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Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective
Evaluating Super-Resolution Models in Biomedical Imaging: Applications and Performance in Segmentation and Classification
Optimizing Digital Image Quality for Improved Skin Cancer Detection
Towards the Performance Characterization of a Robotic Multimodal Diagnostic Imaging System
Breast Lesion Detection Using Weakly Dependent Customized Features and Machine Learning Models with Explainable Artificial Intelligence

Journal Description

Journal of Imaging

Journal of Imaging is an international, multi/interdisciplinary, peer-reviewed, open access journal of imaging techniques published online monthly by MDPI.

Open Accessfree for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), PubMed, PMC, dblp, Inspec, Ei Compendex, and other databases.
Journal Rank: JCR - Q2 (Imaging Science and Photographic Technology) / CiteScore - Q1 (Radiology, Nuclear Medicine and Imaging)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.3 days after submission; acceptance to publication is undertaken in 3.5 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 3.3 (2024); 5-Year Impact Factor: 3.3 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2313-433X

Latest Articles

17 pages, 8033 KiB

Open AccessArticle

PU-DZMS: Point Cloud Upsampling via Dense Zoom Encoder and Multi-Scale Complementary Regression

by Shucong Li, Zhenyu Liu, Tianlei Wang and Zhiheng Zhou

J. Imaging 2025, 11(8), 270; https://doi.org/10.3390/jimaging11080270 - 12 Aug 2025

Point cloud imaging technology usually faces the problem of point cloud sparsity, which leads to a lack of important geometric detail. There are many point cloud upsampling networks that have been designed to solve this problem. However, the existing methods have limitations in [...] Read more.

Point cloud imaging technology usually faces the problem of point cloud sparsity, which leads to a lack of important geometric detail. There are many point cloud upsampling networks that have been designed to solve this problem. However, the existing methods have limitations in local–global relation understanding, leading to contour distortion and many local sparse regions. To this end, PU-DZMS is proposed with two components. (1) the Dense Zoom Encoder (DENZE) is designed to capture local–global features by using ZOOM Blocks with a dense connection. The main module in the ZOOM Block is the Zoom Encoder, which embeds a Transformer mechanism into the down–upsampling process to enhance local–global geometric features. The geometric edge of the point cloud would be clear under the DENZE. (2) The Multi-Scale Complementary Regression (MSCR) module is designed to expand the features and regress a dense point cloud. MSCR obtains the features’ geometric distribution differences across scales to ensure geometric continuity, and it regresses new points by adopting cross-scale residual learning. The local sparse regions of the point cloud would be reduced by the MSCR module. The experimental results on the PU-GAN dataset and the PU-Net dataset show that the proposed method performs well on point cloud upsampling tasks. Full article

(This article belongs to the Topic Applied Computer Vision and Pattern Recognition: 2nd Edition)

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24 pages, 948 KiB

Open AccessReview

A Review on Deep Learning Methods for Glioma Segmentation, Limitations, and Future Perspectives

by Cecilia Diana-Albelda, Álvaro García-Martín and Jesus Bescos

J. Imaging 2025, 11(8), 269; https://doi.org/10.3390/jimaging11080269 - 11 Aug 2025

Accurate and automated segmentation of gliomas from Magnetic Resonance Imaging (MRI) is crucial for effective diagnosis, treatment planning, and patient monitoring. However, the aggressive nature and morphological complexity of these tumors pose significant challenges that call for advanced segmentation techniques. This review provides [...] Read more.

Accurate and automated segmentation of gliomas from Magnetic Resonance Imaging (MRI) is crucial for effective diagnosis, treatment planning, and patient monitoring. However, the aggressive nature and morphological complexity of these tumors pose significant challenges that call for advanced segmentation techniques. This review provides a comprehensive analysis of Deep Learning (DL) methods for glioma segmentation, with a specific focus on bridging the gap between research performance and practical clinical deployment. We evaluate over 80 state-of-the-art models published up to 2025, categorizing them into CNN-based, Pure Transformer, and Hybrid CNN-Transformer architectures. The primary objective of this paper is to critically assess these models not only on their segmentation accuracy but also on their computational efficiency and suitability for real-world medical environments by incorporating hardware resource considerations. We present a comparison of model performance on the BraTS datasets benchmark and introduce a suitability analysis for top-performing models based on their robustness, efficiency, and completeness of tumor region delineation. By identifying current trends, limitations, and key trade-offs, this review offers future research directions aimed at optimizing the balance between technical performance and clinical usability to improve diagnostic outcomes for glioma patients. Full article

(This article belongs to the Section Medical Imaging)

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28 pages, 12443 KiB

Open AccessArticle

Research on the Accessibility of Different Colour Schemes for Web Resources for People with Colour Blindness

by Daiva Sajek, Olena Korotenko and Tetiana Kyrychok

J. Imaging 2025, 11(8), 268; https://doi.org/10.3390/jimaging11080268 - 11 Aug 2025

This study is devoted to the analysis of the perception of colour schemes of web resources by users with different types of colour blindness (colour vision deficiency). The purpose of this study is to develop recommendations for choosing the optimal colour scheme for [...] Read more.

This study is devoted to the analysis of the perception of colour schemes of web resources by users with different types of colour blindness (colour vision deficiency). The purpose of this study is to develop recommendations for choosing the optimal colour scheme for web resource design that will ensure the comfortable perception of content for the broadest possible audience, including users with colour vision deficiency of various types (deuteranopia and deuteranomaly, protanopia and protanomaly, tritanopia, and tritanomaly). This article presents the results of a survey of people with different colour vision deficiencies regarding the accessibility of web resources created using different colour schemes. The colour deviation value ∆E was calculated to objectively assess changes in the perception of different colour groups by people with colour vision impairments. The conclusions of this study emphasise the importance of taking into account the needs of users with colour vision impairments when developing web resources. Specific recommendations for choosing the best colour schemes for websites are also offered, which will help increase the accessibility and effectiveness of web content for users with different types of colour blindness. Full article

(This article belongs to the Special Issue Image and Video Processing for Blind and Visually Impaired)

17 pages, 7225 KiB

Open AccessArticle

Placido Sub-Pixel Edge Detection Algorithm Based on Enhanced Mexican Hat Wavelet Transform and Improved Zernike Moments

by Yujie Wang, Jinyu Liang, Yating Xiao, Xinfeng Liu, Jiale Li, Guangyu Cui and Quan Zhang

J. Imaging 2025, 11(8), 267; https://doi.org/10.3390/jimaging11080267 - 11 Aug 2025

In order to meet the high-precision location requirements of the corneal Placido ring edge in corneal topographic reconstruction, this paper proposes a sub-pixel edge detection algorithm based on multi-scale and multi-position enhanced Mexican Hat Wavelet Transform and improved Zernike moment. Firstly, the image [...] Read more.

In order to meet the high-precision location requirements of the corneal Placido ring edge in corneal topographic reconstruction, this paper proposes a sub-pixel edge detection algorithm based on multi-scale and multi-position enhanced Mexican Hat Wavelet Transform and improved Zernike moment. Firstly, the image undergoes preliminary processing using a multi-scale and multi-position enhanced Mexican Hat Wavelet Transform function. Subsequently, the preliminary edge information extracted is relocated based on the Zernike moments of a 9 × 9 template. Finally, two improved adaptive edge threshold algorithms are employed to determine the actual sub-pixel edge points of the image, thereby realizing sub-pixel edge detection for corneal Placido ring images. Through comparison and analysis of edge extraction results from real human eye images obtained using the algorithm proposed in this paper and those from other existing algorithms, it is observed that the average sub-pixel edge error of other algorithms is 0.286 pixels, whereas the proposed algorithm achieves an average error of only 0.094 pixels. Furthermore, the proposed algorithm demonstrates strong robustness against noise. Full article

(This article belongs to the Section Medical Imaging)

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14 pages, 2224 KiB

Open AccessArticle

Evaluation of Transfer Learning Efficacy for Surgical Suture Quality Classification on Limited Datasets

by Roman Ishchenko, Maksim Solopov, Andrey Popandopulo, Elizaveta Chechekhina, Viktor Turchin, Fedor Popivnenko, Aleksandr Ermak, Konstantyn Ladyk, Anton Konyashin, Kirill Golubitskiy, Aleksei Burtsev and Dmitry Filimonov

J. Imaging 2025, 11(8), 266; https://doi.org/10.3390/jimaging11080266 - 8 Aug 2025

This study evaluates the effectiveness of transfer learning with pre-trained convolutional neural networks (CNNs) for the automated binary classification of surgical suture quality (high-quality/low-quality) using photographs of three suture types: interrupted open vascular sutures (IOVS), continuous over-and-over open sutures (COOS), and interrupted laparoscopic [...] Read more.

This study evaluates the effectiveness of transfer learning with pre-trained convolutional neural networks (CNNs) for the automated binary classification of surgical suture quality (high-quality/low-quality) using photographs of three suture types: interrupted open vascular sutures (IOVS), continuous over-and-over open sutures (COOS), and interrupted laparoscopic sutures (ILS). To address the challenge of limited medical data, eight state-of-the-art CNN architectures—EfficientNetB0, ResNet50V2, MobileNetV3Large, VGG16, VGG19, InceptionV3, Xception, and DenseNet121—were trained and validated on small datasets (100–190 images per type) using 5-fold cross-validation. Performance was assessed using the F1-score, AUC-ROC, and a custom weighted stability-aware score (Score_adj). The results demonstrate that transfer learning achieves robust classification (F1 > 0.90 for IOVS/ILS, 0.79 for COOS) despite data scarcity. ResNet50V2, DenseNet121, and Xception were more stable by Score_adj, with ResNet50V2 achieving the highest AUC-ROC (0.959 ± 0.008) for IOVS internal view classification. GradCAM visualizations confirmed model focus on clinically relevant features (e.g., stitch uniformity, tissue apposition). These findings validate transfer learning as a powerful approach for developing objective, automated surgical skill assessment tools, reducing reliance on subjective expert evaluations while maintaining accuracy in resource-constrained settings. Full article

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

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19 pages, 12806 KiB

Open AccessArticle

A Vision Method for Detecting Citrus Separation Lines Using Line-Structured Light

by Qingcang Yu, Song Xue and Yang Zheng

J. Imaging 2025, 11(8), 265; https://doi.org/10.3390/jimaging11080265 - 8 Aug 2025

The detection of citrus separation lines is a crucial step in the citrus processing industry. Inspired by the achievements of line-structured light technology in surface defect detection, this paper proposes a method for detecting citrus separation lines based on line-structured light. Firstly, a [...] Read more.

The detection of citrus separation lines is a crucial step in the citrus processing industry. Inspired by the achievements of line-structured light technology in surface defect detection, this paper proposes a method for detecting citrus separation lines based on line-structured light. Firstly, a gamma-corrected Otsu method is employed to extract the laser stripe region from the image. Secondly, an improved skeleton extraction algorithm is employed to mitigate the bifurcation errors inherent in original skeleton extraction algorithms while simultaneously acquiring 3D point cloud data of the citrus surface. Finally, the least squares progressive iterative approximation algorithm is applied to approximate the ideal surface curve; subsequently, principal component analysis is used to derive the normals of this ideally fitted curve. The deviation between each point (along its corresponding normal direction) and the actual geometric characteristic curve is then adopted as a quantitative index for separation lines positioning. The average similarity between the extracted separation lines and the manually defined standard separation lines reaches 92.5%. In total, 95% of the points on the separation lines obtained by this method have an error of less than 4 pixels. Experimental results demonstrate that through quantitative deviation analysis of geometric features, automatic detection and positioning of the separation lines are achieved, satisfying the requirements of high precision and non-destructiveness for automatic citrus splitting. Full article

(This article belongs to the Topic Image Processing, Signal Processing and Their Applications)

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21 pages, 7306 KiB

Open AccessArticle

Systematic and Individualized Preparation of External Ear Canal Implants: Development and Validation of an Efficient and Accurate Automated Segmentation System

by Yanjing Luo, Mohammadtaha Kouchakinezhad, Felix Repp, Verena Scheper, Thomas Lenarz and Farnaz Matin-Mann

J. Imaging 2025, 11(8), 264; https://doi.org/10.3390/jimaging11080264 - 8 Aug 2025

External ear canal (EEC) stenosis, often associated with cholesteatoma, carries a high risk of postoperative restenosis despite surgical intervention. While individualized implants offer promise in preventing restenosis, the high morphological variability of EECs and the lack of standardized definitions hinder systematic implant design. [...] Read more.

External ear canal (EEC) stenosis, often associated with cholesteatoma, carries a high risk of postoperative restenosis despite surgical intervention. While individualized implants offer promise in preventing restenosis, the high morphological variability of EECs and the lack of standardized definitions hinder systematic implant design. This study aimed to characterize individual EEC morphology and to develop a validated automated segmentation system for efficient implant preparation. Reference datasets were first generated by manual segmentation using 3D Slicer^TM software version 5.2.2. Based on these, we developed a customized plugin capable of automatically identifying the maximal implantable region within the EEC and measuring its key dimensions. The accuracy of the plugin was assessed by comparing it with manual segmentation results in terms of shape, volume, length, and width. Validation was further performed using three temporal bone implantation experiments with 3D-Bioplotter©-fabricated EEC implants. The automated system demonstrated strong consistency with manual methods and significantly improved segmentation efficiency. The plugin-generated models enabled successful implant fabrication and placement in all validation tests. These results confirm the system’s clinical feasibility and support its use for individualized and systematic EEC implant design. The developed tool holds potential to improve surgical planning and reduce postoperative restenosis in EEC stenosis treatment. Full article

(This article belongs to the Special Issue Current Progress in Medical Image Segmentation)

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23 pages, 5644 KiB

Open AccessArticle

Enhancing YOLOv5 for Autonomous Driving: Efficient Attention-Based Object Detection on Edge Devices

by Mortda A. A. Adam and Jules R. Tapamo

J. Imaging 2025, 11(8), 263; https://doi.org/10.3390/jimaging11080263 - 8 Aug 2025

On-road vision-based systems rely on object detection to ensure vehicle safety and efficiency, making it an essential component of autonomous driving. Deep learning methods show high performance; however, they often require special hardware due to their large sizes and computational complexity, which makes [...] Read more.

On-road vision-based systems rely on object detection to ensure vehicle safety and efficiency, making it an essential component of autonomous driving. Deep learning methods show high performance; however, they often require special hardware due to their large sizes and computational complexity, which makes real-time deployment on edge devices expensive. This study proposes lightweight object detection models based on the YOLOv5s architecture, known for its speed and accuracy. The models integrate advanced channel attention strategies, specifically the ECA module and SE attention blocks, to enhance feature selection while minimizing computational overhead. Four models were developed and trained on the KITTI dataset. The models were analyzed using key evaluation metrics to assess their effectiveness in real-time autonomous driving scenarios, including precision, recall, and mean average precision (mAP). BaseECAx2 emerged as the most efficient model for edge devices, achieving the lowest GFLOPs (13) and smallest model size (9.1 MB) without sacrificing performance. The BaseSE-ECA model demonstrated outstanding accuracy in vehicle detection, reaching a precision of 96.69% and an mAP of 98.4%, making it ideal for high-precision autonomous driving scenarios. We also assessed the models’ robustness in more challenging environments by training and testing them on the BDD-100K dataset. While the models exhibited reduced performance in complex scenarios involving low-light conditions and motion blur, this evaluation highlights potential areas for improvement in challenging real-world driving conditions. This study bridges the gap between affordability and performance, presenting lightweight, cost-effective solutions for integration into real-time autonomous vehicle systems. Full article

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

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29 pages, 3842 KiB

Open AccessArticle

SABE-YOLO: Structure-Aware and Boundary-Enhanced YOLO for Weld Seam Instance Segmentation

by Rui Wen, Wu Xie, Yong Fan and Lanlan Shen

J. Imaging 2025, 11(8), 262; https://doi.org/10.3390/jimaging11080262 - 6 Aug 2025

Accurate weld seam recognition is essential in automated welding systems, as it directly affects path planning and welding quality. With the rapid advancement of industrial vision, weld seam instance segmentation has emerged as a prominent research focus in both academia and industry. However, [...] Read more.

Accurate weld seam recognition is essential in automated welding systems, as it directly affects path planning and welding quality. With the rapid advancement of industrial vision, weld seam instance segmentation has emerged as a prominent research focus in both academia and industry. However, existing approaches still face significant challenges in boundary perception and structural representation. Due to the inherently elongated shapes, complex geometries, and blurred edges of weld seams, current segmentation models often struggle to maintain high accuracy in practical applications. To address this issue, a novel structure-aware and boundary-enhanced YOLO (SABE-YOLO) is proposed for weld seam instance segmentation. First, a Structure-Aware Fusion Module (SAFM) is designed to enhance structural feature representation through strip pooling attention and element-wise multiplicative fusion, targeting the difficulty in extracting elongated and complex features. Second, a C2f-based Boundary-Enhanced Aggregation Module (C2f-BEAM) is constructed to improve edge feature sensitivity by integrating multi-scale boundary detail extraction, feature aggregation, and attention mechanisms. Finally, the inner minimum point distance-based intersection over union (Inner-MPDIoU) is introduced to improve localization accuracy for weld seam regions. Experimental results on the self-built weld seam image dataset show that SABE-YOLO outperforms YOLOv8n-Seg by 3 percentage points in the AP(50–95) metric, reaching 46.3%. Meanwhile, it maintains a low computational cost (18.3 GFLOPs) and a small number of parameters (6.6M), while achieving an inference speed of 127 FPS, demonstrating a favorable trade-off between segmentation accuracy and computational efficiency. The proposed method provides an effective solution for high-precision visual perception of complex weld seam structures and demonstrates strong potential for industrial application. Full article

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

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11 pages, 1947 KiB

Open AccessArticle

Quantitative Magnetic Resonance Imaging and Patient-Reported Outcomes in Patients Undergoing Hip Labral Repair or Reconstruction

by Kyle S. J. Jamar, Adam Peszek, Catherine C. Alder, Trevor J. Wait, Caleb J. Wipf, Carson L. Keeter, Stephanie W. Mayer, Charles P. Ho and James W. Genuario

J. Imaging 2025, 11(8), 261; https://doi.org/10.3390/jimaging11080261 - 5 Aug 2025

This study evaluates the relationship between preoperative cartilage quality, measured by T2 mapping, and patient-reported outcomes following labral tear treatment. We retrospectively reviewed patients aged 14–50 who underwent primary hip arthroscopy with either labral repair or reconstruction. Preoperative T2 values of femoral, acetabular, [...] Read more.

This study evaluates the relationship between preoperative cartilage quality, measured by T2 mapping, and patient-reported outcomes following labral tear treatment. We retrospectively reviewed patients aged 14–50 who underwent primary hip arthroscopy with either labral repair or reconstruction. Preoperative T2 values of femoral, acetabular, and labral tissue were assessed from MRI by blinded reviewers. International Hip Outcome Tool (iHOT-12) scores were collected preoperatively and up to two years postoperatively. Associations between T2 values and iHOT-12 scores were analyzed using univariate mixed linear models. Twenty-nine patients were included (mean age of 32.5 years, BMI 24 kg/m², 48.3% female, and 22 repairs). Across all patients, higher T2 values were associated with higher iHOT-12 scores at baseline and early postoperative timepoints (three months for cartilage and six months for labrum; p < 0.05). Lower T2 values were associated with higher 12- and 24-month iHOT-12 scores across all structures (p < 0.001). Similar trends were observed within the repair and reconstruction subgroups, with delayed negative associations correlating with worse tissue quality. T2 mapping showed time-dependent correlations with iHOT-12 scores, indicating that worse cartilage or labral quality predicts poorer long-term outcomes. These findings support the utility of T2 mapping as a preoperative tool for prognosis in hip preservation surgery. Full article

(This article belongs to the Special Issue New Developments in Musculoskeletal Imaging)

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19 pages, 7531 KiB

Open AccessArticle

Evaluating the Impact of 2D MRI Slice Orientation and Location on Alzheimer’s Disease Diagnosis Using a Lightweight Convolutional Neural Network

by Nadia A. Mohsin and Mohammed H. Abdulameer

J. Imaging 2025, 11(8), 260; https://doi.org/10.3390/jimaging11080260 - 5 Aug 2025

Accurate detection of Alzheimer’s disease (AD) is critical yet challenging for early medical intervention. Deep learning methods, especially convolutional neural networks (CNNs), have shown promising potential for improving diagnostic accuracy using magnetic resonance imaging (MRI). This study aims to identify the most informative [...] Read more.

Accurate detection of Alzheimer’s disease (AD) is critical yet challenging for early medical intervention. Deep learning methods, especially convolutional neural networks (CNNs), have shown promising potential for improving diagnostic accuracy using magnetic resonance imaging (MRI). This study aims to identify the most informative combination of MRI slice orientation and anatomical location for AD classification. We propose an automated framework that first selects the most relevant slices using a feature entropy-based method applied to activation maps from a pretrained CNN model. For classification, we employ a lightweight CNN architecture based on depthwise separable convolutions to efficiently analyze the selected 2D MRI slices extracted from preprocessed 3D brain scans. To further interpret model behavior, an attention mechanism is integrated to analyze which feature level contributes the most to the classification process. The model is evaluated on three binary tasks: AD vs. mild cognitive impairment (MCI), AD vs. cognitively normal (CN), and MCI vs. CN. The experimental results show the highest accuracy (97.4%) in distinguishing AD from CN when utilizing the selected slices from the ninth axial segment, followed by the tenth segment of coronal and sagittal orientations. These findings demonstrate the significance of slice location and orientation in MRI-based AD diagnosis and highlight the potential of lightweight CNNs for clinical use. Full article

(This article belongs to the Section AI in Imaging)

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23 pages, 22135 KiB

Open AccessArticle

Road Marking Damage Degree Detection Based on Boundary Features Enhanced and Asymmetric Large Field-of-View Contextual Features

by Zheng Wang, Ryojun Ikeura, Soichiro Hayakawa and Zhiliang Zhang

J. Imaging 2025, 11(8), 259; https://doi.org/10.3390/jimaging11080259 - 4 Aug 2025

Road markings, as critical components of transportation infrastructure, are crucial for ensuring traffic safety. Accurate quantification of their damage severity is vital for effective maintenance prioritization. However, existing methods are limited to detecting the presence of damage without assessing its extent. To address [...] Read more.

Road markings, as critical components of transportation infrastructure, are crucial for ensuring traffic safety. Accurate quantification of their damage severity is vital for effective maintenance prioritization. However, existing methods are limited to detecting the presence of damage without assessing its extent. To address this limitation, we propose a novel segmentation-based framework for estimating the degree of road marking damage. The method comprises two stages: segmentation of residual pixels from the damaged markings and segmentation of the intact markings region. This dual-segmentation strategy enables precise reconstruction and comparison for severity estimation. To enhance segmentation performance, we proposed two key modules: the Asymmetric Large Field-of-View Contextual (ALFVC) module, which captures rich multi-scale contextual features, and the supervised Boundary Feature Enhancement (BFE) module, which strengthens shape representation and boundary accuracy. The experimental results demonstrate that our method achieved an average segmentation accuracy of 89.44%, outperforming the baseline by 5.86 percentage points. Moreover, the damage quantification achieved a minimum error rate of just 0.22% on the proprietary dataset. The proposed approach was both effective and lightweight, providing valuable support for automated maintenance planning, and significantly improving the efficiency and precision of road marking management. Full article

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

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28 pages, 21813 KiB

Open AccessArticle

Adaptive RGB-D Semantic Segmentation with Skip-Connection Fusion for Indoor Staircase and Elevator Localization

by Zihan Zhu, Henghong Lin, Anastasia Ioannou and Tao Wang

J. Imaging 2025, 11(8), 258; https://doi.org/10.3390/jimaging11080258 - 4 Aug 2025

Accurate semantic segmentation of indoor architectural elements, such as staircases and elevators, is critical for safe and efficient robotic navigation, particularly in complex multi-floor environments. Traditional fusion methods struggle with occlusions, reflections, and low-contrast regions. In this paper, we propose a novel feature [...] Read more.

Accurate semantic segmentation of indoor architectural elements, such as staircases and elevators, is critical for safe and efficient robotic navigation, particularly in complex multi-floor environments. Traditional fusion methods struggle with occlusions, reflections, and low-contrast regions. In this paper, we propose a novel feature fusion module, Skip-Connection Fusion (SCF), that dynamically integrates RGB (Red, Green, Blue) and depth features through an adaptive weighting mechanism and skip-connection integration. This approach enables the model to selectively emphasize informative regions while suppressing noise, effectively addressing challenging conditions such as partially blocked staircases, glossy elevator doors, and dimly lit stair edges, which improves obstacle detection and supports reliable human–robot interaction in complex environments. Extensive experiments on a newly collected dataset demonstrate that SCF consistently outperforms state-of-the-art methods, including PSPNet and DeepLabv3, in both overall mIoU (mean Intersection over Union) and challenging-case performance. Specifically, our SCF module improves segmentation accuracy by 5.23% in the top 10% of challenging samples, highlighting its robustness in real-world conditions. Furthermore, we conduct a sensitivity analysis on the learnable weights, demonstrating their impact on segmentation quality across varying scene complexities. Our work provides a strong foundation for real-world applications in autonomous navigation, assistive robotics, and smart surveillance. Full article

(This article belongs to the Topic State-of-the-Art Object Detection, Tracking, and Recognition Techniques)

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20 pages, 4292 KiB

Open AccessArticle

A Novel Method for Analysing the Curvature of the Anterior Lens: Multi-Radial Scheimpflug Imaging and Custom Conic Fitting Algorithm

by María Arcas-Carbonell, Elvira Orduna-Hospital, María Mechó-García, Guisela Fernández-Espinosa and Ana Sanchez-Cano

J. Imaging 2025, 11(8), 257; https://doi.org/10.3390/jimaging11080257 - 1 Aug 2025

This study describes and validates a novel method for assessing anterior crystalline lens curvature along vertical and horizontal meridians using radial measurements derived from Scheimpflug imaging. The aim was to evaluate whether pupil diameter (PD), anterior lens curvature, and anterior chamber depth (ACD) [...] Read more.

This study describes and validates a novel method for assessing anterior crystalline lens curvature along vertical and horizontal meridians using radial measurements derived from Scheimpflug imaging. The aim was to evaluate whether pupil diameter (PD), anterior lens curvature, and anterior chamber depth (ACD) change during accommodation and whether these changes are age-dependent. A cross-sectional study was conducted on 104 right eyes from healthy participants aged 21–62 years. Sixteen radial images per eye were acquired using the Galilei Dual Scheimpflug Placido Disk Topographer under four accommodative demands (0, 1, 3, and 5 dioptres (D)). Custom software analysed lens curvature by calculating eccentricity in both meridians. Participants were analysed as a total group and by age subgroups. Accommodative amplitude and monocular accommodative facility were inversely correlated with age. Both PD and ACD significantly decreased with higher accommodative demands and age. Relative eccentricity decreased under accommodation, indicating increased lens curvature, especially in younger participants. Significant curvature changes were detected in the horizontal meridian only, although no statistically significant differences between meridians were found overall. The vertical meridian showed slightly higher eccentricity values, suggesting that it remained less curved. By enabling detailed, meridionally stratified in vivo assessment of anterior lens curvature, this novel method provides a valuable non-invasive approach for characterizing age-related biomechanical changes during accommodation. The resulting insights enhance our understanding of presbyopia progression, particularly regarding the spatial remodelling of the anterior lens surface. Full article

(This article belongs to the Special Issue Current Progress in Medical Image Segmentation)

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25 pages, 4145 KiB

Open AccessArticle

Advancing Early Blight Detection in Potato Leaves Through ZeroShot Learning

by Muhammad Shoaib Farooq, Ayesha Kamran, Syed Atir Raza, Muhammad Farooq Wasiq, Bilal Hassan and Nitsa J. Herzog

J. Imaging 2025, 11(8), 256; https://doi.org/10.3390/jimaging11080256 - 31 Jul 2025

Potatoes are one of the world’s most widely cultivated crops, but their yield is coming under mounting pressure from early blight, a fungal disease caused by Alternaria solani. Early detection and accurate identification are key to effective disease management and yield protection. [...] Read more.

Potatoes are one of the world’s most widely cultivated crops, but their yield is coming under mounting pressure from early blight, a fungal disease caused by Alternaria solani. Early detection and accurate identification are key to effective disease management and yield protection. This paper introduces a novel deep learning framework called ZeroShot CNN, which integrates convolutional neural networks (CNNs) and ZeroShot Learning (ZSL) for the efficient classification of seen and unseen disease classes. The model utilizes convolutional layers for feature extraction and employs semantic embedding techniques to identify previously untrained classes. Implemented on the Kaggle potato disease dataset, ZeroShot CNN achieved 98.50% accuracy for seen categories and 99.91% accuracy for unseen categories, outperforming conventional methods. The hybrid approach demonstrated superior generalization, providing a scalable, real-time solution for detecting agricultural diseases. The success of this solution validates the potential in harnessing deep learning and ZeroShot inference to transform plant pathology and crop protection practices. Full article

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

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29 pages, 3731 KiB

Open AccessArticle

An Automated Method for Identifying Voids and Severe Loosening in GPR Images

by Ze Chai, Zicheng Wang, Zeshan Xu, Ziyu Feng and Yafeng Zhao

J. Imaging 2025, 11(8), 255; https://doi.org/10.3390/jimaging11080255 - 30 Jul 2025

This paper proposes a novel automatic recognition method for distinguishing voids and severe loosening in road structures based on features of ground-penetrating radar (GPR) B-scan images. By analyzing differences in image texture, the intensity and clarity of top reflection interfaces, and the regularity [...] Read more.

This paper proposes a novel automatic recognition method for distinguishing voids and severe loosening in road structures based on features of ground-penetrating radar (GPR) B-scan images. By analyzing differences in image texture, the intensity and clarity of top reflection interfaces, and the regularity of internal waveforms, a set of discriminative features is constructed. Based on these features, we develop the FKS-GPR dataset, a high-quality, manually annotated GPR dataset collected from real road environments, covering diverse and complex background conditions. Compared to datasets based on simulations, FKS-GPR offers higher practical relevance. An improved ACF-YOLO network is then designed for automatic detection, and the experimental results show that the proposed method achieves superior accuracy and robustness, validating its effectiveness and engineering applicability. Full article

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

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36 pages, 4309 KiB

Open AccessReview

Deep Learning Techniques for Prostate Cancer Analysis and Detection: Survey of the State of the Art

by Olushola Olawuyi and Serestina Viriri

J. Imaging 2025, 11(8), 254; https://doi.org/10.3390/jimaging11080254 - 28 Jul 2025

The human interpretation of medical images, especially for the detection of cancer in the prostate, has traditionally been a time-consuming and challenging process. Manual examination for the detection of prostate cancer is not only time-consuming but also prone to errors, carrying the risk [...] Read more.

The human interpretation of medical images, especially for the detection of cancer in the prostate, has traditionally been a time-consuming and challenging process. Manual examination for the detection of prostate cancer is not only time-consuming but also prone to errors, carrying the risk of an excess biopsy due to the inherent limitations of human visual interpretation. With the technical advancements and rapid growth of computer resources, machine learning (ML) and deep learning (DL) models have been experimentally used for medical image analysis, particularly in lesion detection. However, several state-of-the-art models have shown promising results. There are still challenges when analysing prostate lesion images due to the distinctive and complex nature of medical images. This study offers an elaborate review of the techniques that are used to diagnose prostate cancer using medical images. The goal is to provide a comprehensive and valuable resource that helps researchers develop accurate and autonomous models for effectively detecting prostate cancer. This paper is structured as follows: First, we outline the issues with prostate lesion detection. We then review the methods for analysing prostate lesion images and classification approaches. We then examine convolutional neural network (CNN) architectures and explore their applications in deep learning (DL) for image-based prostate cancer diagnosis. Finally, we provide an overview of prostate cancer datasets and evaluation metrics in deep learning. In conclusion, this review analyses key findings, highlights the challenges in prostate lesion detection, and evaluates the effectiveness and limitations of current deep learning techniques. Full article

(This article belongs to the Section Medical Imaging)

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18 pages, 7213 KiB

Open AccessArticle

DFCNet: Dual-Stage Frequency-Domain Calibration Network for Low-Light Image Enhancement

by Hui Zhou, Jun Li, Yaming Mao, Lu Liu and Yiyang Lu

J. Imaging 2025, 11(8), 253; https://doi.org/10.3390/jimaging11080253 - 28 Jul 2025

Imaging technologies are widely used in surveillance, medical diagnostics, and other critical applications. However, under low-light conditions, captured images often suffer from insufficient brightness, blurred details, and excessive noise, degrading quality and hindering downstream tasks. Conventional low-light image enhancement (LLIE) methods not only [...] Read more.

Imaging technologies are widely used in surveillance, medical diagnostics, and other critical applications. However, under low-light conditions, captured images often suffer from insufficient brightness, blurred details, and excessive noise, degrading quality and hindering downstream tasks. Conventional low-light image enhancement (LLIE) methods not only require annotated data but also often involve heavy models with high computational costs, making them unsuitable for real-time processing. To tackle these challenges, a lightweight and unsupervised LLIE method utilizing a dual-stage frequency-domain calibration network (DFCNet) is proposed. In the first stage, the input image undergoes the preliminary feature modulation (PFM) module to guide the illumination estimation (IE) module in generating a more accurate illumination map. The final enhanced image is obtained by dividing the input by the estimated illumination map. The second stage is used only during training. It applies a frequency-domain residual calibration (FRC) module to the first-stage output, generating a calibration term that is added to the original input to darken dark regions and brighten bright areas. This updated input is then fed back to the PFM and IE modules for parameter optimization. Extensive experiments on benchmark datasets demonstrate that DFCNet achieves superior performance across multiple image quality metrics while delivering visually clearer and more natural results. Full article

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

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47 pages, 18189 KiB

Open AccessArticle

Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures

by Zineb Sordo, Eric Chagnon, Zixi Hu, Jeffrey J. Donatelli, Peter Andeer, Peter S. Nico, Trent Northen and Daniela Ushizima

J. Imaging 2025, 11(8), 252; https://doi.org/10.3390/jimaging11080252 - 26 Jul 2025

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks [...] Read more.

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model’s foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research. Full article

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

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30 pages, 3451 KiB

Open AccessArticle

Integrating Google Maps and Smooth Street View Videos for Route Planning

by Federica Massimi, Antonio Tedeschi, Kalapraveen Bagadi and Francesco Benedetto

J. Imaging 2025, 11(8), 251; https://doi.org/10.3390/jimaging11080251 - 25 Jul 2025

This research addresses the long-standing dependence on printed maps for navigation and highlights the limitations of existing digital services like Google Street View and Google Street View Player in providing comprehensive solutions for route analysis and understanding. The absence of a systematic approach [...] Read more.

This research addresses the long-standing dependence on printed maps for navigation and highlights the limitations of existing digital services like Google Street View and Google Street View Player in providing comprehensive solutions for route analysis and understanding. The absence of a systematic approach to route analysis, issues related to insufficient street view images, and the lack of proper image mapping for desired roads remain unaddressed by current applications, which are predominantly client-based. In response, we propose an innovative automatic system designed to generate videos depicting road routes between two geographic locations. The system calculates and presents the route conventionally, emphasizing the path on a two-dimensional representation, and in a multimedia format. A prototype is developed based on a cloud-based client–server architecture, featuring three core modules: frames acquisition, frames analysis and elaboration, and the persistence of metadata information and computed videos. The tests, encompassing both real-world and synthetic scenarios, have produced promising results, showcasing the efficiency of our system. By providing users with a real and immersive understanding of requested routes, our approach fills a crucial gap in existing navigation solutions. This research contributes to the advancement of route planning technologies, offering a comprehensive and user-friendly system that leverages cloud computing and multimedia visualization for an enhanced navigation experience. Full article

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

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