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Beyond Nyquist: A Comparative Analysis of 3D Deep Learning Models Enhancing MRI Resolution
Optimisation of Convolution-Based Image Lightness Processing
Automatic Classification of Nodules from 2D Ultrasound Images Using Deep Learning Networks
Gastric Cancer Image Classification: A Comparative Analysis and Feature Fusion Strategies

Journal Description

Journal of Imaging

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

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), PubMed, PMC, dblp, Inspec, Ei Compendex, and other databases.
Journal Rank: CiteScore - Q1 (Computer Graphics and Computer-Aided Design)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20.9 days after submission; acceptance to publication is undertaken in 3.4 days (median values for papers published in this journal in the first half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 2.7 (2023); 5-Year Impact Factor: 3.0 (2023)

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

Latest Articles

22 pages, 828 KiB

Open AccessArticle

MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging

by Sibtain Syed, Rehan Ahmed, Arshad Iqbal, Naveed Ahmed and Mohammed Ali Alshara

J. Imaging 2024, 10(12), 322; https://doi.org/10.3390/jimaging10120322 - 13 Dec 2024

With technological advancements, remarkable progress has been made with the convergence of health sciences and Artificial Intelligence (AI). Modern health systems are proposed to ease patient diagnostics. However, the challenge is to provide AI-based precautions to patients and doctors for more accurate risk [...] Read more.

With technological advancements, remarkable progress has been made with the convergence of health sciences and Artificial Intelligence (AI). Modern health systems are proposed to ease patient diagnostics. However, the challenge is to provide AI-based precautions to patients and doctors for more accurate risk assessment. The proposed healthcare system aims to integrate patients, doctors, laboratories, pharmacies, and administrative personnel use cases and their primary functions onto a single platform. The proposed framework can also process microscopic images, CT scans, X-rays, and MRI to classify malignancy and give doctors a set of AI precautions for patient risk assessment. The proposed framework incorporates various DCNN models for identifying different forms of tumors and fractures in the human body i.e., brain, bones, lungs, kidneys, and skin, and generating precautions with the help of the Fined-Tuned Large Language Model (LLM) i.e., Generative Pretrained Transformer 4 (GPT-4). With enough training data, DCNN can learn highly representative, data-driven, hierarchical image features. The GPT-4 model is selected for generating precautions due to its explanation, reasoning, memory, and accuracy on prior medical assessments and research studies. Classification models are evaluated by classification report (i.e., Recall, Precision, F1 Score, Support, Accuracy, and Macro and Weighted Average) and confusion matrix and have shown robust performance compared to the conventional schemes. Full article

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

16 pages, 1289 KiB

Open AccessArticle

DAT: Deep Learning-Based Acceleration-Aware Trajectory Forecasting

by Ali Asghar Sharifi, Ali Zoljodi and Masoud Daneshtalab

J. Imaging 2024, 10(12), 321; https://doi.org/10.3390/jimaging10120321 - 13 Dec 2024

As the demand for autonomous driving (AD) systems has increased, the enhancement of their safety has become critically important. A fundamental capability of AD systems is object detection and trajectory forecasting of vehicles and pedestrians around the ego-vehicle, which is essential for preventing [...] Read more.

As the demand for autonomous driving (AD) systems has increased, the enhancement of their safety has become critically important. A fundamental capability of AD systems is object detection and trajectory forecasting of vehicles and pedestrians around the ego-vehicle, which is essential for preventing potential collisions. This study introduces the Deep learning-based Acceleration-aware Trajectory forecasting (DAT) model, a deep learning-based approach for object detection and trajectory forecasting, utilizing raw sensor measurements. DAT is an end-to-end model that processes sequential sensor data to detect objects and forecasts their future trajectories at each time step. The core innovation of DAT lies in its novel forecasting module, which leverages acceleration data to enhance trajectory forecasting, leading to the consideration of a variety of agent motion models. We propose a robust and innovative method for estimating ground-truth acceleration for objects, along with an object detector that predicts acceleration attributes for each detected object and a novel method for trajectory forecasting. DAT is trained and evaluated on the NuScenes dataset, demonstrating its empirical effectiveness through extensive experiments. The results indicate that DAT significantly surpasses state-of-the-art methods, particularly in enhancing forecasting accuracy for objects exhibiting both linear and nonlinear motion patterns, achieving up to a

2 \times

improvement. This advancement highlights the critical role of incorporating acceleration data into predictive models, representing a substantial step forward in the development of safer autonomous driving systems. Full article

(This article belongs to the Topic Computer Vision and Image Processing, 2nd Edition)

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16 pages, 5125 KiB

Open AccessArticle

Multi-Level Feature Fusion in CNN-Based Human Action Recognition: A Case Study on EfficientNet-B7

by Pitiwat Lueangwitchajaroen, Sitapa Watcharapinchai, Worawit Tepsan and Sorn Sooksatra

J. Imaging 2024, 10(12), 320; https://doi.org/10.3390/jimaging10120320 (registering DOI) - 12 Dec 2024

Accurate human action recognition is becoming increasingly important across various fields, including healthcare and self-driving cars. A simple approach to enhance model performance is incorporating additional data modalities, such as depth frames, point clouds, and skeleton information, while previous studies have predominantly used [...] Read more.

Accurate human action recognition is becoming increasingly important across various fields, including healthcare and self-driving cars. A simple approach to enhance model performance is incorporating additional data modalities, such as depth frames, point clouds, and skeleton information, while previous studies have predominantly used late fusion techniques to combine these modalities, our research introduces a multi-level fusion approach that combines information at early, intermediate, and late stages together. Furthermore, recognizing the challenges of collecting multiple data types in real-world applications, our approach seeks to exploit multimodal techniques while relying solely on RGB frames as the single data source. In our work, we used RGB frames from the NTU RGB+D dataset as the sole data source. From these frames, we extracted 2D skeleton coordinates and optical flow frames using pre-trained models. We evaluated our multi-level fusion approach with EfficientNet-B7 as a case study, and our methods demonstrated significant improvement, achieving 91.5% in NTU RGB+D 60 dataset accuracy compared to single-modality and single-view models. Despite their simplicity, our methods are also comparable to other state-of-the-art approaches. Full article

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

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14 pages, 2304 KiB

Open AccessArticle

Improved Generalizability in Medical Computer Vision: Hyperbolic Deep Learning in Multi-Modality Neuroimaging

by Cyrus Ayubcha, Sulaiman Sajed, Chady Omara, Anna B. Veldman, Shashi B. Singh, Yashas Ullas Lokesha, Alex Liu, Mohammad Ali Aziz-Sultan, Timothy R. Smith and Andrew Beam

J. Imaging 2024, 10(12), 319; https://doi.org/10.3390/jimaging10120319 - 12 Dec 2024

Deep learning has shown significant value in automating radiological diagnostics but can be limited by a lack of generalizability to external datasets. Leveraging the geometric principles of non-Euclidean space, certain geometric deep learning approaches may offer an alternative means of improving model generalizability. [...] Read more.

Deep learning has shown significant value in automating radiological diagnostics but can be limited by a lack of generalizability to external datasets. Leveraging the geometric principles of non-Euclidean space, certain geometric deep learning approaches may offer an alternative means of improving model generalizability. This study investigates the potential advantages of hyperbolic convolutional neural networks (HCNNs) over traditional convolutional neural networks (CNNs) in neuroimaging tasks. We conducted a comparative analysis of HCNNs and CNNs across various medical imaging modalities and diseases, with a focus on a compiled multi-modality neuroimaging dataset. The models were assessed for their performance parity, robustness to adversarial attacks, semantic organization of embedding spaces, and generalizability. Zero-shot evaluations were also performed with ischemic stroke non-contrast CT images. HCNNs matched CNNs’ performance in less complex settings and demonstrated superior semantic organization and robustness to adversarial attacks. While HCNNs equaled CNNs in out-of-sample datasets identifying Alzheimer’s disease, in zero-shot evaluations, HCNNs outperformed CNNs and radiologists. HCNNs deliver enhanced robustness and organization in neuroimaging data. This likely underlies why, while HCNNs perform similarly to CNNs with respect to in-sample tasks, they confer improved generalizability. Nevertheless, HCNNs encounter efficiency and performance challenges with larger, complex datasets. These limitations underline the need for further optimization of HCNN architectures. HCNNs present promising improvements in generalizability and resilience for medical imaging applications, particularly in neuroimaging. Despite facing challenges with larger datasets, HCNNs enhance performance under adversarial conditions and offer better semantic organization, suggesting valuable potential in generalizable deep learning models in medical imaging and neuroimaging diagnostics. Full article

(This article belongs to the Special Issue Medical Image Classification and Segmentation: Progress and Challenges)

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22 pages, 15973 KiB

Open AccessArticle

Three-Dimensional Bone-Image Synthesis with Generative Adversarial Networks

by Christoph Angermann, Johannes Bereiter-Payr, Kerstin Stock, Gerald Degenhart and Markus Haltmeier

J. Imaging 2024, 10(12), 318; https://doi.org/10.3390/jimaging10120318 - 11 Dec 2024

Medical image processing has been highlighted as an area where deep-learning-based models have the greatest potential. However, in the medical field, in particular, problems of data availability and privacy are hampering research progress and, thus, rapid implementation in clinical routine. The generation of [...] Read more.

Medical image processing has been highlighted as an area where deep-learning-based models have the greatest potential. However, in the medical field, in particular, problems of data availability and privacy are hampering research progress and, thus, rapid implementation in clinical routine. The generation of synthetic data not only ensures privacy but also allows the drawing of new patients with specific characteristics, enabling the development of data-driven models on a much larger scale. This work demonstrates that three-dimensional generative adversarial networks (GANs) can be efficiently trained to generate high-resolution medical volumes with finely detailed voxel-based architectures. In addition, GAN inversion is successfully implemented for the three-dimensional setting and used for extensive research on model interpretability and applications such as image morphing, attribute editing, and style mixing. The results are comprehensively validated on a database of three-dimensional HR-pQCT instances representing the bone micro-architecture of the distal radius. Full article

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

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22 pages, 3640 KiB

Open AccessArticle

Evaluation of Color Difference Models for Wide Color Gamut and High Dynamic Range

by Olga Basova, Sergey Gladilin, Vladislav Kokhan, Mikhalina Kharkevich, Anastasia Sarycheva, Ivan Konovalenko, Mikhail Chobanu and Ilya Nikolaev

J. Imaging 2024, 10(12), 317; https://doi.org/10.3390/jimaging10120317 - 10 Dec 2024

Color difference models (CDMs) are essential for accurate color reproduction in image processing. While CDMs aim to reflect perceived color differences (CDs) from psychophysical data, they remain largely untested in wide color gamut (WCG) and high dynamic range (HDR) contexts, which are underrepresented [...] Read more.

Color difference models (CDMs) are essential for accurate color reproduction in image processing. While CDMs aim to reflect perceived color differences (CDs) from psychophysical data, they remain largely untested in wide color gamut (WCG) and high dynamic range (HDR) contexts, which are underrepresented in current datasets. This gap highlights the need to validate CDMs across WCG and HDR. Moreover, the non-geodesic structure of perceptual color space necessitates datasets covering CDs of various magnitudes, while most existing datasets emphasize only small and threshold CDs. To address this, we collected a new dataset encompassing a broad range of CDs in WCG and HDR contexts and developed a novel CDM fitted to these data. Benchmarking various CDMs using STRESS and significant error fractions on both new and established datasets reveals that CAM16-UCS with power correction is the most versatile model, delivering strong average performance across WCG colors up to 1611 cd/m². However, even the best CDM fails to achieve the desired accuracy limits and yields significant errors. CAM16-UCS, though promising, requires further refinement, particularly in its power correction component to better capture the non-geodesic structure of perceptual color space. Full article

(This article belongs to the Special Issue Color in Image Processing and Computer Vision)

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11 pages, 1525 KiB

Open AccessArticle

Toward Closing the Loop in Image-to-Image Conversion in Radiotherapy: A Quality Control Tool to Predict Synthetic Computed Tomography Hounsfield Unit Accuracy

by Paolo Zaffino, Ciro Benito Raggio, Adrian Thummerer, Gabriel Guterres Marmitt, Johannes Albertus Langendijk, Anna Procopio, Carlo Cosentino, Joao Seco, Antje Christin Knopf, Stefan Both and Maria Francesca Spadea

J. Imaging 2024, 10(12), 316; https://doi.org/10.3390/jimaging10120316 - 10 Dec 2024

In recent years, synthetic Computed Tomography (CT) images generated from Magnetic Resonance (MR) or Cone Beam Computed Tomography (CBCT) acquisitions have been shown to be comparable to real CT images in terms of dose computation for radiotherapy simulation. However, until now, there has [...] Read more.

In recent years, synthetic Computed Tomography (CT) images generated from Magnetic Resonance (MR) or Cone Beam Computed Tomography (CBCT) acquisitions have been shown to be comparable to real CT images in terms of dose computation for radiotherapy simulation. However, until now, there has been no independent strategy to assess the quality of each synthetic image in the absence of ground truth. In this work, we propose a Deep Learning (DL)-based framework to predict the accuracy of synthetic CT in terms of Mean Absolute Error (MAE) without the need for a ground truth (GT). The proposed algorithm generates a volumetric map as an output, informing clinicians of the predicted MAE slice-by-slice. A cascading multi-model architecture was used to deal with the complexity of the MAE prediction task. The workflow was trained and tested on two cohorts of head and neck cancer patients with different imaging modalities: 27 MR scans and 33 CBCT. The algorithm evaluation revealed an accurate HU prediction (a median absolute prediction deviation equal to 4 HU for CBCT-based synthetic CTs and 6 HU for MR-based synthetic CTs), with discrepancies that do not affect the clinical decisions made on the basis of the proposed estimation. The workflow exhibited no systematic error in MAE prediction. This work represents a proof of concept about the feasibility of synthetic CT evaluation in daily clinical practice, and it paves the way for future patient-specific quality assessment strategies. Full article

(This article belongs to the Special Issue Advances in Biomedical Image Processing and Artificial Intelligence for Computer-Aided Diagnosis in Medicine)

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12 pages, 599 KiB

Open AccessArticle

PAS or Not PAS? The Sonographic Assessment of Placenta Accreta Spectrum Disorders and the Clinical Validation of a New Diagnostic and Prognostic Scoring System

by Antonella Vimercati, Arianna Galante, Margherita Fanelli, Francesca Cirignaco, Amerigo Vitagliano, Pierpaolo Nicolì, Andrea Tinelli, Antonio Malvasi, Miriam Dellino, Gianluca Raffaello Damiani, Barbara Crescenza, Giorgio Maria Baldini, Ettore Cicinelli and Marco Cerbone

J. Imaging 2024, 10(12), 315; https://doi.org/10.3390/jimaging10120315 - 10 Dec 2024

This study aimed to evaluate our center’s experience in diagnosing and managing placenta accreta spectrum (PAS) in a high-risk population, focusing on prenatal ultrasound features associated with PAS severity and maternal outcomes. We conducted a retrospective analysis of 102 high-risk patients with confirmed [...] Read more.

This study aimed to evaluate our center’s experience in diagnosing and managing placenta accreta spectrum (PAS) in a high-risk population, focusing on prenatal ultrasound features associated with PAS severity and maternal outcomes. We conducted a retrospective analysis of 102 high-risk patients with confirmed placenta previa who delivered at our center between 2018 and 2023. Patients underwent transabdominal and transvaginal ultrasound scans, assessing typical sonographic features. Binary and multivariate logistic regression analyses were performed to identify sonographic markers predictive of PAS and relative complications. Key ultrasound features—retroplacental myometrial thinning (<1 mm), vascular lacunae, and retroplacental vascularization—were significantly associated with PAS and a higher risk of surgical complications. An exceedingly rare sign, the “riddled cervix” sign, was observed in only three patients with extensive cervical or parametrial involvement. Those patients had the worst surgical outcomes. This study highlights the utility of specific ultrasound features in stratifying PAS risk and guiding clinical and surgical management in high-risk pregnancies. The findings support integrating these markers into prenatal diagnostic protocols to improve patient outcomes and inform surgical planning. Full article

(This article belongs to the Special Issue Clinical and Pathological Imaging in the Era of Artificial Intelligence: New Insights and Perspectives)

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19 pages, 9164 KiB

Open AccessArticle

A Regularization Method for Landslide Thickness Estimation

by Lisa Borgatti, Davide Donati, Liwei Hu, Germana Landi and Fabiana Zama

J. Imaging 2024, 10(12), 314; https://doi.org/10.3390/jimaging10120314 - 10 Dec 2024

Accurate estimation of landslide depth is essential for practical hazard assessment and risk mitigation. This work addresses the problem of determining landslide depth from satellite-derived elevation data. Using the principle of mass conservation, this problem can be formulated as a linear inverse problem. [...] Read more.

Accurate estimation of landslide depth is essential for practical hazard assessment and risk mitigation. This work addresses the problem of determining landslide depth from satellite-derived elevation data. Using the principle of mass conservation, this problem can be formulated as a linear inverse problem. To solve the inverse problem, we present a regularization approach that computes approximate solutions and regularization parameters using the Balancing Principle. Synthetic data were carefully designed and generated to evaluate the method under controlled conditions, allowing for precise validation of its performance. Through comprehensive testing with this synthetic dataset, we demonstrate the method’s robustness across varying noise levels. When applied to real-world data from the Fels landslide in Alaska, the proposed method proved its practical value in reconstructing landslide thickness patterns. These reconstructions showed good agreement with existing geological interpretations, validating the method’s effectiveness in real-world scenarios. Full article

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15 pages, 627 KiB

Open AccessReview

Real-Time Emotion Recognition for Improving the Teaching–Learning Process: A Scoping Review

by Cèlia Llurba and Ramon Palau

J. Imaging 2024, 10(12), 313; https://doi.org/10.3390/jimaging10120313 - 9 Dec 2024

Emotion recognition (ER) is gaining popularity in various fields, including education. The benefits of ER in the classroom for educational purposes, such as improving students’ academic performance, are gradually becoming known. Thus, real-time ER is proving to be a valuable tool for teachers [...] Read more.

Emotion recognition (ER) is gaining popularity in various fields, including education. The benefits of ER in the classroom for educational purposes, such as improving students’ academic performance, are gradually becoming known. Thus, real-time ER is proving to be a valuable tool for teachers as well as for students. However, its feasibility in educational settings requires further exploration. This review offers learning experiences based on real-time ER with students to explore their potential in learning and in improving their academic achievement. The purpose is to present evidence of good implementation and suggestions for their successful application. The content analysis finds that most of the practices lead to significant improvements in terms of educational purposes. Nevertheless, the analysis identifies problems that might block the implementation of these practices in the classroom and in education; among the obstacles identified are the absence of privacy of the students and the support needs of the students. We conclude that artificial intelligence (AI) and ER are potential tools to approach the needs in ordinary classrooms, although reliable automatic recognition is still a challenge for researchers to achieve the best ER feature in real time, given the high input data variability. Full article

(This article belongs to the Special Issue Deep Learning in Image Analysis: Progress and Challenges)

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7 pages, 1102 KiB

Open AccessCommunication

Quantitative MRI Assessment of Post-Surgical Spinal Cord Injury Through Radiomic Analysis

by Azadeh Sharafi, Andrew P. Klein and Kevin M. Koch

J. Imaging 2024, 10(12), 312; https://doi.org/10.3390/jimaging10120312 - 8 Dec 2024

This study investigates radiomic efficacy in post-surgical traumatic spinal cord injury (SCI), overcoming MRI limitations from metal artifacts to enhance diagnosis, severity assessment, and lesion characterization or prognosis and therapy guidance. Traumatic spinal cord injury (SCI) causes severe neurological deficits. While MRI allows [...] Read more.

This study investigates radiomic efficacy in post-surgical traumatic spinal cord injury (SCI), overcoming MRI limitations from metal artifacts to enhance diagnosis, severity assessment, and lesion characterization or prognosis and therapy guidance. Traumatic spinal cord injury (SCI) causes severe neurological deficits. While MRI allows qualitative injury evaluation, standard imaging alone has limitations for precise SCI diagnosis, severity stratification, and pathology characterization, which are needed to guide prognosis and therapy. Radiomics enables quantitative tissue phenotyping by extracting a high-dimensional set of descriptive texture features from medical images. However, the efficacy of postoperative radiomic quantification in the presence of metal-induced MRI artifacts from spinal instrumentation has yet to be fully explored. A total of 50 healthy controls and 12 SCI patients post-stabilization surgery underwent 3D multi-spectral MRI. Automated spinal cord segmentation was followed by radiomic feature extraction. Supervised machine learning categorized SCI versus controls, injury severity, and lesion location relative to instrumentation. Radiomics differentiated SCI patients (Matthews correlation coefficient (MCC) 0.97; accuracy 1.0), categorized injury severity (MCC: 0.95; ACC: 0.98), and localized lesions (MCC: 0.85; ACC: 0.90). Combined T₁ and T₂ features outperformed individual modalities across tasks with gradient boosting models showing the highest efficacy. The radiomic framework achieved excellent performance, differentiating SCI from controls and accurately categorizing injury severity. The ability to reliably quantify SCI severity and localization could potentially inform diagnosis, prognosis, and guide therapy. Further research is warranted to validate radiomic SCI biomarkers and explore clinical integration. Full article

(This article belongs to the Section Medical Imaging)

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59 pages, 3270 KiB

Open AccessReview

State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues

by Fatma Krikid, Hugo Rositi and Antoine Vacavant

J. Imaging 2024, 10(12), 311; https://doi.org/10.3390/jimaging10120311 - 6 Dec 2024

Microscopic image segmentation (MIS) is a fundamental task in medical imaging and biological research, essential for precise analysis of cellular structures and tissues. Despite its importance, the segmentation process encounters significant challenges, including variability in imaging conditions, complex biological structures, and artefacts (e.g., [...] Read more.

Microscopic image segmentation (MIS) is a fundamental task in medical imaging and biological research, essential for precise analysis of cellular structures and tissues. Despite its importance, the segmentation process encounters significant challenges, including variability in imaging conditions, complex biological structures, and artefacts (e.g., noise), which can compromise the accuracy of traditional methods. The emergence of deep learning (DL) has catalyzed substantial advancements in addressing these issues. This systematic literature review (SLR) provides a comprehensive overview of state-of-the-art DL methods developed over the past six years for the segmentation of microscopic images. We critically analyze key contributions, emphasizing how these methods specifically tackle challenges in cell, nucleus, and tissue segmentation. Additionally, we evaluate the datasets and performance metrics employed in these studies. By synthesizing current advancements and identifying gaps in existing approaches, this review not only highlights the transformative potential of DL in enhancing diagnostic accuracy and research efficiency but also suggests directions for future research. The findings of this study have significant implications for improving methodologies in medical and biological applications, ultimately fostering better patient outcomes and advancing scientific understanding. Full article

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17 pages, 10713 KiB

Open AccessArticle

UV Hyperspectral Imaging with Xenon and Deuterium Light Sources: Integrating PCA and Neural Networks for Analysis of Different Raw Cotton Types

by Mohammad Al Ktash, Mona Knoblich, Max Eberle, Frank Wackenhut and Marc Brecht

J. Imaging 2024, 10(12), 310; https://doi.org/10.3390/jimaging10120310 - 5 Dec 2024

Ultraviolet (UV) hyperspectral imaging shows significant promise for the classification and quality assessment of raw cotton, a key material in the textile industry. This study evaluates the efficacy of UV hyperspectral imaging (225–408 nm) using two different light sources: xenon arc (XBO) and [...] Read more.

Ultraviolet (UV) hyperspectral imaging shows significant promise for the classification and quality assessment of raw cotton, a key material in the textile industry. This study evaluates the efficacy of UV hyperspectral imaging (225–408 nm) using two different light sources: xenon arc (XBO) and deuterium lamps, in comparison to NIR hyperspectral imaging. The aim is to determine which light source provides better differentiation between cotton types in UV hyperspectral imaging, as each interacts differently with the materials, potentially affecting imaging quality and classification accuracy. Principal component analysis (PCA) and Quadratic Discriminant Analysis (QDA) were employed to differentiate between various cotton types and hemp plant. PCA for the XBO illumination revealed that the first three principal components (PCs) accounted for 94.8% of the total variance: PC1 (78.4%) and PC2 (11.6%) clustered the samples into four main groups—hemp (HP), recycled cotton (RcC), and organic cotton (OC) from the other cotton samples—while PC3 (6%) further separated RcC. When using the deuterium light source, the first three PCs explained 89.4% of the variance, effectively distinguishing sample types such as HP, RcC, and OC from the remaining samples, with PC3 clearly separating RcC. When combining the PCA scores with QDA, the classification accuracy reached 76.1% for the XBO light source and 85.1% for the deuterium light source. Furthermore, a deep learning technique called a fully connected neural network for classification was applied. The classification accuracy for the XBO and deuterium light sources reached 83.6% and 90.1%, respectively. The results highlight the ability of this method to differentiate conventional and organic cotton, as well as hemp, and to identify distinct types of recycled cotton, suggesting varying recycling processes and possible common origins with raw cotton. These findings underscore the potential of UV hyperspectral imaging, coupled with chemometric models, as a powerful tool for enhancing cotton classification accuracy in the textile industry. Full article

(This article belongs to the Section Color, Multi-spectral, and Hyperspectral Imaging)

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17 pages, 3796 KiB

Open AccessArticle

FastQAFPN-YOLOv8s-Based Method for Rapid and Lightweight Detection of Walnut Unseparated Material

by Junqiu Li, Jiayi Wang, Dexiao Kong, Qinghui Zhang and Zhenping Qiang

J. Imaging 2024, 10(12), 309; https://doi.org/10.3390/jimaging10120309 - 2 Dec 2024

Walnuts possess significant nutritional and economic value. Fast and accurate sorting of shells and kernels will enhance the efficiency of automated production. Therefore, we propose a FastQAFPN-YOLOv8s object detection network to achieve rapid and precise detection of unsorted materials. The method uses lightweight [...] Read more.

Walnuts possess significant nutritional and economic value. Fast and accurate sorting of shells and kernels will enhance the efficiency of automated production. Therefore, we propose a FastQAFPN-YOLOv8s object detection network to achieve rapid and precise detection of unsorted materials. The method uses lightweight Pconv (Partial Convolution) operators to build the FasterNextBlock structure, which serves as the backbone feature extractor for the Fasternet feature extraction network. The ECIoU loss function, combining EIoU (Efficient-IoU) and CIoU (Complete-IoU), speeds up the adjustment of the prediction frame and the network regression. In the Neck section of the network, the QAFPN feature fusion extraction network is proposed to replace the PAN-FPN (Path Aggregation Network—Feature Pyramid Network) in YOLOv8s with a Rep-PAN structure based on the QARepNext reparameterization framework for feature fusion extraction to strike a balance between network performance and inference speed. To validate the method, we built a three-axis mobile sorting device and created a dataset of 3000 images of walnuts after shell removal for experiments. The results show that the improved network contains 6071008 parameters, a training time of 2.49 h, a model size of 12.3 MB, an mAP (Mean Average Precision) of 94.5%, and a frame rate of 52.1 FPS. Compared with the original model, the number of parameters decreased by 45.5%, with training time reduced by 32.7%, the model size shrunk by 45.3%, and frame rate improved by 40.8%. However, some accuracy is sacrificed due to the lightweight design, resulting in a 1.2% decrease in mAP. The network reduces the model size by 59.7 MB and 23.9 MB compared to YOLOv7 and YOLOv6, respectively, and improves the frame rate by 15.67 fps and 22.55 fps, respectively. The average confidence and mAP show minimal changes compared to YOLOv7 and improved by 4.2% and 2.4% compared to YOLOv6, respectively. The FastQAFPN-YOLOv8s detection method effectively reduces model size while maintaining recognition accuracy. Full article

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

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12 pages, 1486 KiB

Open AccessArticle

Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI

by Dayeong An and El-Sayed Ibrahim

J. Imaging 2024, 10(12), 308; https://doi.org/10.3390/jimaging10120308 - 1 Dec 2024

Radiation therapy (RT) is widely used to treat thoracic cancers but carries a risk of radiation-induced heart disease (RIHD). This study aimed to detect early markers of RIHD using machine learning (ML) techniques and cardiac MRI in a rat model. SS.BN3 consomic rats, [...] Read more.

Radiation therapy (RT) is widely used to treat thoracic cancers but carries a risk of radiation-induced heart disease (RIHD). This study aimed to detect early markers of RIHD using machine learning (ML) techniques and cardiac MRI in a rat model. SS.BN3 consomic rats, which have a more subtle RIHD phenotype compared to Dahl salt-sensitive (SS) rats, were treated with localized cardiac RT or sham at 10 weeks of age. Cardiac MRI was performed 8 and 10 weeks post-treatment to assess global and regional cardiac function. ML algorithms were applied to differentiate sham-treated and irradiated rats based on early changes in myocardial function. Despite normal global left ventricular ejection fraction in both groups, strain analysis showed significant reductions in the anteroseptal and anterolateral segments of irradiated rats. Gradient boosting achieved an F1 score of 0.94 and an ROC value of 0.95, while random forest showed an accuracy of 88%. These findings suggest that ML, combined with cardiac MRI, can effectively detect early preclinical changes in RIHD, particularly alterations in regional myocardial contractility, highlighting the potential of these techniques for early detection and monitoring of radiation-induced cardiac dysfunction. Full article

(This article belongs to the Special Issue Progress and Challenges in Biomedical Image Analysis)

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17 pages, 648 KiB

Open AccessArticle

Temporal Gap-Aware Attention Model for Temporal Action Proposal Generation

by Sorn Sooksatra and Sitapa Watcharapinchai

J. Imaging 2024, 10(12), 307; https://doi.org/10.3390/jimaging10120307 - 29 Nov 2024

Temporal action proposal generation is a method for extracting temporal action instances or proposals from untrimmed videos. Existing methods often struggle to segment contiguous action proposals, which are a group of action boundaries with small temporal gaps. To address this limitation, we propose [...] Read more.

Temporal action proposal generation is a method for extracting temporal action instances or proposals from untrimmed videos. Existing methods often struggle to segment contiguous action proposals, which are a group of action boundaries with small temporal gaps. To address this limitation, we propose incorporating an attention mechanism to weigh the importance of each proposal within a contiguous group. This mechanism leverages the gap displacement between proposals to calculate attention scores, enabling a more accurate localization of action boundaries. We evaluate our method against a state-of-the-art boundary-based baseline on ActivityNet v1.3 and Thumos 2014 datasets. The experimental results demonstrate that our approach significantly improves the performance of short-duration and contiguous action proposals, achieving an average recall of 78.22%. Full article

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39 pages, 3120 KiB

Open AccessArticle

A Comparative Review of the SWEET Simulator: Theoretical Verification Against Other Simulators

by Amine Ben-Daoued, Frédéric Bernardin and Pierre Duthon

J. Imaging 2024, 10(12), 306; https://doi.org/10.3390/jimaging10120306 - 27 Nov 2024

Accurate luminance-based image generation is critical in physically based simulations, as even minor inaccuracies in radiative transfer calculations can introduce noise or artifacts, adversely affecting image quality. The radiative transfer simulator, SWEET, uses a backward Monte Carlo approach, and its performance is analyzed [...] Read more.

Accurate luminance-based image generation is critical in physically based simulations, as even minor inaccuracies in radiative transfer calculations can introduce noise or artifacts, adversely affecting image quality. The radiative transfer simulator, SWEET, uses a backward Monte Carlo approach, and its performance is analyzed alongside other simulators to assess how Monte Carlo-induced biases vary with parameters like optical thickness and medium anisotropy. This work details the advancements made to SWEET since the previous publication, with a specific focus on a more comprehensive comparison with other simulators such as Mitsuba. The core objective is to evaluate the precision of SWEET by comparing radiometric quantities like luminance, which serves as a method for validating the simulator. This analysis is particularly important in contexts such as automotive camera imaging, where accurate scene representation is crucial to reducing noise and ensuring the reliability of image-based systems in autonomous driving. By focusing on detailed radiometric comparisons, this study underscores SWEET’s ability to minimize noise, thus providing high-quality imaging for advanced applications. Full article

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16 pages, 20362 KiB

Open AccessArticle

IngredSAM: Open-World Food Ingredient Segmentation via a Single Image Prompt

by Leyi Chen, Bowen Wang and Jiaxin Zhang

J. Imaging 2024, 10(12), 305; https://doi.org/10.3390/jimaging10120305 - 26 Nov 2024

Food semantic segmentation is of great significance in the field of computer vision and artificial intelligence, especially in the application of food image analysis. Due to the complexity and variety of food, it is difficult to effectively handle this task using supervised methods. [...] Read more.

Food semantic segmentation is of great significance in the field of computer vision and artificial intelligence, especially in the application of food image analysis. Due to the complexity and variety of food, it is difficult to effectively handle this task using supervised methods. Thus, we introduce IngredSAM, a novel approach for open-world food ingredient semantic segmentation, extending the capabilities of the Segment Anything Model (SAM). Utilizing visual foundation models (VFMs) and prompt engineering, IngredSAM leverages discriminative and matchable semantic features between a single clean image prompt of specific ingredients and open-world images to guide the generation of accurate segmentation masks in real-world scenarios. This method addresses the challenges of traditional supervised models in dealing with the diverse appearances and class imbalances of food ingredients. Our framework demonstrates significant advancements in the segmentation of food ingredients without any training process, achieving 2.85% and 6.01% better performance than previous state-of-the-art methods on both FoodSeg103 and UECFoodPix datasets. IngredSAM exemplifies a successful application of one-shot, open-world segmentation, paving the way for downstream applications such as enhancements in nutritional analysis and consumer dietary trend monitoring. Full article

(This article belongs to the Section AI in Imaging)

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19 pages, 785 KiB

Open AccessArticle

Transformer Dil-DenseUnet: An Advanced Architecture for Stroke Segmentation

by Nesrine Jazzar, Besma Mabrouk and Ali Douik

J. Imaging 2024, 10(12), 304; https://doi.org/10.3390/jimaging10120304 - 25 Nov 2024

We propose a novel architecture, Transformer Dil-DenseUNet, designed to address the challenges of accurately segmenting stroke lesions in MRI images. Precise segmentation is essential for diagnosing and treating stroke patients, as it provides critical spatial insights into the affected brain regions and the [...] Read more.

We propose a novel architecture, Transformer Dil-DenseUNet, designed to address the challenges of accurately segmenting stroke lesions in MRI images. Precise segmentation is essential for diagnosing and treating stroke patients, as it provides critical spatial insights into the affected brain regions and the extent of damage. Traditional manual segmentation is labor-intensive and error-prone, highlighting the need for automated solutions. Our Transformer Dil-DenseUNet combines DenseNet, dilated convolutions, and Transformer blocks, each contributing unique strengths to enhance segmentation accuracy. The DenseNet component captures fine-grained details and global features by leveraging dense connections, improving both precision and feature reuse. The dilated convolutional blocks, placed before each DenseNet module, expand the receptive field, capturing broader contextual information essential for accurate segmentation. Additionally, the Transformer blocks within our architecture address CNN limitations in capturing long-range dependencies by modeling complex spatial relationships through multi-head self-attention mechanisms. We assess our model’s performance on the Ischemic Stroke Lesion Segmentation Challenge 2015 (SISS 2015) and ISLES 2022 datasets. In the testing phase, the model achieves a Dice coefficient of 0.80 ± 0.30 on SISS 2015 and 0.81 ± 0.33 on ISLES 2022, surpassing the current state-of-the-art results on these datasets. Full article

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

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21 pages, 11261 KiB

Open AccessArticle

Enhanced YOLOv8 Ship Detection Empower Unmanned Surface Vehicles for Advanced Maritime Surveillance

by Abdelilah Haijoub, Anas Hatim, Antonio Guerrero-Gonzalez, Mounir Arioua and Khalid Chougdali

J. Imaging 2024, 10(12), 303; https://doi.org/10.3390/jimaging10120303 - 24 Nov 2024

The evolution of maritime surveillance is significantly marked by the incorporation of Artificial Intelligence and machine learning into Unmanned Surface Vehicles (USVs). This paper presents an AI approach for detecting and tracking unmanned surface vehicles, specifically leveraging an enhanced version of YOLOv8, fine-tuned [...] Read more.

The evolution of maritime surveillance is significantly marked by the incorporation of Artificial Intelligence and machine learning into Unmanned Surface Vehicles (USVs). This paper presents an AI approach for detecting and tracking unmanned surface vehicles, specifically leveraging an enhanced version of YOLOv8, fine-tuned for maritime surveillance needs. Deployed on the NVIDIA Jetson TX2 platform, the system features an innovative architecture and perception module optimized for real-time operations and energy efficiency. Demonstrating superior detection accuracy with a mean Average Precision (mAP) of 0.99 and achieving an operational speed of 17.99 FPS, all while maintaining energy consumption at just 5.61 joules. The remarkable balance between accuracy, processing speed, and energy efficiency underscores the potential of this system to significantly advance maritime safety, security, and environmental monitoring. Full article

(This article belongs to the Section Visualization and Computer Graphics)

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