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J. Imaging, Volume 10, Issue 11 (November 2024) – 34 articles

Cover Story (view full-size image): Radiomics aids clinical decision making, but users need to switch between different software to complete the whole process. To address this issue, matRadiomics integrates the entire radiomics workflow into a single platform. This study extends its use to preclinical applications, being validated through a zebrafish case study on early malformation differentiation. Incorporating Pyradiomics for feature extraction, it employs machine learning models with k-fold cross-validation. The results highlight the challenges in feature extraction from high-resolution preclinical images and propose optimized settings to reduce computation time without compromising data integrity. SVM achieved the best performance, demonstrating the potential of matRadiomics to advance preclinical and translational research. View this paper
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18 pages, 12381 KiB  
Article
AQSA—Algorithm for Automatic Quantification of Spheres Derived from Cancer Cells in Microfluidic Devices
by Ana Belén Peñaherrera-Pazmiño, Ramiro Fernando Isa-Jara, Elsa Hincapié-Arias, Silvia Gómez, Denise Belgorosky, Eduardo Imanol Agüero, Matías Tellado, Ana María Eiján, Betiana Lerner and Maximiliano Pérez
J. Imaging 2024, 10(11), 295; https://doi.org/10.3390/jimaging10110295 - 20 Nov 2024
Viewed by 959
Abstract
Sphere formation assay is an accepted cancer stem cell (CSC) enrichment method. CSCs play a crucial role in chemoresistance and cancer recurrence. Therefore, CSC growth is studied in plates and microdevices to develop prediction chemotherapy assays in cancer. As counting spheres cultured in [...] Read more.
Sphere formation assay is an accepted cancer stem cell (CSC) enrichment method. CSCs play a crucial role in chemoresistance and cancer recurrence. Therefore, CSC growth is studied in plates and microdevices to develop prediction chemotherapy assays in cancer. As counting spheres cultured in devices is laborious, time-consuming, and operator-dependent, a computational program called the Automatic Quantification of Spheres Algorithm (ASQA) that detects, identifies, counts, and measures spheres automatically was developed. The algorithm and manual counts were compared, and there was no statistically significant difference (p = 0.167). The performance of the AQSA is better when the input image has a uniform background, whereas, with a nonuniform background, artifacts can be interpreted as spheres according to image characteristics. The areas of spheres derived from LN229 cells and CSCs from primary cultures were measured. For images with one sphere, area measurements obtained with the AQSA and SpheroidJ were compared, and there was no statistically significant difference between them (p = 0.173). Notably, the AQSA detects more than one sphere, compared to other approaches available in the literature, and computes the sphere area automatically, which enables the observation of treatment response in the sphere derived from the human glioblastoma LN229 cell line. In addition, the algorithm identifies spheres with numbers to identify each one over time. The AQSA analyzes many images in 0.3 s per image with a low computational cost, enabling laboratories from developing countries to perform sphere counts and area measurements without needing a powerful computer. Consequently, it can be a useful tool for automated CSC quantification from cancer cell lines, and it can be adjusted to quantify CSCs from primary culture cells. CSC-derived sphere detection is highly relevant as it avoids expensive treatments and unnecessary toxicity. Full article
(This article belongs to the Special Issue Advancements in Imaging Techniques for Detection of Cancer)
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17 pages, 2661 KiB  
Article
Spatially Localized Visual Perception Estimation by Means of Prosthetic Vision Simulation
by Diego Luján Villarreal and Wolfgang Krautschneider
J. Imaging 2024, 10(11), 294; https://doi.org/10.3390/jimaging10110294 - 18 Nov 2024
Viewed by 1138
Abstract
Retinal prosthetic devices aim to repair some vision in visually impaired patients by electrically stimulating neural cells in the visual system. Although there have been several notable advancements in the creation of electrically stimulated small dot-like perceptions, a deeper comprehension of the physical [...] Read more.
Retinal prosthetic devices aim to repair some vision in visually impaired patients by electrically stimulating neural cells in the visual system. Although there have been several notable advancements in the creation of electrically stimulated small dot-like perceptions, a deeper comprehension of the physical properties of phosphenes is still necessary. This study analyzes the influence of two independent electrode array topologies to achieve single-localized stimulation while the retina is electrically stimulated: a two-dimensional (2D) hexagon-shaped array reported in clinical studies and a patented three-dimensional (3D) linear electrode carrier. For both, cell stimulation is verified in COMSOL Multiphysics by developing a lifelike 3D computational model that includes the relevant retinal interface elements and dynamics of the voltage-gated ionic channels. The evoked percepts previously described in clinical studies using the 2D array are strongly associated with our simulation-based findings, allowing for the development of analytical models of the evoked percepts. Moreover, our findings identify differences between visual sensations induced by the arrays. The 2D array showed drawbacks during stimulation; similarly, the state-of-the-art 2D visual prostheses provide only dot-like visual sensations in close proximity to the electrode. The 3D design could offer a technique for improving cell selectivity because it requires low-intensity threshold activation which results in volumes of stimulation similar to the volume surrounded by a solitary RGC. Our research establishes a proof-of-concept technique for determining the utility of the 3D electrode array for selectively activating individual RGCs at the highest density via small-sized electrodes while maintaining electrochemical safety. Full article
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4 pages, 173 KiB  
Editorial
Editorial on the Special Issue “Fluorescence Imaging and Analysis of Cellular Systems”
by Ashutosh Sharma
J. Imaging 2024, 10(11), 293; https://doi.org/10.3390/jimaging10110293 - 18 Nov 2024
Viewed by 658
Abstract
Fluorescence imaging has indeed become a cornerstone in modern cell biology due to its ability to offer highly sensitive, specific, and real-time visualization of cellular structures and dynamic processes [...] Full article
(This article belongs to the Special Issue Fluorescence Imaging and Analysis of Cellular System)
10 pages, 1746 KiB  
Technical Note
MOTH: Memory-Efficient On-the-Fly Tiling of Histological Image Annotations Using QuPath
by Thomas Kauer, Jannik Sehring, Kai Schmid, Marek Bartkuhn, Benedikt Wiebach, Slaven Crnkovic, Grazyna Kwapiszewska, Till Acker and Daniel Amsel
J. Imaging 2024, 10(11), 292; https://doi.org/10.3390/jimaging10110292 - 15 Nov 2024
Viewed by 637
Abstract
The emerging usage of digitalized histopathological images is leading to a novel possibility for data analysis. With the help of artificial intelligence algorithms, it is now possible to detect certain structures and morphological features on whole slide images automatically. This enables algorithms to [...] Read more.
The emerging usage of digitalized histopathological images is leading to a novel possibility for data analysis. With the help of artificial intelligence algorithms, it is now possible to detect certain structures and morphological features on whole slide images automatically. This enables algorithms to count, measure, or evaluate those areas when trained properly. To achieve suitable training, datasets must be annotated and curated by users in programs like QuPath. The extraction of this data for artificial intelligence algorithms is still rather tedious and needs to be saved on a local hard drive. We developed a toolkit for integration into existing pipelines and tools, like U-net, for the on-the-fly extraction of annotation tiles from existing QuPath projects. The tiles can be directly used as input for artificial intelligence algorithms, and the results are directly transferred back to QuPath for visual inspection. With the toolkit, we created a convenient way to incorporate QuPath into existing AI workflows. Full article
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12 pages, 3874 KiB  
Article
Anatomical Characteristics of Cervicomedullary Compression on MRI Scans in Children with Achondroplasia
by Isabella Trautwein, Daniel Behme, Philip Kunkel, Jasper Gerdes and Klaus Mohnike
J. Imaging 2024, 10(11), 291; https://doi.org/10.3390/jimaging10110291 - 14 Nov 2024
Viewed by 713
Abstract
This retrospective study assessed anatomical characteristics of cervicomedullary compression in children with achondroplasia. Twelve anatomical parameters were analyzed (foramen magnum diameter and area; myelon area; clivus length; tentorium and occipital angles; brainstem volume outside the posterior fossa; and posterior fossa, cerebellum, supratentorial ventricular [...] Read more.
This retrospective study assessed anatomical characteristics of cervicomedullary compression in children with achondroplasia. Twelve anatomical parameters were analyzed (foramen magnum diameter and area; myelon area; clivus length; tentorium and occipital angles; brainstem volume outside the posterior fossa; and posterior fossa, cerebellum, supratentorial ventricular system, intracranial cerebrospinal fluid, and fourth ventricle volumes) from sagittal and transversal T1- and T2-weighted magnetic resonance imaging (MRI) scans from 37 children with achondroplasia aged ≤ 4 years (median [range] 0.8 [0.1–3.6] years) and compared with scans from 37 children without achondroplasia (median age 1.5 [0–3.9] years). Mann–Whitney U testing was used for between-group comparisons. Foramen magnum diameter and area were significantly smaller in children with achondroplasia compared with the reference group (mean 10.0 vs. 16.1 mm [p < 0.001] and 109.0 vs. 160.8 mm2 [p = 0.005], respectively). The tentorial angle was also steeper in children with achondroplasia (mean 47.6 vs. 38.1 degrees; p < 0.001), while the clivus was significantly shorter (mean 23.5 vs. 30.3 mm; p < 0.001). Significant differences were also observed in myelon area, occipital angle, fourth ventricle, intracranial cerebrospinal fluid and supratentorial ventricular volumes, and the volume of brainstem protruding beyond the posterior fossa (all p < 0.05). MRI analysis of brain structures may provide a standardized value to indicate decompression surgery in children with achondroplasia. Full article
(This article belongs to the Special Issue Deep Learning in Computer Vision)
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18 pages, 4159 KiB  
Article
Preclinical Implementation of matRadiomics: A Case Study for Early Malformation Prediction in Zebrafish Model
by Fabiano Bini, Elisa Missori, Gaia Pucci, Giovanni Pasini, Franco Marinozzi, Giusi Irma Forte, Giorgio Russo and Alessandro Stefano
J. Imaging 2024, 10(11), 290; https://doi.org/10.3390/jimaging10110290 - 14 Nov 2024
Viewed by 637
Abstract
Radiomics provides a structured approach to support clinical decision-making through key steps; however, users often face difficulties when switching between various software platforms to complete the workflow. To streamline this process, matRadiomics integrates the entire radiomics workflow within a single platform. This study [...] Read more.
Radiomics provides a structured approach to support clinical decision-making through key steps; however, users often face difficulties when switching between various software platforms to complete the workflow. To streamline this process, matRadiomics integrates the entire radiomics workflow within a single platform. This study extends matRadiomics to preclinical settings and validates it through a case study focused on early malformation differentiation in a zebrafish model. The proposed plugin incorporates Pyradiomics and streamlines feature extraction, selection, and classification using machine learning models (linear discriminant analysis—LDA; k-nearest neighbors—KNNs; and support vector machines—SVMs) with k-fold cross-validation for model validation. Classifier performances are evaluated using area under the ROC curve (AUC) and accuracy. The case study indicated the criticality of the long time required to extract features from preclinical images, generally of higher resolution than clinical images. To address this, a feature analysis was conducted to optimize settings, reducing extraction time while maintaining similarity to the original features. As a result, SVM exhibited the best performance for early malformation differentiation in zebrafish (AUC = 0.723; accuracy of 0.72). This case study underscores the plugin’s versatility and effectiveness in early biological outcome prediction, emphasizing its applicability across biomedical research fields. Full article
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21 pages, 9768 KiB  
Article
The Methodology of Adaptive Levels of Interval for Laser Speckle Imaging
by Ali A. Al-Temeemy
J. Imaging 2024, 10(11), 289; https://doi.org/10.3390/jimaging10110289 - 11 Nov 2024
Viewed by 654
Abstract
A methodology is proposed for use in the laser speckle imaging field. This methodology modified the graphical and numerical speckle pattern imaging methods to improve their extraction and discrimination capabilities when processing the embedded temporal activity in the images of laser speckle patterns. [...] Read more.
A methodology is proposed for use in the laser speckle imaging field. This methodology modified the graphical and numerical speckle pattern imaging methods to improve their extraction and discrimination capabilities when processing the embedded temporal activity in the images of laser speckle patterns. This is through enabling these methods to adapt the levels of speckle images’ interval during processing to speed up the process and overcome the lack of discrimination when they deal with a complex scattering medium having regions of various scales of activity. The impact of using the new methodology on the imaging methods’ performance was evaluated using graphical and numerical evaluation tests, in addition, an exceptional laser speckle imaging system was designed and implemented to undertake a series of experimental validation tests on this methodology. The evaluation and experimental validation tests show the effectiveness of this methodology on the extraction and discrimination capabilities for the standard imaging speckle pattern methods and prove its ability to provide high performance with the real images of speckle patterns. The results also show an improvement in the processing speed for both graphical and numerical methods when the adaptive levels methodology is applied to them, which reaches 78% for the graphical and 87% for the numerical speckle processing methods. Full article
(This article belongs to the Section Image and Video Processing)
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23 pages, 5755 KiB  
Article
Iris Recognition System Using Advanced Segmentation Techniques and Fuzzy Clustering Methods for Robotic Control
by Slim Ben Chaabane, Rafika Harrabi and Hassene Seddik
J. Imaging 2024, 10(11), 288; https://doi.org/10.3390/jimaging10110288 - 8 Nov 2024
Viewed by 683
Abstract
The idea of developing a robot controlled by iris movement to assist physically disabled individuals is, indeed, innovative and has the potential to significantly improve their quality of life. This technology can empower individuals with limited mobility and enhance their ability to interact [...] Read more.
The idea of developing a robot controlled by iris movement to assist physically disabled individuals is, indeed, innovative and has the potential to significantly improve their quality of life. This technology can empower individuals with limited mobility and enhance their ability to interact with their environment. Disability of movement has a huge impact on the lives of physically disabled people. Therefore, there is need to develop a robot that can be controlled using iris movement. The main idea of this work revolves around iris recognition from an eye image, specifically identifying the centroid of the iris. The centroid’s position is then utilized to issue commands to control the robot. This innovative approach leverages iris movement as a means of communication and control, offering a potential breakthrough in assisting individuals with physical disabilities. The proposed method aims to improve the precision and effectiveness of iris recognition by incorporating advanced segmentation techniques and fuzzy clustering methods. Fast gradient filters using a fuzzy inference system (FIS) are employed to separate the iris from its surroundings. Then, the bald eagle search (BES) algorithm is employed to locate and isolate the iris region. Subsequently, the fuzzy KNN algorithm is applied for the matching process. This combined methodology aims to improve the overall performance of iris recognition systems by leveraging advanced segmentation, search, and classification techniques. The results of the proposed model are validated using the true success rate (TSR) and compared to those of other existing models. These results highlight the effectiveness of the proposed method for the 400 tested images representing 40 people. Full article
(This article belongs to the Topic Applications in Image Analysis and Pattern Recognition)
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31 pages, 14397 KiB  
Article
Precision Ice Detection on Power Transmission Lines: A Novel Approach with Multi-Scale Retinex and Advanced Morphological Edge Detection Monitoring
by Nalini Rizkyta Nusantika, Jin Xiao and Xiaoguang Hu
J. Imaging 2024, 10(11), 287; https://doi.org/10.3390/jimaging10110287 - 8 Nov 2024
Viewed by 563
Abstract
Line icings on the power transmission lines are dangerous risks that may lead to situations like structural damage or power outages. The current techniques used for identifying ice have certain drawbacks, particularly when used in complex environments. This paper aims to detect lines [...] Read more.
Line icings on the power transmission lines are dangerous risks that may lead to situations like structural damage or power outages. The current techniques used for identifying ice have certain drawbacks, particularly when used in complex environments. This paper aims to detect lines on the top and bottom in PTLI with low illumination and complex backgrounds. The proposed method integrates multistage image processing techniques, including image enhancement, filtering, thresholding, object isolation, edge detection, and line identification. A binocular camera is used to capture images of PTLI. The effectiveness of the method is evaluated through a series of metrics, including accuracy, sensitivity, specificity, and precision, and compared with existing methods. It is observed that the proposed method significantly outperforms the existing methods of ice detection and thickness measurement. This paper uses average accuracy of detection and isolation of ice formations under various conditions at a percentage of 98.35, sensitivity at 91.63%, specificity at 99.42%, and precision of 96.03%. Furthermore, the accuracy of the ice thickness based on the thickness measurements is shown with a much smaller RMSE of 1.20 mm, MAE of 1.10 mm, and R-squared of 0.95. The proposed scheme for ice detection provides a more accurate and reliable method for monitoring ice formation on power transmission lines. Full article
(This article belongs to the Topic Applications in Image Analysis and Pattern Recognition)
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17 pages, 3237 KiB  
Article
ssc-cdi: A Memory-Efficient, Multi-GPU Package for Ptychography with Extreme Data
by Yuri Rossi Tonin, Alan Zanoni Peixinho, Mauro Luiz Brandao-Junior, Paola Ferraz and Eduardo Xavier Miqueles
J. Imaging 2024, 10(11), 286; https://doi.org/10.3390/jimaging10110286 - 7 Nov 2024
Viewed by 964
Abstract
We introduce <tt>ssc-cdi</tt>, an open-source software package from the Sirius Scientific Computing family, designed for memory-efficient, single-node multi-GPU ptychography reconstruction. <tt>ssc-cdi</tt> offers a range of reconstruction engines in Python version 3.9.2 and C++/CUDA. It aims at developing local expertise and customized solutions to [...] Read more.
We introduce <tt>ssc-cdi</tt>, an open-source software package from the Sirius Scientific Computing family, designed for memory-efficient, single-node multi-GPU ptychography reconstruction. <tt>ssc-cdi</tt> offers a range of reconstruction engines in Python version 3.9.2 and C++/CUDA. It aims at developing local expertise and customized solutions to meet the specific needs of beamlines and user community of the Brazilian Synchrotron Light Laboratory (LNLS). We demonstrate ptychographic reconstruction of beamline data and present benchmarks for the package. Results show that <tt>ssc-cdi</tt> effectively handles extreme datasets typical of modern X-ray facilities without significantly compromising performance, offering a complementary approach to well-established packages of the community and serving as a robust tool for high-resolution imaging applications. Full article
(This article belongs to the Special Issue Recent Advances in X-ray Imaging)
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18 pages, 6875 KiB  
Article
A Mathematical Model for Wind Velocity Field Reconstruction and Visualization Taking into Account the Topography Influence
by Guzel Khayretdinova and Christian Gout
J. Imaging 2024, 10(11), 285; https://doi.org/10.3390/jimaging10110285 - 7 Nov 2024
Viewed by 784
Abstract
In this paper, we propose a global modelling for vector field approximation from a given finite set of vectors (corresponding to the wind velocity field or marine currents). In the modelling, we propose using the minimization on a Hilbert space of an energy [...] Read more.
In this paper, we propose a global modelling for vector field approximation from a given finite set of vectors (corresponding to the wind velocity field or marine currents). In the modelling, we propose using the minimization on a Hilbert space of an energy functional that includes a fidelity criterion to the data and a smoothing term. We discretize the continuous problem using a finite elements method. We then propose taking into account the topographic effects on the wind velocity field, and visualization using a free library is also proposed, which constitutes an added value compared to other vector field approximation models. Full article
(This article belongs to the Special Issue Geometry Reconstruction from Images (2nd Edition))
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18 pages, 5155 KiB  
Article
Strabismus Detection in Monocular Eye Images for Telemedicine Applications
by Wattanapong Kurdthongmee, Lunla Udomvej, Arsanchai Sukkuea, Piyadhida Kurdthongmee, Chitchanok Sangeamwong and Chayanid Chanakarn
J. Imaging 2024, 10(11), 284; https://doi.org/10.3390/jimaging10110284 - 7 Nov 2024
Viewed by 732
Abstract
This study presents a novel method for the early detection of strabismus, a common eye misalignment disorder, with an emphasis on its application in telemedicine. The technique leverages synchronized eye movements to estimate the pupil location of one eye based on the other, [...] Read more.
This study presents a novel method for the early detection of strabismus, a common eye misalignment disorder, with an emphasis on its application in telemedicine. The technique leverages synchronized eye movements to estimate the pupil location of one eye based on the other, achieving close alignment in non-strabismic cases. Regression models for each eye are developed using advanced machine learning algorithms, and significant discrepancies between estimated and actual pupil positions indicate the presence of strabismus. This approach provides a non-invasive, efficient solution for early detection and bridges the gap between basic research and clinical care by offering an accessible, machine learning-based tool that facilitates timely intervention and improved outcomes in diverse healthcare settings. The potential for pediatric screening is discussed as a possible direction for future research. Full article
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11 pages, 3523 KiB  
Article
Bright Luminal Sign on High b-Value Diffusion-Weighted Magnetic Resonance Enterography Imaging as a New Biomarker to Predict Fibrotic Strictures in Crohn’s Disease Patients: A Retrospective Preliminary Study
by Luca Pio Stoppino, Stefano Piscone, Ottavia Quarta Colosso, Sara Saccone, Paola Milillo, Nicola Della Valle, Rodolfo Sacco, Alfonso Reginelli, Luca Macarini and Roberta Vinci
J. Imaging 2024, 10(11), 283; https://doi.org/10.3390/jimaging10110283 - 7 Nov 2024
Viewed by 531
Abstract
A retrospective analysis was conducted to investigate how a bright luminal sign on high b-value diffusion-weighted imaging (DWI) could be considered as a new biomarker for identifying fibrotic strictures in Crohn’s disease (CD). Fibrotic strictures, due to excessive deposition of extracellular matrix following [...] Read more.
A retrospective analysis was conducted to investigate how a bright luminal sign on high b-value diffusion-weighted imaging (DWI) could be considered as a new biomarker for identifying fibrotic strictures in Crohn’s disease (CD). Fibrotic strictures, due to excessive deposition of extracellular matrix following chronic inflammatory processes, can be difficult to distinguish from inflammatory strictures using endoscopy. This study was performed on 65 patients with CD who underwent MRE, and among them 32 patients showed the bright luminal sign on high b-value DWI. DWI findings were compared to pre- and post-contrast MRE data. Luminal bright sign performance results were calculated using a confusion matrix, the relationship between categorical variables was assessed by the χ2 test of independence, and the Kruskal–Wallis test (ANOVA) was used for the assessment of statistical significance of differences between groups. The results indicated a high sensitivity (90%) and specificity (85%) of the bright luminal sign for fibro-stenotic CD and a significant correlation between DWI luminal brightness and markers such as the homogeneous enhancement pattern (p < 0.001), increase in enhancement percentage from 70 s to 7 min after gadolinium injection (p < 0.001), and submucosal fat penetration (p = 0.05). These findings indicate that DWI hyperintensity can be considered as a good non-invasive indicator for the detection of severe intestinal fibrosis and may provide an efficient and accurate method for assessing fibrotic strictures. This new non-invasive biomarker could allow an early diagnosis of fibrotic stricture, delaying the onset of complications and subsequent surgery. Moreover, further evaluations through larger prospective trials with histopathological correlation are needed to confirm these results and completely determine the clinical benefits of DWI in treating CD. Full article
(This article belongs to the Special Issue New Perspectives in Medical Image Analysis)
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20 pages, 2978 KiB  
Article
Considerations for a Micromirror Array Optimized for Compressive Sensing (VIS to MIR) in Space Applications
by Ulrike Dauderstädt, Peter Dürr, Detlef Kunze, Sara Francés González, Donato Borrelli, Lorenzo Palombi, Valentina Raimondi and Michael Wagner
J. Imaging 2024, 10(11), 282; https://doi.org/10.3390/jimaging10110282 - 5 Nov 2024
Viewed by 673
Abstract
Earth observation (EO) is crucial for addressing environmental and societal challenges, but it struggles with revisit times and spatial resolution. The EU-funded SURPRISE project aims to improve EO capabilities by studying space instrumentation using compressive sensing (CS) implemented through spatial light modulators (SLMs) [...] Read more.
Earth observation (EO) is crucial for addressing environmental and societal challenges, but it struggles with revisit times and spatial resolution. The EU-funded SURPRISE project aims to improve EO capabilities by studying space instrumentation using compressive sensing (CS) implemented through spatial light modulators (SLMs) based on micromirror arrays (MMAs) to improve the ground sampling distance. In the SURPRISE project, we studied the development of an MMA that meets the requirements of a CS-based geostationary instrument working in the visible (VIS) and mid-infrared (MIR) spectral ranges. This paper describes the optical simulation procedure and the results obtained for analyzing the performance of such an MMA with the goal of identifying a mirror design that would allow the device to meet the optical requirements of this specific application. Full article
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16 pages, 5429 KiB  
Article
Video WeAther RecoGnition (VARG): An Intensity-Labeled Video Weather Recognition Dataset
by Himanshu Gupta, Oleksandr Kotlyar, Henrik Andreasson and Achim J. Lilienthal
J. Imaging 2024, 10(11), 281; https://doi.org/10.3390/jimaging10110281 - 5 Nov 2024
Viewed by 705
Abstract
Adverse weather (rain, snow, and fog) can negatively impact computer vision tasks by introducing noise in sensor data; therefore, it is essential to recognize weather conditions for building safe and robust autonomous systems in the agricultural and autonomous driving/drone sectors. The performance degradation [...] Read more.
Adverse weather (rain, snow, and fog) can negatively impact computer vision tasks by introducing noise in sensor data; therefore, it is essential to recognize weather conditions for building safe and robust autonomous systems in the agricultural and autonomous driving/drone sectors. The performance degradation in computer vision tasks due to adverse weather depends on the type of weather and the intensity, which influences the amount of noise in sensor data. However, existing weather recognition datasets often lack intensity labels, limiting their effectiveness. To address this limitation, we present VARG, a novel video-based weather recognition dataset with weather intensity labels. The dataset comprises a diverse set of short video sequences collected from various social media platforms and videos recorded by the authors, processed into usable clips, and categorized into three major weather categories, rain, fog, and snow, with three intensity classes: absent/no, moderate, and high. The dataset contains 6742 annotated clips from 1079 videos, with the training set containing 5159 clips and the test set containing 1583 clips. Two sets of annotations are provided for training, the first set to train the models as a multi-label weather intensity classifier and the second set to train the models as a multi-class classifier for three weather scenarios. This paper describes the dataset characteristics and presents an evaluation study using several deep learning-based video recognition approaches for weather intensity prediction. Full article
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20 pages, 12767 KiB  
Article
A Real-Time End-to-End Framework with a Stacked Model Using Ultrasound Video for Cardiac Septal Defect Decision-Making
by Siti Nurmani, Ria Nova, Ade Iriani Sapitri, Muhammad Naufal Rachmatullah, Bambang Tutuko, Firdaus Firdaus, Annisa Darmawahyuni, Anggun Islami, Satria Mandala, Radiyati Umi Partan, Akhiar Wista Arum and Rio Bastian
J. Imaging 2024, 10(11), 280; https://doi.org/10.3390/jimaging10110280 - 3 Nov 2024
Viewed by 865
Abstract
Echocardiography is the gold standard for the comprehensive diagnosis of cardiac septal defects (CSDs). Currently, echocardiography diagnosis is primarily based on expert observation, which is laborious and time-consuming. With digitization, deep learning (DL) can be used to improve the efficiency of the diagnosis. [...] Read more.
Echocardiography is the gold standard for the comprehensive diagnosis of cardiac septal defects (CSDs). Currently, echocardiography diagnosis is primarily based on expert observation, which is laborious and time-consuming. With digitization, deep learning (DL) can be used to improve the efficiency of the diagnosis. This study presents a real-time end-to-end framework tailored for pediatric ultrasound video analysis for CSD decision-making. The framework employs an advanced real-time architecture based on You Only Look Once (Yolo) techniques for CSD decision-making with high accuracy. Leveraging the state of the art with the Yolov8l (large) architecture, the proposed model achieves a robust performance in real-time processes. It can be observed that the experiment yielded a mean average precision (mAP) exceeding 89%, indicating the framework’s effectiveness in accurately diagnosing CSDs from ultrasound (US) videos. The Yolov8l model exhibits precise performance in the real-time testing of pediatric patients from Mohammad Hoesin General Hospital in Palembang, Indonesia. Based on the results of the proposed model using 222 US videos, it exhibits 95.86% accuracy, 96.82% sensitivity, and 98.74% specificity. During real-time testing in the hospital, the model exhibits a 97.17% accuracy, 95.80% sensitivity, and 98.15% specificity; only 3 out of the 53 US videos in the real-time process were diagnosed incorrectly. This comprehensive approach holds promise for enhancing clinical decision-making and improving patient outcomes in pediatric cardiology. Full article
(This article belongs to the Special Issue Deep Learning in Image Analysis: Progress and Challenges)
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16 pages, 9878 KiB  
Article
An Enhanced Deep Learning Model for Effective Crop Pest and Disease Detection
by Yongqi Yuan, Jinhua Sun and Qian Zhang
J. Imaging 2024, 10(11), 279; https://doi.org/10.3390/jimaging10110279 - 2 Nov 2024
Viewed by 1051
Abstract
Traditional machine learning methods struggle with plant pest and disease image recognition, particularly when dealing with small sample sizes, indistinct features, and numerous categories. This paper proposes an improved ResNet34 model (ESA-ResNet34) for crop pest and disease detection. The model employs ResNet34 as [...] Read more.
Traditional machine learning methods struggle with plant pest and disease image recognition, particularly when dealing with small sample sizes, indistinct features, and numerous categories. This paper proposes an improved ResNet34 model (ESA-ResNet34) for crop pest and disease detection. The model employs ResNet34 as its backbone and introduces an efficient spatial attention mechanism (effective spatial attention, ESA) to focus on key regions of the images. By replacing the standard convolutions in ResNet34 with depthwise separable convolutions, the model reduces its parameter count by 85.37% and its computational load by 84.51%. Additionally, Dropout is used to mitigate overfitting, and data augmentation techniques such as center cropping and horizontal flipping are employed to enhance the model’s robustness. The experimental results show that the improved algorithm achieves an accuracy, precision, and F1 score of 87.09%, 87.14%, and 86.91%, respectively, outperforming several benchmark models (including AlexNet, VGG16, MobileNet, DenseNet, and various ResNet variants). These findings demonstrate that the proposed ESA-ResNet34 model significantly enhances crop pest and disease detection. Full article
(This article belongs to the Special Issue Imaging Applications in Agriculture)
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20 pages, 6618 KiB  
Article
Convolutional Neural Network-Based Deep Learning Methods for Skeletal Growth Prediction in Dental Patients
by Miran Hikmat Mohammed, Zana Qadir Omer, Barham Bahroz Aziz, Jwan Fateh Abdulkareem, Trefa Mohammed Ali Mahmood, Fadil Abdullah Kareem and Dena Nadhim Mohammad
J. Imaging 2024, 10(11), 278; https://doi.org/10.3390/jimaging10110278 - 2 Nov 2024
Viewed by 781
Abstract
This study aimed to predict the skeletal growth maturation using convolutional neural network-based deep learning methods using cervical vertebral maturation and the lower 2nd molar calcification level so that skeletal maturation can be detected from orthopantomography using multiclass classification. About 1200 cephalometric radiographs [...] Read more.
This study aimed to predict the skeletal growth maturation using convolutional neural network-based deep learning methods using cervical vertebral maturation and the lower 2nd molar calcification level so that skeletal maturation can be detected from orthopantomography using multiclass classification. About 1200 cephalometric radiographs and 1200 OPGs were selected from patients seeking treatment in dental centers. The level of skeletal maturation was detected by CNN using the multiclass classification method, and each image was identified as a cervical vertebral maturation index (CVMI); meanwhile, the chronological age was estimated from the level of the 2nd molar calcification. The model’s final result demonstrates a high degree of accuracy with which each stage and gender can be predicted. Cervical vertebral maturation reported high accuracy in males (98%), while females showed high accuracy of 2nd molar calcification. CNN multiclass classification is an accurate method to detect the level of maturation, whether from cervical maturation or the calcification of the lower 2nd molar, and the calcification level of the lower 2nd molar is a reliable method to trust in the growth level, so the traditional OPG is enough for this purpose. Full article
(This article belongs to the Section AI in Imaging)
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21 pages, 12658 KiB  
Article
A Dual-Module System for Copyright-Free Image Recommendation and Infringement Detection in Educational Materials
by Yeongha Kim, Soyeon Kim, Seonghyun Min, Youngung Han, Ohyoung Lee and Wongyum Kim
J. Imaging 2024, 10(11), 277; https://doi.org/10.3390/jimaging10110277 - 1 Nov 2024
Viewed by 765
Abstract
Images are extensively utilized in educational materials due to their efficacy in conveying complex concepts. However, unauthorized use of images frequently results in legal issues related to copyright infringement. To mitigate this problem, we introduce a dual-module system specifically designed for educators. The [...] Read more.
Images are extensively utilized in educational materials due to their efficacy in conveying complex concepts. However, unauthorized use of images frequently results in legal issues related to copyright infringement. To mitigate this problem, we introduce a dual-module system specifically designed for educators. The first module, a copyright infringement detection system, employs deep learning techniques to verify the copyright status of images. It utilizes a Convolutional Variational Autoencoder (CVAE) model to extract significant features from copyrighted images and compares them against user-provided images. If infringement is detected, the second module, an image retrieval system, recommends alternative copyright-free images using a Vision Transformer (ViT)-based hashing model. Evaluation on benchmark datasets demonstrates the system’s effectiveness, achieving a mean Average Precision (mAP) of 0.812 on the Flickr25k dataset. Additionally, a user study involving 65 teachers indicates high satisfaction levels, particularly in addressing copyright concerns and ease of use. Our system significantly aids educators in creating educational materials that comply with copyright regulations. Full article
(This article belongs to the Section Image and Video Processing)
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20 pages, 49386 KiB  
Article
On the Dynamism of Paintings Through the Distribution of Edge Directions
by Adrien Deliege, Maria Giulia Dondero and Enzo D’Armenio
J. Imaging 2024, 10(11), 276; https://doi.org/10.3390/jimaging10110276 - 1 Nov 2024
Viewed by 819
Abstract
The digitization of artworks has recently offered new computational perspectives on the study of art history. While much of the focus has been on classifying styles or identifying objects, the analysis of more abstract concepts, such as the perception of motion or dynamism [...] Read more.
The digitization of artworks has recently offered new computational perspectives on the study of art history. While much of the focus has been on classifying styles or identifying objects, the analysis of more abstract concepts, such as the perception of motion or dynamism in still images, remains largely unexplored. Semioticians and artists have long explored the representation of dynamism in still images, but they often did so through theoretical frameworks or visual techniques, without a quantitative approach to measuring it. This paper proposes a method for computing and comparing the dynamism of paintings through edge detection. Our approach is based on the idea that the dynamism of a painting can be quantified by analyzing the edges in the image, whose distribution can be used to identify patterns and trends across artists and movements. We demonstrate the applicability of our method in three key areas: studying the temporal evolution of dynamism across different artistic styles, as well as within the works of a single artist (Wassily Kandinsky), visualizing and clustering a large database of abstract paintings through PixPlot, and retrieving similarly dynamic images. We show that the dynamism of a painting can be effectively quantified and visualized using edge detection techniques, providing new insights into the study of visual culture. Full article
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19 pages, 2586 KiB  
Review
A Review of Application of Deep Learning in Endoscopic Image Processing
by Zihan Nie, Muhao Xu, Zhiyong Wang, Xiaoqi Lu and Weiye Song
J. Imaging 2024, 10(11), 275; https://doi.org/10.3390/jimaging10110275 - 1 Nov 2024
Viewed by 982
Abstract
Deep learning, particularly convolutional neural networks (CNNs), has revolutionized endoscopic image processing, significantly enhancing the efficiency and accuracy of disease diagnosis through its exceptional ability to extract features and classify complex patterns. This technology automates medical image analysis, alleviating the workload of physicians [...] Read more.
Deep learning, particularly convolutional neural networks (CNNs), has revolutionized endoscopic image processing, significantly enhancing the efficiency and accuracy of disease diagnosis through its exceptional ability to extract features and classify complex patterns. This technology automates medical image analysis, alleviating the workload of physicians and enabling a more focused and personalized approach to patient care. However, despite these remarkable achievements, there are still opportunities to further optimize deep learning models for endoscopic image analysis, including addressing limitations such as the requirement for large annotated datasets and the challenge of achieving higher diagnostic precision, particularly for rare or subtle pathologies. This review comprehensively examines the profound impact of deep learning on endoscopic image processing, highlighting its current strengths and limitations. It also explores potential future directions for research and development, outlining strategies to overcome existing challenges and facilitate the integration of deep learning into clinical practice. Ultimately, the goal is to contribute to the ongoing advancement of medical imaging technologies, leading to more accurate, personalized, and optimized medical care for patients. Full article
(This article belongs to the Section Image and Video Processing)
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21 pages, 4755 KiB  
Article
MIMO-Uformer: A Transformer-Based Image Deblurring Network for Vehicle Surveillance Scenarios
by Jian Zhang, Baoping Cheng, Tengying Zhang, Yongsheng Zhao, Tao Fu, Zijian Wu and Xiaoming Tao
J. Imaging 2024, 10(11), 274; https://doi.org/10.3390/jimaging10110274 - 31 Oct 2024
Viewed by 670
Abstract
Motion blur is a common problem in the field of surveillance scenarios, and it obstructs the acquisition of valuable information. Thanks to the success of deep learning, a sequence of CNN-based architecture has been designed for image deblurring and has made great progress. [...] Read more.
Motion blur is a common problem in the field of surveillance scenarios, and it obstructs the acquisition of valuable information. Thanks to the success of deep learning, a sequence of CNN-based architecture has been designed for image deblurring and has made great progress. As another type of neural network, transformers have exhibited powerful deep representation learning and impressive performance based on high-level vision tasks. Transformer-based networks leverage self-attention to capture the long-range dependencies in the data, yet the computational complexity is quadratic to the spatial resolution, which makes transformers infeasible for the restoration of high-resolution images. In this article, we propose an efficient transformer-based deblurring network, named MIMO-Uformer, for vehicle-surveillance scenarios. The distinct feature of the MIMO-Uformer is that the basic-window-based multi-head self-attention (W-MSA) of the Swin transformer is employed to reduce the computational complexity and then incorporated into a multi-input and multi-output U-shaped network (MIMO-UNet). The performance can benefit from the operation of multi-scale images by MIMO-UNet. However, most deblurring networks are designed for global blur, while local blur is more common under vehicle-surveillance scenarios since the motion blur is primarily caused by local moving vehicles. Based on this observation, we further propose an Intersection over Patch (IoP) factor and a supervised morphological loss to improve the performance based on local blur. Extensive experiments on a public and a self-established dataset are carried out to verify the effectiveness. As a result, the deblurring behavior based on PSNR is improved at least 0.21 dB based on GOPRO and 0.74 dB based on the self-established datasets compared to the existing benchmarks. Full article
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13 pages, 5013 KiB  
Article
Influence of Target Surface BRDF on Non-Line-of-Sight Imaging
by Yufeng Yang, Kailei Yang and Ao Zhang
J. Imaging 2024, 10(11), 273; https://doi.org/10.3390/jimaging10110273 - 29 Oct 2024
Viewed by 699
Abstract
The surface material of an object is a key factor that affects non-line-of-sight (NLOS) imaging. In this paper, we introduce the bidirectional reflectance distribution function (BRDF) into NLOS imaging to study how the target surface material influences the quality of NLOS images. First, [...] Read more.
The surface material of an object is a key factor that affects non-line-of-sight (NLOS) imaging. In this paper, we introduce the bidirectional reflectance distribution function (BRDF) into NLOS imaging to study how the target surface material influences the quality of NLOS images. First, the BRDF of two surface materials (aluminized insulation material and white paint board) was modeled using deep neural networks and compared with a five-parameter empirical model to validate the method’s accuracy. The method was then applied to fit BRDF data for different common materials. Finally, NLOS target simulations with varying surface materials were reconstructed using the confocal diffusion tomography algorithm. The reconstructed NLOS images were classified via a convolutional neural network to assess how different surface materials impacted imaging quality. The results show that image clarity improves when decreasing the specular reflection and increasing the diffuse reflection, with the best results obtained for surfaces exhibiting a high diffuse reflection and no specular reflection. Full article
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34 pages, 10545 KiB  
Article
Mapping the Knowledge Structure of Image Recognition in Cultural Heritage: A Scientometric Analysis Using CiteSpace, VOSviewer, and Bibliometrix
by Fei Ju
J. Imaging 2024, 10(11), 272; https://doi.org/10.3390/jimaging10110272 - 26 Oct 2024
Viewed by 1098
Abstract
The application of image recognition techniques in the realm of cultural heritage represents a significant advancement in preservation and analysis. However, existing scholarship on this topic has largely concentrated on specific methodologies and narrow categories, leaving a notable gap in broader understanding. This [...] Read more.
The application of image recognition techniques in the realm of cultural heritage represents a significant advancement in preservation and analysis. However, existing scholarship on this topic has largely concentrated on specific methodologies and narrow categories, leaving a notable gap in broader understanding. This study aims to address this deficiency through a thorough bibliometric analysis of the Web of Science (WoS) literature from 1995 to 2024, integrating both qualitative and quantitative approaches to elucidate the macro-level evolution of the field. Our analysis reveals that the integration of artificial intelligence, particularly deep learning, has significantly enhanced digital documentation, artifact identification, and overall cultural heritage management. Looking forward, it is imperative that research endeavors expand the application of these techniques into multidisciplinary domains, including ecological monitoring and social policy. Additionally, this paper examines non-invasive identification methods for material classification and damage detection, highlighting the role of advanced modeling in optimizing the management of heritage sites. The emergence of keywords such as ‘ecosystem services’, ‘models’, and ‘energy’ in the recent literature underscores a shift toward sustainable practices in cultural heritage conservation. This trend reflects a growing recognition of the interconnectedness between heritage preservation and environmental sciences. The heightened awareness of environmental crises has, in turn, spurred the development of image recognition technologies tailored for cultural heritage applications. Prospective research in this field is anticipated to witness rapid advancements, particularly in real-time monitoring and community engagement, leading to the creation of more holistic tools for heritage conservation. Full article
(This article belongs to the Section Computer Vision and Pattern Recognition)
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28 pages, 4463 KiB  
Article
Cleft Lip and Palate Classification Through Vision Transformers and Siamese Neural Networks
by Oraphan Nantha, Benjaporn Sathanarugsawait and Prasong Praneetpolgrang
J. Imaging 2024, 10(11), 271; https://doi.org/10.3390/jimaging10110271 - 25 Oct 2024
Viewed by 726
Abstract
This study introduces a novel approach for the diagnosis of Cleft Lip and/or Palate (CL/P) by integrating Vision Transformers (ViTs) and Siamese Neural Networks. Our study is the first to employ this integration specifically for CL/P classification, leveraging the strengths of both models [...] Read more.
This study introduces a novel approach for the diagnosis of Cleft Lip and/or Palate (CL/P) by integrating Vision Transformers (ViTs) and Siamese Neural Networks. Our study is the first to employ this integration specifically for CL/P classification, leveraging the strengths of both models to handle complex, multimodal data and few-shot learning scenarios. Unlike previous studies that rely on single-modality data or traditional machine learning models, we uniquely fuse anatomical data from ultrasound images with functional data from speech spectrograms. This multimodal approach captures both structural and acoustic features critical for accurate CL/P classification. Employing Siamese Neural Networks enables effective learning from a small number of labeled examples, enhancing the model’s generalization capabilities in medical imaging contexts where data scarcity is a significant challenge. The models were tested on the UltraSuite CLEFT dataset, which includes ultrasound video sequences and synchronized speech data, across three cleft types: Bilateral, Unilateral, and Palate-only clefts. The two-stage model demonstrated superior performance in classification accuracy (82.76%), F1-score (80.00–86.00%), precision, and recall, particularly distinguishing Bilateral and Unilateral Cleft Lip and Palate with high efficacy. This research underscores the significant potential of advanced AI techniques in medical diagnostics, offering valuable insights into their application for improving clinical outcomes in patients with CL/P. Full article
(This article belongs to the Section AI in Imaging)
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11 pages, 2484 KiB  
Article
Mitigating Interobserver Variability in Radiomics with ComBat: A Feasibility Study
by Alessia D’Anna, Giuseppe Stella, Anna Maria Gueli, Carmelo Marino and Alfredo Pulvirenti
J. Imaging 2024, 10(11), 270; https://doi.org/10.3390/jimaging10110270 - 24 Oct 2024
Viewed by 698
Abstract
This study investigates Intraobserver Features Variability (IFV) in radiomics studies and assesses the effectiveness of the ComBat harmonization method in mitigating these effects. Methods: This study utilizes data from the NSCLC-Radiomics-Interobserver1 dataset, comprising CT scans of 22 Non-Small Cell Lung Cancer (NSCLC) patients, [...] Read more.
This study investigates Intraobserver Features Variability (IFV) in radiomics studies and assesses the effectiveness of the ComBat harmonization method in mitigating these effects. Methods: This study utilizes data from the NSCLC-Radiomics-Interobserver1 dataset, comprising CT scans of 22 Non-Small Cell Lung Cancer (NSCLC) patients, with multiple Gross Tumor Volume (GTV) delineations performed by five radiation oncologists. Segmentation was completed manually (“vis”) or by autosegmentation with manual editing (“auto”). A total of 1229 radiomic features were extracted from each GTV, segmentation method, and oncologist. Features extracted included first order, shape, GLCM, GLRLM, GLSZM, and GLDM from original, wavelet-filtered, and LoG-filtered images. Results: Before implementing ComBat harmonization, 83% of features exhibited p-values below 0.05 in the “vis” approach; this percentage decreased to 34% post-harmonization. Similarly, for the “auto” approach, 75% of features demonstrated statistical significance prior to ComBat, but this figure declined to 33% after its application. Among a subset of three expert radiation oncologists, percentages changed from 77% to 25% for “vis” contouring and from 64% to 23% for “auto” contouring. This study demonstrates that ComBat harmonization could effectively reduce IFV, enhancing the feasibility of multicenter radiomics studies. It also highlights the significant impact of physician experience on radiomics analysis outcomes. Full article
(This article belongs to the Special Issue Advances in Image Analysis: Shapes, Textures and Multifractals)
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21 pages, 1071 KiB  
Article
YOLO-I3D: Optimizing Inflated 3D Models for Real-Time Human Activity Recognition
by Ruikang Luo, Aman Anand, Farhana Zulkernine and Francois Rivest
J. Imaging 2024, 10(11), 269; https://doi.org/10.3390/jimaging10110269 - 24 Oct 2024
Viewed by 943
Abstract
Human Activity Recognition (HAR) plays a critical role in applications such as security surveillance and healthcare. However, existing methods, particularly two-stream models like Inflated 3D (I3D), face significant challenges in real-time applications due to their high computational demand, especially from the optical flow [...] Read more.
Human Activity Recognition (HAR) plays a critical role in applications such as security surveillance and healthcare. However, existing methods, particularly two-stream models like Inflated 3D (I3D), face significant challenges in real-time applications due to their high computational demand, especially from the optical flow branch. In this work, we address these limitations by proposing two major improvements. First, we introduce a lightweight motion information branch that replaces the computationally expensive optical flow component with a lower-resolution RGB input, significantly reducing computation time. Second, we incorporate YOLOv5, an efficient object detector, to further optimize the RGB branch for faster real-time performance. Experimental results on the Kinetics-400 dataset demonstrate that our proposed two-stream I3D Light model improves the original I3D model’s accuracy by 4.13% while reducing computational cost. Additionally, the integration of YOLOv5 into the I3D model enhances accuracy by 1.42%, providing a more efficient solution for real-time HAR tasks. Full article
(This article belongs to the Section Computer Vision and Pattern Recognition)
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12 pages, 2392 KiB  
Communication
Multi-Head Attention Refiner for Multi-View 3D Reconstruction
by Kyunghee Lee, Ihjoon Cho, Boseung Yang and Unsang Park
J. Imaging 2024, 10(11), 268; https://doi.org/10.3390/jimaging10110268 - 24 Oct 2024
Viewed by 2182
Abstract
Traditional 3D reconstruction models have consistently faced the challenge of balancing high recall of object edges with maintaining a high precision. In this paper, we introduce a post-processing method, the Multi-Head Attention Refiner (MA-R), designed to address this issue by integrating a multi-head [...] Read more.
Traditional 3D reconstruction models have consistently faced the challenge of balancing high recall of object edges with maintaining a high precision. In this paper, we introduce a post-processing method, the Multi-Head Attention Refiner (MA-R), designed to address this issue by integrating a multi-head attention mechanism into the U-Net style refiner module. Our method demonstrates improved capability in capturing intricate image details, leading to significant enhancements in boundary predictions and recall rates. In our experiments, the proposed approach notably improves the reconstruction performance of Pix2Vox++ when multiple images are used as the input. Specifically, with 20-view images, our method achieves an IoU score of 0.730, a 1.1% improvement over the 0.719 of Pix2Vox++, and a 2.1% improvement in F-Score, achieving 0.483 compared to 0.462 of Pix2Vox++. These results underscore the robustness of our approach in enhancing both precision and recall in 3D reconstruction tasks involving multiple views. Full article
(This article belongs to the Special Issue Geometry Reconstruction from Images (2nd Edition))
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11 pages, 3819 KiB  
Article
Toward the Application of Dual-Energy Computed Tomography with Virtual Non-Hydroxyapatite Color-Coded Maps to Identify Traumatic Fractures in Daily Emergency Settings
by Claudio Ventura, Laura Maria Cacioppa, Sonia Caldarelli, Giovanni Sallei, Federico Lamponi, Marco Mascitti, Marina Carotti, Chiara Floridi and Gianluca Valeri
J. Imaging 2024, 10(11), 267; https://doi.org/10.3390/jimaging10110267 - 23 Oct 2024
Viewed by 669
Abstract
To evaluate the advantages of dual-energy computed tomography (DECT) virtual non-hydroxyapatite color mapping (VNHAP) in combination with standard bone CT (BCT) in the identification of subtle or occult traumatic fractures referred to emergency and acceptance departments (DEAs). Forty patients (22 men; mean age [...] Read more.
To evaluate the advantages of dual-energy computed tomography (DECT) virtual non-hydroxyapatite color mapping (VNHAP) in combination with standard bone CT (BCT) in the identification of subtle or occult traumatic fractures referred to emergency and acceptance departments (DEAs). Forty patients (22 men; mean age 83 ± 23.7 y) with suspected traumatic fractures referred to our emergency department and examined with a fast kilovoltage-switching single-source spectral CT scan between January and October 2023 were retrospectively reviewed. The BCT and VNHAP images were blindly evaluated by two radiologists with >10 years and <2 years of experience in musculoskeletal imaging. Both techniques were evaluated in terms of sensitivity (SE), specificity (SP), positive and negative predictive values (PPVs and NPVs) and accuracy for fracture detection, as confirmed at a 3-month clinical–instrumental follow-up. Inter-observer agreement and examination times were also analyzed. Fractures were confirmed in 18/40 cases. The highest values of diagnostic performance for VNHAP images were obtained in terms of SP (90.9% and 95%) and PPV (87.5% and 92.8%) and for the less experienced operator. No statistically significant differences were observed between the diagnostic accuracy of the two readers in the evaluation of VNHAP images. Inter-observer agreement was moderate (κ = 0.536) for BCT and substantial (κ = 0.680) for VNHAP. Comparing the two operators, a significantly longer examination time for BCT and no significant difference for VNHAP were registered. Our preliminary experience may encourage the employment of VNHAP maps in combination with BCT images in emergency settings. Their use could be time-saving and valuable in terms of diagnostic performance, especially for less experienced operators. Full article
(This article belongs to the Special Issue New Perspectives in Medical Image Analysis)
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13 pages, 3266 KiB  
Article
Frequency Domain-Based Super Resolution Using Two-Dimensional Structure Consistency for Ultra-High-Resolution Display
by Yu Lim Seo, Suk-Ju Kang and Yeon-Kug Moon
J. Imaging 2024, 10(11), 266; https://doi.org/10.3390/jimaging10110266 - 23 Oct 2024
Viewed by 706
Abstract
Recent advancements in the field of super resolution (SR) have seen the adoption of generative adversarial networks (GANs) for realistic images. In this case, when performing with low-resolution (LR) images, several challenges arise due to the loss of high-frequency details from high-resolution (HR) [...] Read more.
Recent advancements in the field of super resolution (SR) have seen the adoption of generative adversarial networks (GANs) for realistic images. In this case, when performing with low-resolution (LR) images, several challenges arise due to the loss of high-frequency details from high-resolution (HR) images, potentially leading to unwanted distortions in the generated SR images. Our paper presents a novel solution by using two-dimensional structure consistency (TSC) for image analysis. The TSC serves as a mask, enabling adaptive analysis based on the unique frequency characteristics of different image regions. Furthermore, a mutual loss mechanism, which dynamically adjusts the training process based on the results filtered by the TSC-based mask, is introduced. Additionally, the TSC loss is proposed to enhance our model capacity to generate precise TSC in high-frequency regions. As a result, our method effectively reduces distortions in high-frequency areas while preserving clarity in regions containing low-frequency components. Our method outperforms other SR techniques, demonstrating superior results in both qualitative and quantitative evaluations. Quantitative measurements, including PSNR, SSIM, and the perceptual metric LPIPS, show comparable PSNR and SSIM values, while the perceptual SR quality is notably improved according to the LPIPS metric. Full article
(This article belongs to the Section Image and Video Processing)
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