Previous Issue
Volume 11, July
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.7
3.3
Journals
J. Imaging
Volume 11
Issue 8
share announcement

J. Imaging, Volume 11, Issue 8 (August 2025) – 23 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
24 pages, 948 KiB  
Review
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
Abstract
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)
►▼ Show Figures

Figure 1

28 pages, 12443 KiB  
Article
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
Abstract
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  
Article
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
Abstract
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)
►▼ Show Figures

Figure 1

14 pages, 2224 KiB  
Article
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
Viewed by 177
Abstract
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 (Scoreadj). 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 Scoreadj, 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)
►▼ Show Figures

Figure 1

19 pages, 12806 KiB  
Article
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
Viewed by 151
Abstract
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)
►▼ Show Figures

Figure 1

21 pages, 7306 KiB  
Article
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
Viewed by 148
Abstract
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 SlicerTM 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)
►▼ Show Figures

Graphical abstract

23 pages, 5644 KiB  
Article
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
Viewed by 199
Abstract
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)
►▼ Show Figures

Figure 1

29 pages, 3842 KiB  
Article
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
Viewed by 143
Abstract
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)
►▼ Show Figures

Figure 1

11 pages, 1947 KiB  
Article
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
Viewed by 274
Abstract
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)
►▼ Show Figures

Figure 1

19 pages, 7531 KiB  
Article
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
Viewed by 281
Abstract
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)
►▼ Show Figures

Figure 1

23 pages, 22135 KiB  
Article
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
Viewed by 280
Abstract
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)
►▼ Show Figures

Figure 1

28 pages, 21813 KiB  
Article
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
Viewed by 318
Abstract
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)
►▼ Show Figures

Figure 1

20 pages, 4292 KiB  
Article
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
Viewed by 220
Abstract
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)
►▼ Show Figures

Figure 1

25 pages, 4145 KiB  
Article
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
Viewed by 305
Abstract
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)
►▼ Show Figures

Figure 1

29 pages, 3731 KiB  
Article
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
Viewed by 328
Abstract
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)
►▼ Show Figures

Figure 1

36 pages, 4309 KiB  
Review
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
Viewed by 539
Abstract
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)
►▼ Show Figures

Figure 1

18 pages, 7213 KiB  
Article
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
Viewed by 291
Abstract
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)
►▼ Show Figures

Figure 1

47 pages, 18189 KiB  
Article
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
Viewed by 803
Abstract
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)
►▼ Show Figures

Graphical abstract

30 pages, 3451 KiB  
Article
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
Viewed by 404
Abstract
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)
►▼ Show Figures

Figure 1

19 pages, 1282 KiB  
Article
The Role of Radiomic Analysis and Different Machine Learning Models in Prostate Cancer Diagnosis
by Eleni Bekou, Ioannis Seimenis, Athanasios Tsochatzis, Karafyllia Tziagkana, Nikolaos Kelekis, Savas Deftereos, Nikolaos Courcoutsakis, Michael I. Koukourakis and Efstratios Karavasilis
J. Imaging 2025, 11(8), 250; https://doi.org/10.3390/jimaging11080250 - 23 Jul 2025
Viewed by 348
Abstract
Prostate cancer (PCa) is the most common malignancy in men. Precise grading is crucial for the effective treatment approaches of PCa. Machine learning (ML) applied to biparametric Magnetic Resonance Imaging (bpMRI) radiomics holds promise for improving PCa diagnosis and prognosis. This study investigated [...] Read more.
Prostate cancer (PCa) is the most common malignancy in men. Precise grading is crucial for the effective treatment approaches of PCa. Machine learning (ML) applied to biparametric Magnetic Resonance Imaging (bpMRI) radiomics holds promise for improving PCa diagnosis and prognosis. This study investigated the efficiency of seven ML models to diagnose the different PCa grades, changing the input variables. Our studied sample comprised 214 men who underwent bpMRI in different imaging centers. Seven ML algorithms were compared using radiomic features extracted from T2-weighted (T2W) and diffusion-weighted (DWI) MRI, with and without the inclusion of Prostate-Specific Antigen (PSA) values. The performance of the models was evaluated using the receiver operating characteristic curve analysis. The models’ performance was strongly dependent on the input parameters. Radiomic features derived from T2WI and DWI, whether used independently or in combination, demonstrated limited clinical utility, with AUC values ranging from 0.703 to 0.807. However, incorporating the PSA index significantly improved the models’ efficiency, regardless of lesion location or degree of malignancy, resulting in AUC values ranging from 0.784 to 1.00. There is evidence that ML methods, in combination with radiomic analysis, can contribute to solving differential diagnostic problems of prostate cancers. Also, optimization of the analysis method is critical, according to the results of our study. Full article
(This article belongs to the Section Medical Imaging)
►▼ Show Figures

Figure 1

26 pages, 11237 KiB  
Article
Reclassification Scheme for Image Analysis in GRASS GIS Using Gradient Boosting Algorithm: A Case of Djibouti, East Africa
by Polina Lemenkova
J. Imaging 2025, 11(8), 249; https://doi.org/10.3390/jimaging11080249 - 23 Jul 2025
Viewed by 522
Abstract
Image analysis is a valuable approach in a wide array of environmental applications. Mapping land cover categories depicted from satellite images enables the monitoring of landscape dynamics. Such a technique plays a key role for land management and predictive ecosystem modelling. Satellite-based mapping [...] Read more.
Image analysis is a valuable approach in a wide array of environmental applications. Mapping land cover categories depicted from satellite images enables the monitoring of landscape dynamics. Such a technique plays a key role for land management and predictive ecosystem modelling. Satellite-based mapping of environmental dynamics enables us to define factors that trigger these processes and are crucial for our understanding of Earth system processes. In this study, a reclassification scheme of image analysis was developed for mapping the adjusted categorisation of land cover types using multispectral remote sensing datasets and Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The data included four Landsat 8–9 satellite images on 2015, 2019, 2021 and 2023. The sequence of time series was used to determine land cover dynamics. The classification scheme consisting of 17 initial land cover classes was employed by logical workflow to extract 10 key land cover types of the coastal areas of Bab-el-Mandeb Strait, southern Red Sea. Special attention is placed to identify changes in the land categories regarding the thermal saline lake, Lake Assal, with fluctuating salinity and water levels. The methodology included the use of machine learning (ML) image analysis GRASS GIS modules ‘r.reclass’ for the reclassification of a raster map based on category values. Other modules included ‘r.random’, ‘r.learn.train’ and ‘r.learn.predict’ for gradient boosting ML classifier and ‘i.cluster’ and ‘i.maxlik’ for clustering and maximum-likelihood discriminant analysis. To reveal changes in the land cover categories around the Lake of Assal, this study uses ML and reclassification methods for image analysis. Auxiliary modules included ‘i.group’, ‘r.import’ and other GRASS GIS scripting techniques applied to Landsat image processing and for the identification of land cover variables. The results of image processing demonstrated annual fluctuations in the landscapes around the saline lake and changes in semi-arid and desert land cover types over Djibouti. The increase in the extent of semi-desert areas and the decrease in natural vegetation proved the processes of desertification of the arid environment in Djibouti caused by climate effects. The developed land cover maps provided information for assessing spatial–temporal changes in Djibouti. The proposed ML-based methodology using GRASS GIS can be employed for integrating techniques of image analysis for land management in other arid regions of Africa. Full article
(This article belongs to the Special Issue Self-Supervised Learning for Image Processing and Analysis)
►▼ Show Figures

Figure 1

18 pages, 33092 KiB  
Article
Yarn Color Measurement Method Based on Digital Photography
by Jinxing Liang, Guanghao Wu, Ke Yang, Jiangxiaotian Ma, Jihao Wang, Hang Luo, Xinrong Hu and Yong Liu
J. Imaging 2025, 11(8), 248; https://doi.org/10.3390/jimaging11080248 - 22 Jul 2025
Viewed by 280
Abstract
To overcome the complexity of yarn color measurement using spectrophotometry with yarn winding techniques and to enhance consistency with human visual perception, a yarn color measurement method based on digital photography is proposed. This study employs a photographic colorimetry system to capture digital [...] Read more.
To overcome the complexity of yarn color measurement using spectrophotometry with yarn winding techniques and to enhance consistency with human visual perception, a yarn color measurement method based on digital photography is proposed. This study employs a photographic colorimetry system to capture digital images of single yarns. The yarn and background are segmented using the K-means clustering algorithm, and the centerline of the yarn is extracted using a skeletonization algorithm. Spectral reconstruction and colorimetric principles are then applied to calculate the color values of pixels along the centerline. Considering the nonlinear characteristics of human brightness perception, the final yarn color is obtained through a nonlinear texture-adaptive weighted computation. The method is validated through psychophysical experiments using six yarns of different colors and compared with spectrophotometry and five other photographic measurement methods. Results indicate that among the seven yarn color measurement methods, including spectrophotometry, the proposed method—based on centerline extraction and nonlinear texture-adaptive weighting—yields results that more closely align with actual visual perception. Furthermore, among the six photographic measurement methods, the proposed method produces most similar to those obtained using spectrophotometry. This study demonstrates the inconsistency between spectrophotometric measurements and human visual perception of yarn color and provides methodological support for developing visually consistent color measurement methods for textured textiles. Full article
(This article belongs to the Section Color, Multi-spectral, and Hyperspectral Imaging)
►▼ Show Figures

Figure 1

24 pages, 8015 KiB  
Article
Innovative Multi-View Strategies for AI-Assisted Breast Cancer Detection in Mammography
by Beibit Abdikenov, Tomiris Zhaksylyk, Aruzhan Imasheva, Yerzhan Orazayev and Temirlan Karibekov
J. Imaging 2025, 11(8), 247; https://doi.org/10.3390/jimaging11080247 - 22 Jul 2025
Viewed by 562
Abstract
Mammography is the main method for early detection of breast cancer, which is still a major global health concern. However, inter-reader variability and the inherent difficulty of interpreting subtle radiographic features frequently limit the accuracy of diagnosis. A thorough assessment of deep convolutional [...] Read more.
Mammography is the main method for early detection of breast cancer, which is still a major global health concern. However, inter-reader variability and the inherent difficulty of interpreting subtle radiographic features frequently limit the accuracy of diagnosis. A thorough assessment of deep convolutional neural networks (CNNs) for automated mammogram classification is presented in this work, along with the introduction of two innovative multi-view integration techniques: Dual-Branch Ensemble (DBE) and Merged Dual-View (MDV). By setting aside two datasets for out-of-sample testing, we evaluate the generalizability of the model using six different mammography datasets that represent various populations and imaging systems. We compare a number of cutting-edge architectures on both individual and combined datasets, including ResNet, DenseNet, EfficientNet, MobileNet, Vision Transformers, and VGG19. Both MDV and DBE strategies improve classification performance, according to experimental results. VGG19 and DenseNet both obtained high ROC AUC scores of 0.9051 and 0.7960 under the MDV approach. DenseNet demonstrated strong performance in the DBE setting, achieving a ROC AUC of 0.8033, while ResNet50 recorded a ROC AUC of 0.8042. These enhancements demonstrate how beneficial multi-view fusion is for boosting model robustness. The impact of domain shift is further highlighted by generalization tests, which emphasize the need for diverse datasets in training. These results offer practical advice for improving CNN architectures and integration tactics, which will aid in the creation of trustworthy, broadly applicable AI-assisted breast cancer screening tools. Full article
(This article belongs to the Section Medical Imaging)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-23
Previous Issue
Back to TopTop