over the last decades, plenty of ancient manuscripts have been digitized all over the world, and particularly in Europe. The fruition of these huge digital archives is often limited by the bleed-through effect due to the acid nature of the inks used, resulting [...] Read more.

Over the last decades, plenty of ancient manuscripts have been digitized all over the world, and particularly in Europe. The fruition of these huge digital archives is often limited by the bleed-through effect due to the acid nature of the inks used, resulting in very noisy images. Several authors have recently worked on bleed-through removal, using different approaches. With the aim of developing a bleed-through removal tool, capable of batch application on a large number of images, of the order of hundred thousands, we used machine learning and robust statistical methods with four different methods, and applied them to two medieval manuscripts. The methods used are (i) non-local means (NLM); (ii) Gaussian mixture models (GMMs); (iii) biweight estimation; and (iv) Gaussian blur. The application of these methods to the two quoted manuscripts shows that these methods are, in general, quite effective in bleed-through removal, but the selection of the method has to be performed according to the characteristics of the manuscript, e.g., if there is no ink fading and the difference between bleed-through pixels and the foreground text is clear, we can use a stronger model without the risk of losing important information. Conversely, if the distinction between bleed-through and foreground pixels is less pronounced, it is better to use a weaker model to preserve useful details. Full article

(This article belongs to the Section Document Analysis and Processing)

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17 pages, 2046 KiB

Open AccessArticle

Breast Lesion Detection Using Weakly Dependent Customized Features and Machine Learning Models with Explainable Artificial Intelligence

by Simona Moldovanu, Dan Munteanu, Keka C. Biswas and Luminita Moraru

J. Imaging 2025, 11(5), 135; https://doi.org/10.3390/jimaging11050135 - 28 Apr 2025

Viewed by 239

Abstract

This research proposes a novel strategy for accurate breast lesion classification that combines explainable artificial intelligence (XAI), machine learning (ML) classifiers, and customized weakly dependent features from ultrasound (BU) images. Two new weakly dependent feature classes are proposed to improve the diagnostic accuracy [...] Read more.

This research proposes a novel strategy for accurate breast lesion classification that combines explainable artificial intelligence (XAI), machine learning (ML) classifiers, and customized weakly dependent features from ultrasound (BU) images. Two new weakly dependent feature classes are proposed to improve the diagnostic accuracy and diversify the training data. These are based on image intensity variations and the area of bounded partitions and provide complementary rather than overlapping information. ML classifiers such as Random Forest (RF), Extreme Gradient Boosting (XGB), Gradient Boosting Classifiers (GBC), and LASSO regression were trained with both customized feature classes. To validate the reliability of our study and the results obtained, we conducted a statistical analysis using the McNemar test. Later, an XAI model was combined with ML to tackle the influence of certain features, the constraints of feature selection, and the interpretability capabilities across various ML models. LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) models were used in the XAI process to enhance the transparency and interpretation in clinical decision-making. The results revealed common relevant features for the malignant class, consistently identified by all of the classifiers, and for the benign class. However, we observed variations in the feature importance rankings across the different classifiers. Furthermore, our study demonstrates that the correlation between dependent features does not impact explainability. Full article

(This article belongs to the Special Issue Celebrating the 10th Anniversary of the Journal of Imaging)

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22 pages, 7640 KiB

Open AccessArticle

Bilingual Sign Language Recognition: A YOLOv11-Based Model for Bangla and English Alphabets

by Nawshin Navin, Fahmid Al Farid, Raiyen Z. Rakin, Sadman S. Tanzim, Mashrur Rahman, Shakila Rahman, Jia Uddin and Hezerul Abdul Karim

J. Imaging 2025, 11(5), 134; https://doi.org/10.3390/jimaging11050134 - 27 Apr 2025

Viewed by 675

Abstract

Communication through sign language effectively helps both hearing- and speaking-impaired individuals connect. However, there are problems with the interlingual communication between Bangla Sign Language (BdSL) and English Sign Language (ASL) due to the absence of a unified system. This study aims to introduce [...] Read more.

Communication through sign language effectively helps both hearing- and speaking-impaired individuals connect. However, there are problems with the interlingual communication between Bangla Sign Language (BdSL) and English Sign Language (ASL) due to the absence of a unified system. This study aims to introduce a detection system that incorporates these two sign languages to enhance the flow of communication for those who use these forms of sign language. This study developed and tested a deep learning-based sign-language detection system that can recognize both BdSL and ASL alphabets concurrently in real time. The approach uses a YOLOv11 object detection architecture that has been trained with an open-source dataset on a set of 9556 images containing 64 different letter signs from both languages. Data preprocessing was applied to enhance the performance of the model. Evaluation criteria, including the precision, recall, mAP, and other parameter values were also computed to evaluate the model. The performance analysis of the proposed method shows a precision of 99.12% and average recall rates of 99.63% in 30 epochs. The studies show that the proposed model outperforms the current techniques in sign language recognition (SLR) and can be used in communicating assistive technologies and human–computer interaction systems. Full article

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

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30 pages, 5602 KiB

Open AccessReview

A Comprehensive Review on Document Image Binarization

by Bilal Bataineh, Mohamed Tounsi, Nuha Zamzami, Jehan Janbi, Waleed Abdel Karim Abu-ain, Tarik AbuAin and Shaima Elnazer

J. Imaging 2025, 11(5), 133; https://doi.org/10.3390/jimaging11050133 - 26 Apr 2025

Viewed by 506

Abstract

In today’s digital age, the conversion of hardcopy documents into digital formats is widespread. This process involves electronically scanning and storing large volumes of documents. These documents come from various sources, including records and reports, camera-captured text and screen snapshots, official documents, newspapers, [...] Read more.

In today’s digital age, the conversion of hardcopy documents into digital formats is widespread. This process involves electronically scanning and storing large volumes of documents. These documents come from various sources, including records and reports, camera-captured text and screen snapshots, official documents, newspapers, medical reports, music scores, and more. In the domain of document analysis techniques, an essential step is document image binarization. Its goal is to eliminate unnecessary data from images and preserve only the text. Despite the existence of multiple techniques for binarization, the presence of degradation in document images can hinder their efficacy. The objective of this work is to provide an extensive review and analysis of the document binarization field, emphasizing its importance and addressing the challenges encountered during the image binarization process. Additionally, it provides insights into techniques and methods employed for image binarization. The current paper also introduces benchmark datasets for evaluating binarization accuracy, model training, evaluation metrics, and the effectiveness of recent methods. Full article

(This article belongs to the Section Document Analysis and Processing)

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24 pages, 4056 KiB

Open AccessArticle

Unveiling the Ultimate Meme Recipe: Image Embeddings for Identifying Top Meme Templates from r/Memes

by Jan Sawicki

J. Imaging 2025, 11(5), 132; https://doi.org/10.3390/jimaging11050132 - 23 Apr 2025

Viewed by 419

Abstract

Meme analysis, particularly identifying top meme templates, is crucial for understanding digital culture, communication trends, and the spread of online humor, as memes serve as units of cultural transmission that shape public discourse. Tracking popular templates enables researchers to examine their role in [...] Read more.

Meme analysis, particularly identifying top meme templates, is crucial for understanding digital culture, communication trends, and the spread of online humor, as memes serve as units of cultural transmission that shape public discourse. Tracking popular templates enables researchers to examine their role in social engagement, ideological framing, and viral dynamics within digital ecosystems. This study explored the viral nature of memes by analyzing a large dataset of over 1.5 million meme submissions from Reddit’s r/memes subreddit, spanning from January 2021 to July 2024. The focus was on uncovering the most popular meme templates by applying advanced image processing techniques. Apart from building an overall understanding of the memesphere, the main contribution was a selection of top meme templates providing a recipe for the best meme template for the meme creators (memesters). Using Vision Transformer (ViT) models, visual features of memes were analyzed without the influence of text, and memes were grouped into 1000 clusters that represented distinct templates. By combining image captioning and keyword extraction methods, key characteristics of the templates were identified, highlighting those with the most visual consistency. A deeper examination of the most popular memes revealed that factors like timing, cultural relevance, and references to current events played a significant role in their virality. Although user identity had limited influence on meme success, a closer look at contributors revealed an interesting pattern of a bot account and two prominent users. Ultimately, the study pinpointed the ten most popular meme templates, many of which were based on pop culture, offering insights into what makes a meme likely to go viral in today’s digital culture. Full article

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

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11 pages, 1645 KiB

Open AccessCommunication

Improvements in Image Registration, Segmentation, and Artifact Removal in ThermOcular Imaging System

by Navid Shahsavari, Ehsan Zare Bidaki, Alexander Wong and Paul J. Murphy

J. Imaging 2025, 11(5), 131; https://doi.org/10.3390/jimaging11050131 - 23 Apr 2025

Viewed by 239

Abstract

The assessment of ocular surface temperature (OST) plays a pivotal role in the diagnosis and management of various ocular diseases. This paper introduces significant enhancements to the ThermOcular system, initially developed for precise OST measurement using infrared (IR) thermography. These advancements focus on [...] Read more.

The assessment of ocular surface temperature (OST) plays a pivotal role in the diagnosis and management of various ocular diseases. This paper introduces significant enhancements to the ThermOcular system, initially developed for precise OST measurement using infrared (IR) thermography. These advancements focus on accuracy improvements that reduce user dependency and increase the system’s diagnostic capabilities. A novel addition to the system includes the use of EyeTags, which assist clinicians in selecting control points more easily, thus reducing errors associated with manual selection. Furthermore, the integration of state-of-the-art semantic segmentation models trained on the newest dataset is explored. Among these, the OCRNet-HRNet-w18 model achieved a segmentation accuracy of 96.21% MIOU, highlighting the effectiveness of the improved pipeline. Additionally, the challenge of eliminating eyelashes in IR frames, which cause artifactual measurement errors in OST assessments, is addressed. Through a newly developed method, the influence of eyelashes is eliminated, thereby enhancing the precision of temperature readings. Moreover, an algorithm for blink detection and elimination is implemented, significantly improving upon the basic methods previously utilized. These innovations not only enhance the reliability of OST measurements, but also contribute to the system’s efficiency and diagnostic accuracy, marking a significant step forward in ocular health monitoring and diagnostics. Full article

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

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13 pages, 405 KiB

Open AccessArticle

Correlation Between SCORE2-Diabetes and Coronary Artery Calcium Score in Patients with Type 2 Diabetes Mellitus: A Cross-Sectional Study in Vietnam

by Hung Phi Truong, Hoang Minh Tran, Thuan Huynh, Dung N. Q. Nguyen, Dung Thuong Ho, Cuong Cao Tran, Sang Van Nguyen and Tuan Minh Vo

J. Imaging 2025, 11(5), 130; https://doi.org/10.3390/jimaging11050130 - 23 Apr 2025

Viewed by 902

Abstract

(1) Background: The SCORE2-Diabetes model has been developed as an effective tool to estimate the 10-year cardiovascular risk in patients with diabetes. Coronary computed tomography angiography (CCTA) and its derived Coronary Artery Calcium Score (CACS) are widely used non-invasive imaging tools for assessing [...] Read more.

(1) Background: The SCORE2-Diabetes model has been developed as an effective tool to estimate the 10-year cardiovascular risk in patients with diabetes. Coronary computed tomography angiography (CCTA) and its derived Coronary Artery Calcium Score (CACS) are widely used non-invasive imaging tools for assessing coronary artery disease (CAD). This study aimed to evaluate the correlation between CACS and SCORE2-Diabetes in patients with T2DM. (2) Methods: A cross-sectional study was conducted from October 2023 to May 2024. We included patients aged 40 to 69 years with T2DM who underwent a coronary multislice CT scan due to atypical angina. The correlation between CACS and SCORE2-Diabetes was analyzed using Spearman’s rank correlation coefficient. (3) Results: A total of 100 patients met the inclusion criteria, including 71 males and 29 females, with a mean age of 61.9 ± 5.4 years. The differences in CACS and SCORE2-Diabetes among different degrees of coronary artery stenosis were statistically significant (p < 0.05). A statistically significant but weak positive correlation was observed between CACS and SCORE2-Diabetes across all risk categories, with Spearman’s rank correlation coefficients ranging from 0.27 to 0.28 (p < 0.01). (4) Conclusions: Despite the weak correlation between CACS and SCORE2-Diabetes, understanding their relationship and independent associations with disease severity is valuable. The combination of these two tools may warrant investigation in future studies to potentially enhance cardiovascular risk assessment in T2DM patients. Full article

(This article belongs to the Section Medical Imaging)

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26 pages, 13259 KiB

Open AccessArticle

Method for Indoor Seismic Intensity Assessment Based on Image Processing Techniques

by Jingsong Yang, Guoxing Lu, Yanxiong Wu and Fumin Peng

J. Imaging 2025, 11(5), 129; https://doi.org/10.3390/jimaging11050129 - 22 Apr 2025

Viewed by 231

Abstract

The seismic intensity experienced indoors directly reflects the degree of damage to the internal structure of the building. The current classification of indoor strength relies on manual surveys and qualitative descriptions of macro phenomena, which are subjective, unable to capture real-time dynamic changes [...] Read more.

The seismic intensity experienced indoors directly reflects the degree of damage to the internal structure of the building. The current classification of indoor strength relies on manual surveys and qualitative descriptions of macro phenomena, which are subjective, unable to capture real-time dynamic changes in the room, and lack quantitative indicators. In this paper, we present the Image Evaluation of Seismic Intensity (IESI) method, which is based on image processing technology. This method mainly evaluates the degree of responses from objects by identifying the percentage of movement of different types of objects in images taken before and after an earthquake. In order to further improve the recognition accuracy, we combined the camera vibration degree and the object displacement between images to correct the generated earthquake intensity level estimation, so as to achieve the rapid assessment of an earthquake’s intensity indoors. We took, as an example, 29 sets of seismic data from different scenarios. We used the IESI method to evaluate the seismic intensity of these scenarios. Compared with the seismic intensity evaluation results obtained by the Post-disaster Sensor-based Condition Assessment of Buildings (PSAB) and the Image-based Seismic Damage Assessment System (IDEAS) methods, the accuracy of the IESI method was higher by more than 30%, and its accuracy reached 97%. The universality of the IESI method in different indoor scenarios was demonstrated. In a low-intensity evaluation experiment, the accuracy of the IESI method also reached 91%, which verifies the reliability of the IESI method in low-intensity regions. Full article

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

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J. Imaging, Volume 11, Issue 5 (May 2025) – 32 articles

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