Journal of Imaging最新文献

{"title":"Pano-GAN: A Deep Generative Model for Panoramic Dental Radiographs.","authors":"Søren Pedersen, Sanyam Jain, Mikkel Chavez, Viktor Ladehoff, Bruna Neves de Freitas, Ruben Pauwels","doi":"10.3390/jimaging11020041","DOIUrl":"10.3390/jimaging11020041","url":null,"abstract":"<p><p>This paper presents the development of a generative adversarial network (GAN) for the generation of synthetic dental panoramic radiographs. While this is an exploratory study, the ultimate aim is to address the scarcity of data in dental research and education. A deep convolutional GAN (DCGAN) with the Wasserstein loss and a gradient penalty (WGAN-GP) was trained on a dataset of 2322 radiographs of varying quality. The focus of this study was on the dentoalveolar part of the radiographs; other structures were cropped out. Significant data cleaning and preprocessing were conducted to standardize the input formats while maintaining anatomical variability. Four candidate models were identified by varying the critic iterations, number of features and the use of denoising prior to training. To assess the quality of the generated images, a clinical expert evaluated a set of generated synthetic radiographs using a ranking system based on visibility and realism, with scores ranging from 1 (very poor) to 5 (excellent). It was found that most generated radiographs showed moderate depictions of dentoalveolar anatomical structures, although they were considerably impaired by artifacts. The mean evaluation scores showed a trade-off between the model trained on non-denoised data, which showed the highest subjective quality for finer structures, such as the <i>mandibular canal</i> and <i>trabecular bone</i>, and one of the models trained on denoised data, which offered better overall image quality, especially in terms of <i>clarity and sharpness</i> and <i>overall realism</i>. These outcomes serve as a foundation for further research into GAN architectures for dental imaging applications.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856485/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of Novel Image Processing Method for Objective Quantification of Intra-Articular Bleeding During Arthroscopic Procedures.","authors":"Olgar Birsel, Umut Zengin, Ilker Eren, Ali Ersen, Beren Semiz, Mehmet Demirhan","doi":"10.3390/jimaging11020040","DOIUrl":"10.3390/jimaging11020040","url":null,"abstract":"<p><p>Visual clarity is crucial for shoulder arthroscopy, directly influencing surgical precision and outcomes. Despite advances in imaging technology, intraoperative bleeding remains a significant obstacle to optimal visibility, with subjective evaluation methods lacking consistency and standardization. This study proposes a novel image processing system to objectively quantify bleeding and assess surgical effectiveness. The system uses color recognition algorithms to calculate a bleeding score based on pixel ratios by incorporating multiple color spaces to enhance accuracy and minimize errors. Moreover, 200 three-second video clips from prior arthroscopic rotator cuff repairs were evaluated by three senior surgeons trained on the system's color metrics and scoring process. Assessments were repeated two weeks later to test intraobserver reliability. The system's scores were compared to the average score given by the surgeons. The average surgeon-assigned score was 5.10 (range: 1-9.66), while the system scored videos from 1 to 9.46, with an average of 5.08. The mean absolute error between system and surgeon scores was 0.56, with a standard deviation of 0.50, achieving agreement ranging from [0.96,0.98] with 96.7% confidence (ICC = 0.967). This system provides a standardized method to evaluate intraoperative bleeding, enabling the precise detection of blood variations and supporting advanced technologies like autonomous arthropumps to enhance arthroscopy and surgical outcomes.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dimensional Accuracy Assessment of Medical Anatomical Models Produced by Hospital-Based Fused Deposition Modeling 3D Printer.","authors":"Kevin Wendo, Catherine Behets, Olivier Barbier, Benoit Herman, Thomas Schubert, Benoit Raucent, Raphael Olszewski","doi":"10.3390/jimaging11020039","DOIUrl":"10.3390/jimaging11020039","url":null,"abstract":"<p><p>As 3D printing technology expands rapidly in medical disciplines, the accuracy evaluation of 3D-printed medical models is required. However, no established guidelines to assess the dimensional error of anatomical models exist. This study aims to evaluate the dimensional accuracy of medical models 3D-printed using a hospital-based Fused Deposition Modeling (FDM) 3D printer. Two dissected cadaveric right hands were marked with Titanium Kirshner wires to identify landmarks on the heads and bases of all metacarpals and proximal and middle phalanges. Both hands were scanned using a Cone Beam Computed Tomography scanner. Image post-processing and segmentation were performed on 3D Slicer software. Hand models were 3D-printed using a professional hospital-based FDM 3D printer. Manual measurements of all landmarks marked on both pairs of cadaveric and 3D-printed hands were taken by two independent observers using a digital caliper. The Mean Absolute Difference (MAD) and Mean Dimensional Error (MDE) were calculated. Our results showed an acceptable level of dimensional accuracy. The overall study's MAD was 0.32 mm (±0.34), and its MDE was 1.03% (±0.83). These values fall within the recommended range of errors. A high level of dimensional accuracy of the 3D-printed anatomical models was achieved, suggesting their reliability and suitability for medical applications.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856956/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semantic-Guided Transformer Network for Crop Classification in Hyperspectral Images.","authors":"Weiqiang Pi, Tao Zhang, Rongyang Wang, Guowei Ma, Yong Wang, Jianmin Du","doi":"10.3390/jimaging11020037","DOIUrl":"10.3390/jimaging11020037","url":null,"abstract":"<p><p>The hyperspectral remote sensing images of agricultural crops contain rich spectral information, which can provide important details about crop growth status, diseases, and pests. However, existing crop classification methods face several key limitations when processing hyperspectral remote sensing images, primarily in the following aspects. First, the complex background in the images. Various elements in the background may have similar spectral characteristics to the crops, and this spectral similarity makes the classification model susceptible to background interference, thus reducing classification accuracy. Second, the differences in crop scales increase the difficulty of feature extraction. In different image regions, the scale of crops can vary significantly, and traditional classification methods often struggle to effectively capture this information. Additionally, due to the limitations of spectral information, especially under multi-scale variation backgrounds, the extraction of crop information becomes even more challenging, leading to instability in the classification results. To address these issues, a semantic-guided transformer network (SGTN) is proposed, which aims to effectively overcome the limitations of these deep learning methods and improve crop classification accuracy and robustness. First, a multi-scale spatial-spectral information extraction (MSIE) module is designed that effectively handle the variations of crops at different scales in the image, thereby extracting richer and more accurate features, and reducing the impact of scale changes. Second, a semantic-guided attention (SGA) module is proposed, which enhances the model's sensitivity to crop semantic information, further reducing background interference and improving the accuracy of crop area recognition. By combining the MSIE and SGA modules, the SGTN can focus on the semantic features of crops at multiple scales, thus generating more accurate classification results. Finally, a two-stage feature extraction structure is employed to further optimize the extraction of crop semantic features and enhance classification accuracy. The results show that on the Indian Pines, Pavia University, and Salinas benchmark datasets, the overall accuracies of the proposed model are 98.24%, 98.34%, and 97.89%, respectively. Compared with other methods, the model achieves better classification accuracy and generalization performance. In the future, the SGTN is expected to be applied to more agricultural remote sensing tasks, such as crop disease detection and yield prediction, providing more reliable technical support for precision agriculture and agricultural monitoring.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-Based Approaches for Breast Density Estimation from Mammograms: A Comprehensive Review.","authors":"Khaldoon Alhusari, Salam Dhou","doi":"10.3390/jimaging11020038","DOIUrl":"10.3390/jimaging11020038","url":null,"abstract":"<p><p>Breast cancer, as of 2022, is the most prevalent type of cancer in women. Breast density-a measure of the non-fatty tissue in the breast-is a strong risk factor for breast cancer that can be estimated from mammograms. The importance of studying breast density is twofold. First, high breast density can be a factor in lowering mammogram sensitivity, as dense tissue can mask tumors. Second, higher breast density is associated with an increased risk of breast cancer, making accurate assessments vital. This paper presents a comprehensive review of the mammographic density estimation literature, with an emphasis on machine-learning-based approaches. The approaches reviewed can be classified as visual, software-, machine learning-, and segmentation-based. Machine learning methods can be further broken down into two categories: traditional machine learning and deep learning approaches. The most commonly utilized models are support vector machines (SVMs) and convolutional neural networks (CNNs), with classification accuracies ranging from 76.70% to 98.75%. Major limitations of the current works include subjectivity and cost-inefficiency. Future work can focus on addressing these limitations, potentially through the use of unsupervised segmentation and state-of-the-art deep learning models such as transformers. By addressing the current limitations, future research can pave the way for more reliable breast density estimation methods, ultimately improving early detection and diagnosis.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856162/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"iForal: Automated Handwritten Text Transcription for Historical Medieval Manuscripts.","authors":"Alexandre Matos, Pedro Almeida, Paulo L Correia, Osvaldo Pacheco","doi":"10.3390/jimaging11020036","DOIUrl":"10.3390/jimaging11020036","url":null,"abstract":"<p><p>The transcription of historical manuscripts aims at making our cultural heritage more accessible to experts and also to the larger public, but it is a challenging and time-intensive task. This paper contributes an automated solution for text layout recognition, segmentation, and recognition to speed up the transcription process of historical manuscripts. The focus is on transcribing Portuguese municipal documents from the Middle Ages in the context of the iForal project, including the contribution of an annotated dataset containing Portuguese medieval documents, notably a corpus of 67 Portuguese royal charter data. The proposed system can accurately identify document layouts, isolate the text, segment, and transcribe it. Results for the layout recognition model achieved 0.98 mAP@0.50 and 0.98 precision, while the text segmentation model achieved 0.91 mAP@0.50, detecting 95% of the lines. The text recognition model achieved 8.1% character error rate (CER) and 25.5% word error rate (WER) on the test set. These results can then be validated by palaeographers with less effort, contributing to achieving high-quality transcriptions faster. Moreover, the automatic models developed can be utilized as a basis for the creation of models that perform well for other historical handwriting styles, notably using transfer learning techniques. The contributed dataset has been made available on the HTR United catalogue, which includes training datasets to be used for automatic transcription or segmentation models. The models developed can be used, for instance, on the eSriptorium platform, which is used by a vast community of experts.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of an Optimal Convolutional Neural Network Architecture for MRI Brain Tumor Classification by Exploiting Particle Swarm Optimization.","authors":"Sofia El Amoury, Youssef Smili, Youssef Fakhri","doi":"10.3390/jimaging11020031","DOIUrl":"10.3390/jimaging11020031","url":null,"abstract":"<p><p>The classification of brain tumors using MRI scans is critical for accurate diagnosis and effective treatment planning, though it poses significant challenges due to the complex and varied characteristics of tumors, including irregular shapes, diverse sizes, and subtle textural differences. Traditional convolutional neural network (CNN) models, whether handcrafted or pretrained, frequently fall short in capturing these intricate details comprehensively. To address this complexity, an automated approach employing Particle Swarm Optimization (PSO) has been applied to create a CNN architecture specifically adapted for MRI-based brain tumor classification. PSO systematically searches for an optimal configuration of architectural parameters-such as the types and numbers of layers, filter quantities and sizes, and neuron numbers in fully connected layers-with the objective of enhancing classification accuracy. This performance-driven method avoids the inefficiencies of manual design and iterative trial and error. Experimental results indicate that the PSO-optimized CNN achieves a classification accuracy of 99.19%, demonstrating significant potential for improving diagnostic precision in complex medical imaging applications and underscoring the value of automated architecture search in advancing critical healthcare technology.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11857081/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the Potential of Latent Space for the Classification of Paint Defects.","authors":"Doaa Almhaithawi, Alessandro Bellini, Georgios C Chasparis, Tania Cerquitelli","doi":"10.3390/jimaging11020033","DOIUrl":"10.3390/jimaging11020033","url":null,"abstract":"<p><p>Defect detection methods have greatly assisted human operators in various fields, from textiles to surfaces and mechanical components, by facilitating decision-making processes and reducing visual fatigue. This area of research is widely recognized as a cross-industry concern, particularly in the manufacturing sector. Nevertheless, each specific application brings unique challenges that require tailored solutions. This paper presents a novel framework for leveraging latent space representations in defect detection tasks, focusing on improving explainability while maintaining accuracy. This work delves into how latent spaces can be utilized by integrating unsupervised and supervised analyses. We propose a hybrid methodology that not only identifies known defects but also provides a mechanism for detecting anomalies and dynamically adapting to new defect types. This dual approach supports human operators, reducing manual workload and enhancing interpretability.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856999/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revealing Gender Bias from Prompt to Image in Stable Diffusion.","authors":"Yankun Wu, Yuta Nakashima, Noa Garcia","doi":"10.3390/jimaging11020035","DOIUrl":"10.3390/jimaging11020035","url":null,"abstract":"<p><p>Social biases in generative models have gained increasing attention. This paper proposes an automatic evaluation protocol for text-to-image generation, examining how gender bias originates and perpetuates in the generation process of Stable Diffusion. Using triplet prompts that vary by gender indicators, we trace presentations at several stages of the generation process and explore dependencies between prompts and images. Our findings reveal the bias persists throughout all internal stages of the generating process and manifests in the entire images. For instance, differences in object presence, such as different instruments and outfit preferences, are observed across genders and extend to overall image layouts. Moreover, our experiments demonstrate that neutral prompts tend to produce images more closely aligned with those from masculine prompts than with their female counterparts. We also investigate prompt-image dependencies to further understand how bias is embedded in the generated content. Finally, we offer recommendations for developers and users to mitigate this effect in text-to-image generation.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GCNet: A Deep Learning Framework for Enhanced Grape Cluster Segmentation and Yield Estimation Incorporating Occluded Grape Detection with a Correction Factor for Indoor Experimentation.","authors":"Rubi Quiñones, Syeda Mariah Banu, Eren Gultepe","doi":"10.3390/jimaging11020034","DOIUrl":"10.3390/jimaging11020034","url":null,"abstract":"<p><p>Object segmentation algorithms have heavily relied on deep learning techniques to estimate the count of grapes which is a strong indicator for the yield success of grapes. The issue with using object segmentation algorithms for grape analytics is that they are limited to counting only the visible grapes, thus omitting hidden grapes, which affect the true estimate of grape yield. Many grapes are occluded because of either the compactness of the grape bunch cluster or due to canopy interference. This introduces the need for models to be able to estimate the unseen berries to give a more accurate estimate of the grape yield by improving grape cluster segmentation. We propose the Grape Counting Network (GCNet), a novel framework for grape cluster segmentation, integrating deep learning techniques with correction factors to address challenges in indoor yield estimation. GCNet incorporates occlusion adjustments, enhancing segmentation accuracy even under conditions of foliage and cluster compactness, and setting new standards in agricultural indoor imaging analysis. This approach improves yield estimation accuracy, achieving a R² of 0.96 and reducing mean absolute error (MAE) by 10% compared to previous methods. We also propose a new dataset called GrapeSet which contains visible imagery of grape clusters imaged indoors, along with their ground truth mask, total grape count, and weight in grams. The proposed framework aims to encourage future research in determining which features of grapes can be leveraged to estimate the correct grape yield count, equip grape harvesters with the knowledge of early yield estimation, and produce accurate results in object segmentation algorithms for grape analytics.</p>","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11856392/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}