Journal of Imaging

16 pages, 1139 KiB

Open AccessArticle

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

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

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

Abstract

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

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

► Show Figures

Figure 1

21 pages, 827 KiB

Open AccessReview

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

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

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

Abstract

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

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

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

► Show Figures

Figure 1

20 pages, 17085 KiB

Open AccessArticle

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

by Xiwang Zhou, Yongming Yang and Dingfei Yan

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

Abstract

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

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

► Show Figures

Figure 1

21 pages, 9110 KiB

Open AccessArticle

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

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

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

Abstract

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

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

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

► Show Figures

Figure 1

19 pages, 6412 KiB

Open AccessArticle

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

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

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

Viewed by 45

Abstract

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

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

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

► Show Figures

Figure 1

15 pages, 2054 KiB

Open AccessArticle

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

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

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

Viewed by 121

Abstract

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

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

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

► Show Figures

Figure 1

29 pages, 63247 KiB

Open AccessArticle

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

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

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

Viewed by 101

Abstract

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

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

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

► Show Figures

Figure 1

17 pages, 2046 KiB

Open AccessArticle

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

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

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

Viewed by 133

Abstract

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

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

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

► Show Figures

Figure 1

22 pages, 7640 KiB

Open AccessArticle

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

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

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

Viewed by 307

Abstract

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

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

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

► Show Figures

Figure 1

30 pages, 5602 KiB

Open AccessReview

A Comprehensive Review on Document Image Binarization

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

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

Viewed by 260

Abstract

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

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

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

► Show Figures

Figure 1

24 pages, 4056 KiB

Open AccessArticle

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

by Jan Sawicki

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

Viewed by 268

Abstract

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

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

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

► Show Figures

Figure 1

11 pages, 1644 KiB

Open AccessCommunication

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

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

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

Viewed by 167

Abstract

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

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

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

► Show Figures

Figure 1

13 pages, 406 KiB

Open AccessArticle

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

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

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

Viewed by 537

Abstract

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

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

(This article belongs to the Section Medical Imaging)

► Show Figures

Figure 1

26 pages, 13259 KiB

Open AccessArticle

Method for Indoor Seismic Intensity Assessment Based on Image Processing Techniques

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

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

Viewed by 168

Abstract

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

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

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

► Show Figures

Figure 1

23 pages, 37586 KiB

Open AccessArticle

Revisiting Wölfflin in the Age of AI: A Study of Classical and Baroque Composition in Generative Models

by Adrien Deliege, Maria Giulia Dondero and Enzo D’Armenio

J. Imaging 2025, 11(5), 128; https://doi.org/10.3390/jimaging11050128 - 22 Apr 2025

Viewed by 130

Abstract

This study explores how contemporary text-to-image models interpret and generate Classical and Baroque styles under Wölfflin’s framework—two categories that are atemporal and transversal across media. Our goal is to see whether generative AI can replicate the nuanced stylistic cues that art historians attribute [...] Read more.

This study explores how contemporary text-to-image models interpret and generate Classical and Baroque styles under Wölfflin’s framework—two categories that are atemporal and transversal across media. Our goal is to see whether generative AI can replicate the nuanced stylistic cues that art historians attribute to them. We prompted two popular models (DALL•E and Midjourney) using explicit style labels (e.g., “baroque” and “classical”) as well as more implicit cues (e.g., “dynamic”, “static”, or reworked Wölfflin descriptors). We then collected expert ratings and conducted broader qualitative reviews to assess how each output aligned with Wölfflin’s characteristics. Our findings suggest that the term “baroque” usually evokes features recognizable in typically historical Baroque artworks, while “classical” often yields less distinct results, particularly when a specified genre (portrait, still life) imposes a centered, closed-form composition. Removing explicit style labels may produce highly abstract images, revealing that Wölfflin’s descriptors alone may be insufficient to convey Classical or Baroque styles efficiently. Interestingly, the term “dynamic” gives rather chaotic images, yet this chaos is somehow ordered, centered, and has an almost Classical feel. Altogether, these observations highlight the complexity of bridging canonical stylistic frameworks and contemporary AI training biases, underscoring the need to update or refine Wölfflin’s atemporal categories to accommodate how generative models—and modern visual culture—reinterpret Classical and Baroque. Full article

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

► Show Figures

Figure 1

10 pages, 928 KiB

Open AccessArticle

Reliability of Quadriceps and Hamstring Soft Tissue Measures Using Dual-Energy X-Ray Absorptiometry (DXA) Scans

by Trey R. Naylor, Mariana V. Jacobs, Cameron J. Elder, Michael A. Samaan and Jody L. Clasey

J. Imaging 2025, 11(5), 127; https://doi.org/10.3390/jimaging11050127 - 22 Apr 2025

Viewed by 169

Abstract

Purpose: The purpose of this study was to determine the intra- and interrater reliability of quadriceps and hamstring soft tissue measures using DXA scans. Methods: A total of 44 subjects (23 males) participated in this study. The first total body DXA scan was [...] Read more.

Purpose: The purpose of this study was to determine the intra- and interrater reliability of quadriceps and hamstring soft tissue measures using DXA scans. Methods: A total of 44 subjects (23 males) participated in this study. The first total body DXA scan was performed in the standard anterior/posterior scanning position, followed by two additional total body scans while the subjects were lying on their left and right sides with the leg of interest extended and the contralateral leg bent. Unique regions of interest were created for analyses of mineral-free lean masses (MFL) using custom analysis software with manual tracing (by two investigators) of the quadriceps (QUADS) and hamstrings (HAMS) of the right and left thighs. Between–within repeated measure analysis of variance (ANOVA) was used to determine if there were significant differences among the MFL measures, while intraclass correlation coefficients (ICC) and coefficients of variation (CV) were used to assess the intra- and interrater reliability. Results: Between-group analyses revealed that Investigator 2 had small yet significantly higher mean differences for right QUADS (2346.6 ± 602.4 g vs. 2327.4 ± 587.9 g), left QUADS (2337.3 ± 581.9 g vs. 2312.8 ± 581.2 g), right HAMS (2655.9 ± 626.3 g vs. 2543.0 ± 593.5 g), and left HAMS (2686.1 ± 628.1 g vs. 2562.8 ± 596.5 g) when compared to Investigator 1. Intraclass correlation coefficients between (≥0.984) and within (≥0.992) raters were high for all MFL measures, with low variation across all MFL measures (≤1.62%). Conclusions: Despite having significant group mean differences, our results revealed strong and significant reliability, and we recommend that a single investigator analyze the scans twice and that the mean of the two measures be used for final reporting within a given study. Full article

(This article belongs to the Section Medical Imaging)

► Show Figures

Figure 1

Journal Menu

Journal Browser

J. Imaging, Volume 11, Issue 5 (May 2025) – 16 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI