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10 pages, 4728 KiB

Open AccessCommunication

High-Resolution Iodine-Enhanced Micro-Computed Tomography of Intact Human Hearts for Detailed Coronary Microvasculature Analyses

by Joerg Reifart and Paul Iaizzo

J. Imaging 2024, 10(7), 173; https://doi.org/10.3390/jimaging10070173 - 18 Jul 2024

Viewed by 737

Abstract

Identifying the detailed anatomies of the coronary microvasculature remains an area of research; one needs to develop methods for non-destructive, high-resolution, three-dimensional imaging of these vessels for computational modeling. Currently employed Micro-Computed Tomography (Micro-CT) protocols for vasa vasorum analyses require organ dissection and, [...] Read more.

Identifying the detailed anatomies of the coronary microvasculature remains an area of research; one needs to develop methods for non-destructive, high-resolution, three-dimensional imaging of these vessels for computational modeling. Currently employed Micro-Computed Tomography (Micro-CT) protocols for vasa vasorum analyses require organ dissection and, in most cases, non-clearable contrast agents. Here, we describe a method developed for a non-destructive, economical means to achieve high-resolution images of the human coronary microvasculature without organ dissection. Formalin-fixed human hearts were cannulated using venogram balloon catheters, which were then fixed into the specimen’s aortic root. The canulated hearts, protected by a polyethylene bag, were placed in radiolucent containers filled with insulating polyurethane foam to reduce movement. For vasculature staining, iodine potassium iodide (IKI, Lugol’s solution; 6.3% Potassium Iodide, 4.1% Iodide) was injected. Contrast distributions were monitored using a North Star Imaging X3000 micro-CT scanner with low-radiation settings, followed by high-radiation scanning (3600 rad, 60 kV, 900 mA) for the final high-resolution imaging. We successfully imaged four intact human hearts presenting with chronic total coronary occlusions of the right coronary artery. This imaging enabled detailed analyses of the vasa vasorum surrounding stenosed and occluded segments. After imaging, the hearts were cleared of iodine and excess polyurethane foam and returned to their initial formalin-fixed state for indefinite storage. Conclusions: the described methodologies allow for the non-destructive, high-resolution micro-CT imaging of coronary microvasculature in intact human hearts, paving the way for detailed computational 3D microvascular reconstructions with a macrovascular context. Full article

(This article belongs to the Section Medical Imaging)

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18 pages, 2231 KiB

Open AccessArticle

Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations

by Serban Vădineanu, Daniël M. Pelt, Oleh Dzyubachyk and Kees Joost Batenburg

J. Imaging 2024, 10(7), 172; https://doi.org/10.3390/jimaging10070172 - 17 Jul 2024

Cited by 1 | Viewed by 724

Abstract

Deep-learning algorithms for cell segmentation typically require large data sets with high-quality annotations to be trained with. However, the annotation cost for obtaining such sets may prove to be prohibitively expensive. Our work aims to reduce the time necessary to create high-quality annotations [...] Read more.

Deep-learning algorithms for cell segmentation typically require large data sets with high-quality annotations to be trained with. However, the annotation cost for obtaining such sets may prove to be prohibitively expensive. Our work aims to reduce the time necessary to create high-quality annotations of cell images by using a relatively small well-annotated data set for training a convolutional neural network to upgrade lower-quality annotations, produced at lower annotation costs. We investigate the performance of our solution when upgrading the annotation quality for labels affected by three types of annotation error: omission, inclusion, and bias. We observe that our method can upgrade annotations affected by high error levels from 0.3 to 0.9 Dice similarity with the ground-truth annotations. We also show that a relatively small well-annotated set enlarged with samples with upgraded annotations can be used to train better-performing cell segmentation networks compared to training only on the well-annotated set. Moreover, we present a use case where our solution can be successfully employed to increase the quality of the predictions of a segmentation network trained on just 10 annotated samples. Full article

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

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20 pages, 3178 KiB

Open AccessArticle

Deep Efficient Data Association for Multi-Object Tracking: Augmented with SSIM-Based Ambiguity Elimination

by Aswathy Prasannakumar and Deepak Mishra

J. Imaging 2024, 10(7), 171; https://doi.org/10.3390/jimaging10070171 - 16 Jul 2024

Viewed by 698

Abstract

Recently, to address the multiple object tracking (MOT) problem, we harnessed the power of deep learning-based methods. The tracking-by-detection approach to multiple object tracking (MOT) involves two primary steps: object detection and data association. In the first step, objects of interest are detected [...] Read more.

Recently, to address the multiple object tracking (MOT) problem, we harnessed the power of deep learning-based methods. The tracking-by-detection approach to multiple object tracking (MOT) involves two primary steps: object detection and data association. In the first step, objects of interest are detected in each frame of a video. The second step establishes the correspondence between these detected objects across different frames to track their trajectories. This paper proposes an efficient and unified data association method that utilizes a deep feature association network (deepFAN) to learn the associations. Additionally, the Structural Similarity Index Metric (SSIM) is employed to address uncertainties in the data association, complementing the deep feature association network. These combined association computations effectively link the current detections with the previous tracks, enhancing the overall tracking performance. To evaluate the efficiency of the proposed MOT framework, we conducted a comprehensive analysis of the popular MOT datasets, such as the MOT challenge and UA-DETRAC. The results showed that our technique performed substantially better than the current state-of-the-art methods in terms of standard MOT metrics. Full article

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

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13 pages, 789 KiB

Open AccessArticle

Noninvasive Quantification of Glucose Metabolism in Mice Myocardium Using the Spline Reconstruction Technique

by Alexandros Vrachliotis, Anastasios Gaitanis, Nicholas E. Protonotarios, George A. Kastis and Lena Costaridou

J. Imaging 2024, 10(7), 170; https://doi.org/10.3390/jimaging10070170 - 16 Jul 2024

Viewed by 831

Abstract

The spline reconstruction technique (SRT) is a fast algorithm based on a novel numerical implementation of an analytic representation of the inverse Radon transform. The purpose of this study was to compare the SRT, filtered back-projection (FBP), and the Tera-Tomo 3D algorithm for [...] Read more.

The spline reconstruction technique (SRT) is a fast algorithm based on a novel numerical implementation of an analytic representation of the inverse Radon transform. The purpose of this study was to compare the SRT, filtered back-projection (FBP), and the Tera-Tomo 3D algorithm for various iteration numbers, using small-animal dynamic PET data obtained from a Mediso nanoScan^® PET/CT scanner. For this purpose, Patlak graphical kinetic analysis was employed to noninvasively quantify the myocardial metabolic rate of glucose (

M R G l u

) in seven male C57BL/6 mice (

n = 7

). All analytic reconstructions were performed via software for tomographic image reconstruction. The analysis of all PET-reconstructed images was conducted with PMOD software (version 3.506, PMOD Technologies LLC, Fällanden, Switzerland) using the inferior vena cava as the image-derived input function. Statistical significance was determined by employing the one-way analysis of variance test. The results revealed that the differences between the values of

M R G l u

obtained via SRT versus FBP, and the variants of he Tera-Tomo 3D algorithm were not statistically significant (p > 0.05). Overall, the SRT appears to perform similarly to the other algorithms investigated, providing a valid alternative analytic method for preclinical dynamic PET studies. Full article

(This article belongs to the Special Issue SPECT and PET Imaging of Small Animals Volume 2nd Edition)

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11 pages, 3199 KiB

Open AccessCommunication

Accurate Determination of Camera Quantum Efficiency from a Single Image

by Yuri Rzhanov

J. Imaging 2024, 10(7), 169; https://doi.org/10.3390/jimaging10070169 - 16 Jul 2024

Viewed by 788

Abstract

Knowledge of spectral sensitivity is important for high-precision comparison of images taken by different cameras and recognition of objects and interpretation of scenes for which color is an important cue. Direct estimation of quantum efficiency curves (QECs) is a complicated and tedious process [...] Read more.

Knowledge of spectral sensitivity is important for high-precision comparison of images taken by different cameras and recognition of objects and interpretation of scenes for which color is an important cue. Direct estimation of quantum efficiency curves (QECs) is a complicated and tedious process requiring specialized equipment, and many camera manufacturers do not make spectral characteristics publicly available. This has led to the development of indirect techniques that are unreliable due to being highly sensitive to noise in the input data, and which often require the imposition of additional ad hoc conditions, some of which do not always hold. We demonstrate the reason for the lack of stability in the determination of QECs and propose an approach that guarantees the stability of QEC reconstruction, even in the presence of noise. A device for the realization of this approach is also proposed. The reported results were used as a basis for the granted US patent. Full article

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

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31 pages, 5788 KiB

Open AccessArticle

Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images

by Nasr Y. Gharaibeh, Roberto De Fazio, Bassam Al-Naami, Abdel-Razzak Al-Hinnawi and Paolo Visconti

J. Imaging 2024, 10(7), 168; https://doi.org/10.3390/jimaging10070168 - 15 Jul 2024

Viewed by 972

Abstract

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and [...] Read more.

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature. 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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14 pages, 4700 KiB

Open AccessArticle

Few-Shot Conditional Learning: Automatic and Reliable Device Classification for Medical Test Equipment

by Eva Pachetti, Giulio Del Corso, Serena Bardelli and Sara Colantonio

J. Imaging 2024, 10(7), 167; https://doi.org/10.3390/jimaging10070167 - 13 Jul 2024

Viewed by 584

Abstract

The limited availability of specialized image databases (particularly in hospitals, where tools vary between providers) makes it difficult to train deep learning models. This paper presents a few-shot learning methodology that uses a pre-trained ResNet integrated with an encoder as a backbone to [...] Read more.

The limited availability of specialized image databases (particularly in hospitals, where tools vary between providers) makes it difficult to train deep learning models. This paper presents a few-shot learning methodology that uses a pre-trained ResNet integrated with an encoder as a backbone to encode conditional shape information for the classification of neonatal resuscitation equipment from less than 100 natural images. The model is also strengthened by incorporating a reliability score, which enriches the prediction with an estimation of classification reliability. The model, whose performance is cross-validated, reached a median accuracy performance of over 99% (and a lower limit of 73.4% for the least accurate model/fold) using only 87 meta-training images. During the test phase on complex natural images, performance was slightly degraded due to a sub-optimal segmentation strategy (FastSAM) required to maintain the real-time inference phase (median accuracy 87.25%). This methodology proves to be excellent for applying complex classification models to contexts (such as neonatal resuscitation) that are not available in public databases. Improvements to the automatic segmentation strategy prior to the extraction of conditional information will allow a natural application in simulation and hospital settings. Full article

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

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16 pages, 3448 KiB

Open AccessArticle

Development and Validation of Four Different Methods to Improve MRI-CEST Tumor pH Mapping in Presence of Fat

by Francesco Gammaraccio, Daisy Villano, Pietro Irrera, Annasofia A. Anemone, Antonella Carella, Alessia Corrado and Dario Livio Longo

J. Imaging 2024, 10(7), 166; https://doi.org/10.3390/jimaging10070166 - 12 Jul 2024

Viewed by 656

Abstract

CEST-MRI is an emerging imaging technique suitable for various in vivo applications, including the quantification of tumor acidosis. Traditionally, CEST contrast is calculated by asymmetry analysis, but the presence of fat signals leads to wrong contrast quantification and hence to inaccurate pH measurements. [...] Read more.

CEST-MRI is an emerging imaging technique suitable for various in vivo applications, including the quantification of tumor acidosis. Traditionally, CEST contrast is calculated by asymmetry analysis, but the presence of fat signals leads to wrong contrast quantification and hence to inaccurate pH measurements. In this study, we investigated four post-processing approaches to overcome fat signal influences and enable correct CEST contrast calculations and tumor pH measurements using iopamidol. The proposed methods involve replacing the Z-spectrum region affected by fat peaks by (i) using a linear interpolation of the fat frequencies, (ii) applying water pool Lorentzian fitting, (iii) considering only the positive part of the Z-spectrum, or (iv) calculating a correction factor for the ratiometric value. In vitro and in vivo studies demonstrated the possibility of using these approaches to calculate CEST contrast and then to measure tumor pH, even in the presence of moderate to high fat fraction values. However, only the method based on the water pool Lorentzian fitting produced highly accurate results in terms of pH measurement in tumor-bearing mice with low and high fat contents. Full article

(This article belongs to the Section Medical Imaging)

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19 pages, 28330 KiB

Open AccessArticle

Development of a Method for Commercial Style Transfer of Historical Architectural Facades Based on Stable Diffusion Models

by Jiaxin Zhang, Yiying Huang, Zhixin Li, Yunqin Li, Zhilin Yu and Mingfei Li

J. Imaging 2024, 10(7), 165; https://doi.org/10.3390/jimaging10070165 - 11 Jul 2024

Viewed by 941

Abstract

In the sphere of urban renewal of historic districts, preserving and innovatively reinterpreting traditional architectural styles remains a primary research focus. However, the modernization and adaptive reuse of traditional buildings often necessitate changes in their functionality. To cater to the demands of tourism [...] Read more.

In the sphere of urban renewal of historic districts, preserving and innovatively reinterpreting traditional architectural styles remains a primary research focus. However, the modernization and adaptive reuse of traditional buildings often necessitate changes in their functionality. To cater to the demands of tourism in historic districts, many traditional residential buildings require conversion to commercial use, resulting in a mismatch between their external form and their internal function. This study explored an automated approach to transform traditional residences into commercially viable designs, offering an efficient and scalable solution for the modernization of historic architecture. We developed a methodology based on diffusion models, focusing on a dataset of nighttime shopfront facades. By training a low-rank adaptation (LoRA) model and integrating the ControlNet model, we enhanced the accuracy and stability of the generated images. The methodology’s performance was validated through qualitative and quantitative assessments, optimizing the batch size, repetition, and learning rate configurations. These evaluations confirmed the method’s effectiveness. Our findings significantly advance the modern commercial style transformation of historical architectural facades, providing a novel solution that maintains the aesthetic and functional integrity, thereby fostering breakthroughs in traditional design thinking and exploring new possibilities for the preservation and commercial adaptation of historical buildings. Full article

(This article belongs to the Section AI in Imaging)

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15 pages, 3416 KiB

Open AccessArticle

UIDF-Net: Unsupervised Image Dehazing and Fusion Utilizing GAN and Encoder–Decoder

by Anxin Zhao, Liang Li and Shuai Liu

J. Imaging 2024, 10(7), 164; https://doi.org/10.3390/jimaging10070164 - 11 Jul 2024

Viewed by 1377

Abstract

Haze weather deteriorates image quality, causing images to become blurry with reduced contrast. This makes object edges and features unclear, leading to lower detection accuracy and reliability. To enhance haze removal effectiveness, we propose an image dehazing and fusion network based on the [...] Read more.

Haze weather deteriorates image quality, causing images to become blurry with reduced contrast. This makes object edges and features unclear, leading to lower detection accuracy and reliability. To enhance haze removal effectiveness, we propose an image dehazing and fusion network based on the encoder–decoder paradigm (UIDF-Net). This network leverages the Image Fusion Module (MDL-IFM) to fuse the features of dehazed images, producing clearer results. Additionally, to better extract haze information, we introduce a haze encoder (Mist-Encode) that effectively processes different frequency features of images, improving the model’s performance in image dehazing tasks. Experimental results demonstrate that the proposed model achieves superior dehazing performance compared to existing algorithms on outdoor datasets. Full article

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13 pages, 2352 KiB

Open AccessArticle

An Arduino-Powered Device for the Study of White Perception beyond the Visual Chromatic Critical Flicker Fusion Frequency

by Francisco J. Ávila

J. Imaging 2024, 10(7), 163; https://doi.org/10.3390/jimaging10070163 - 10 Jul 2024

Viewed by 899

Abstract

Arduino microcontrollers are used for a wide range of technological and biomedical applications, such as image classification, computer vision, brain–computer interaction and vision experiments. Here, we present a new cost-effective mini-device based on RGB LED flicker stimulation for the assessment of the chromatic [...] Read more.

Arduino microcontrollers are used for a wide range of technological and biomedical applications, such as image classification, computer vision, brain–computer interaction and vision experiments. Here, we present a new cost-effective mini-device based on RGB LED flicker stimulation for the assessment of the chromatic temporal resolution of the visual function based on the concept of critical flicker fusion frequency (CFF). The assembly of the device and its testing in thirty young subjects demonstrate the steady white visual perception of a trichromatic flicker stimulus (mixture of red, green and blue stimuli) beyond the CFF. Macular function as measured by photo-stress recovery time (PRT) was found to be independent of the CFF measurements for red, green and blue lights. However, a statistical correlation was found between the contrast modulation for CFF for red and green stimuli and PRT. Finally, wavefront measurements demonstrate that high-order aberrations improve the temporal resolution of the visual function. Full article

(This article belongs to the Special Issue Human Attention and Visual Cognition (2nd Edition))

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12 pages, 3830 KiB

Open AccessArticle

Comparative Evaluation of Convolutional Neural Network Object Detection Algorithms for Vehicle Detection

by Saieshan Reddy, Nelendran Pillay and Navin Singh

J. Imaging 2024, 10(7), 162; https://doi.org/10.3390/jimaging10070162 - 5 Jul 2024

Viewed by 762

Abstract

The domain of object detection was revolutionized with the introduction of Convolutional Neural Networks (CNNs) in the field of computer vision. This article aims to explore the architectural intricacies, methodological differences, and performance characteristics of three CNN-based object detection algorithms, namely Faster Region-Based [...] Read more.

The domain of object detection was revolutionized with the introduction of Convolutional Neural Networks (CNNs) in the field of computer vision. This article aims to explore the architectural intricacies, methodological differences, and performance characteristics of three CNN-based object detection algorithms, namely Faster Region-Based Convolutional Network (R-CNN), You Only Look Once v3 (YOLO), and Single Shot MultiBox Detector (SSD) in the specific domain application of vehicle detection. The findings of this study indicate that the SSD object detection algorithm outperforms the other approaches in terms of both performance and processing speeds. The Faster R-CNN approach detected objects in images with an average speed of 5.1 s, achieving a mean average precision of 0.76 and an average loss of 0.467. YOLO v3 detected objects with an average speed of 1.16 s, achieving a mean average precision of 0.81 with an average loss of 1.183. In contrast, SSD detected objects with an average speed of 0.5 s, exhibiting the highest mean average precision of 0.92 despite having a higher average loss of 2.625. Notably, all three object detectors achieved an accuracy exceeding 99%. Full article

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

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15 pages, 3271 KiB

Open AccessArticle

A 2.5D Self-Training Strategy for Carotid Artery Segmentation in T1-Weighted Brain Magnetic Resonance Images

by Adriel Silva de Araújo, Márcio Sarroglia Pinho, Ana Maria Marques da Silva, Luis Felipe Fiorentini and Jefferson Becker

J. Imaging 2024, 10(7), 161; https://doi.org/10.3390/jimaging10070161 - 3 Jul 2024

Viewed by 747

Abstract

Precise annotations for large medical image datasets can be time-consuming. Additionally, when dealing with volumetric regions of interest, it is typical to apply segmentation techniques on 2D slices, compromising important information for accurately segmenting 3D structures. This study presents a deep learning pipeline [...] Read more.

Precise annotations for large medical image datasets can be time-consuming. Additionally, when dealing with volumetric regions of interest, it is typical to apply segmentation techniques on 2D slices, compromising important information for accurately segmenting 3D structures. This study presents a deep learning pipeline that simultaneously tackles both challenges. Firstly, to streamline the annotation process, we employ a semi-automatic segmentation approach using bounding boxes as masks, which is less time-consuming than pixel-level delineation. Subsequently, recursive self-training is utilized to enhance annotation quality. Finally, a 2.5D segmentation technique is adopted, wherein a slice of a volumetric image is segmented using a pseudo-RGB image. The pipeline was applied to segment the carotid artery tree in T1-weighted brain magnetic resonance images. Utilizing 42 volumetric non-contrast T1-weighted brain scans from four datasets, we delineated bounding boxes around the carotid arteries in the axial slices. Pseudo-RGB images were generated from these slices, and recursive segmentation was conducted using a Res-Unet-based neural network architecture. The model’s performance was tested on a separate dataset, with ground truth annotations provided by a radiologist. After recursive training, we achieved an Intersection over Union (IoU) score of (0.68 ± 0.08) on the unseen dataset, demonstrating commendable qualitative results. Full article

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

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15 pages, 1734 KiB

Open AccessArticle

Hybrid Ensemble Deep Learning Model for Advancing Ischemic Brain Stroke Detection and Classification in Clinical Application

by Radwan Qasrawi, Ibrahem Qdaih, Omar Daraghmeh, Suliman Thwib, Stephanny Vicuna Polo, Siham Atari and Diala Abu Al-Halawa

J. Imaging 2024, 10(7), 160; https://doi.org/10.3390/jimaging10070160 - 2 Jul 2024

Viewed by 1186

Abstract

Ischemic brain strokes are severe medical conditions that occur due to blockages in the brain’s blood flow, often caused by blood clots or artery blockages. Early detection is crucial for effective treatment. This study aims to improve the detection and classification of ischemic [...] Read more.

Ischemic brain strokes are severe medical conditions that occur due to blockages in the brain’s blood flow, often caused by blood clots or artery blockages. Early detection is crucial for effective treatment. This study aims to improve the detection and classification of ischemic brain strokes in clinical settings by introducing a new approach that integrates the stroke precision enhancement, ensemble deep learning, and intelligent lesion detection and segmentation models. The proposed hybrid model was trained and tested using a dataset of 10,000 computed tomography scans. A 25-fold cross-validation technique was employed, while the model’s performance was evaluated using accuracy, precision, recall, and F1 score. The findings indicate significant improvements in accuracy for different stages of stroke images when enhanced using the SPEM model with contrast-limited adaptive histogram equalization set to 4. Specifically, accuracy showed significant improvement (from 0.876 to 0.933) for hyper-acute stroke images; from 0.881 to 0.948 for acute stroke images, from 0.927 to 0.974 for sub-acute stroke images, and from 0.928 to 0.982 for chronic stroke images. Thus, the study shows significant promise for the detection and classification of ischemic brain strokes. Further research is needed to validate its performance on larger datasets and enhance its integration into clinical settings. Full article

(This article belongs to the Section AI in Imaging)

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18 pages, 32013 KiB

Open AccessArticle

Imaging Based Techniques Combined with Color Measurements for the Enhancement of Medieval Wall Paintings in the Framework of EHEM Project

by Paola Pogliani, Claudia Pelosi, Luca Lanteri and Giulia Bordi

J. Imaging 2024, 10(7), 159; https://doi.org/10.3390/jimaging10070159 - 29 Jun 2024

Viewed by 909

Abstract

(1) Background: This paper illustrates an innovative methodological approach chosen to study and map the colors of the medieval wall painting of Santa Maria Antiqua in the Roman Forum, one of the pilot sites of the EHEM project (Enhancement of Heritage Experiences: The [...] Read more.

(1) Background: This paper illustrates an innovative methodological approach chosen to study and map the colors of the medieval wall painting of Santa Maria Antiqua in the Roman Forum, one of the pilot sites of the EHEM project (Enhancement of Heritage Experiences: The Middle Ages). Digital Layered Models of Architecture and Mural Paintings over Time). (2) Methods: Two methods were employed to gather information about colors and mapping. Specifically, colorimetry was utilized for spot measurements, and hypercolorimetric multispectral imaging (HMI) was employed to map the same colors sampled through colorimetry. (3) Results: Chromatic data for all colors in the wall paintings were obtained in the CIELAB color space. Additionally, chromatic similarity maps were generated using the innovative HMI system, a multispectral imaging technique capable of obtaining color data information through advanced calibration software named SpectraPick^® (Version 1.1). This comprehensive approach facilitates a thorough understanding of color characteristics and distribution. (4) Conclusions: The color measurements and mapping represent significant advancements in the interpretation of medieval wall paintings, which are often fragmentary and stratigraphically complex. This research sheds new light on the colors used and enhances our understanding of the original appearance of the iconographic patterns. Furthermore, it enables the reconstruction of colors that closely resemble the originals. Full article

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

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27 pages, 4105 KiB

Open AccessArticle

Pollen Grain Classification Using Some Convolutional Neural Network Architectures

by Benjamin Garga, Hamadjam Abboubakar, Rodrigue Saoungoumi Sourpele, David Libouga Li Gwet and Laurent Bitjoka

J. Imaging 2024, 10(7), 158; https://doi.org/10.3390/jimaging10070158 - 28 Jun 2024

Viewed by 610

Abstract

The main objective of this work is to use convolutional neural networks (CNN) to improve the performance in previous works on their baseline for pollen grain classification, by improving the performance of the following eight popular architectures: InceptionV3, VGG16, VGG19, ResNet50, NASNet, Xception, [...] Read more.

The main objective of this work is to use convolutional neural networks (CNN) to improve the performance in previous works on their baseline for pollen grain classification, by improving the performance of the following eight popular architectures: InceptionV3, VGG16, VGG19, ResNet50, NASNet, Xception, DenseNet201 and InceptionResNetV2, which are benchmarks on several classification tasks, like on the ImageNet dataset. We use a well-known annotated public image dataset for the Brazilian savanna, called POLLEN73S, composed of 2523 images. Holdout cross-validation is the name of the method used in this work. The experiments carried out showed that DenseNet201 and ResNet50 outperform the other CNNs tested, achieving results of 97.217% and 94.257%, respectively, in terms of accuracy, higher than the existing results, with a difference of 1.517% and 0.257%, respectively. VGG19 is the architecture with the lowest performance, achieving a result of 89.463%. Full article

(This article belongs to the Topic Applications in Image Analysis and Pattern Recognition)

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21 pages, 86652 KiB

Open AccessArticle

Toward Unbiased High-Quality Portraits through Latent-Space Evaluation

by Doaa Almhaithawi, Alessandro Bellini and Tania Cerquitelli

J. Imaging 2024, 10(7), 157; https://doi.org/10.3390/jimaging10070157 - 28 Jun 2024

Viewed by 787

Abstract

Images, texts, voices, and signals can be synthesized by latent spaces in a multidimensional vector, which can be explored without the hurdles of noise or other interfering factors. In this paper, we present a practical use case that demonstrates the power of latent [...] Read more.

Images, texts, voices, and signals can be synthesized by latent spaces in a multidimensional vector, which can be explored without the hurdles of noise or other interfering factors. In this paper, we present a practical use case that demonstrates the power of latent space in exploring complex realities such as image space. We focus on DaVinciFace, an AI-based system that explores the StyleGAN2 space to create a high-quality portrait for anyone in the style of the Renaissance genius Leonardo da Vinci. The user enters one of their portraits and receives the corresponding Da Vinci-style portrait as an output. Since most of Da Vinci’s artworks depict young and beautiful women (e.g., “La Belle Ferroniere”, “Beatrice de’ Benci”), we investigate the ability of DaVinciFace to account for other social categorizations, including gender, race, and age. The experimental results evaluate the effectiveness of our methodology on 1158 portraits acting on the vector representations of the latent space to produce high-quality portraits that retain the facial features of the subject’s social categories, and conclude that sparser vectors have a greater effect on these features. To objectively evaluate and quantify our results, we solicited human feedback via a crowd-sourcing campaign. Analysis of the human feedback showed a high tolerance for the loss of important identity features in the resulting portraits when the Da Vinci style is more pronounced, with some exceptions, including Africanized individuals. Full article

(This article belongs to the Topic Applications in Image Analysis and Pattern Recognition)

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21 pages, 11701 KiB

Open AccessArticle

GOYA: Leveraging Generative Art for Content-Style Disentanglement

by Yankun Wu, Yuta Nakashima and Noa Garcia

J. Imaging 2024, 10(7), 156; https://doi.org/10.3390/jimaging10070156 - 26 Jun 2024

Viewed by 1117

Abstract

The content-style duality is a fundamental element in art. These two dimensions can be easily differentiated by humans: content refers to the objects and concepts in an artwork, and style to the way it looks. Yet, we have not found a way to [...] Read more.

The content-style duality is a fundamental element in art. These two dimensions can be easily differentiated by humans: content refers to the objects and concepts in an artwork, and style to the way it looks. Yet, we have not found a way to fully capture this duality with visual representations. While style transfer captures the visual appearance of a single artwork, it fails to generalize to larger sets. Similarly, supervised classification-based methods are impractical since the perception of style lies on a spectrum and not on categorical labels. We thus present GOYA, which captures the artistic knowledge of a cutting-edge generative model for disentangling content and style in art. Experiments show that GOYA explicitly learns to represent the two artistic dimensions (content and style) of the original artistic image, paving the way for leveraging generative models in art analysis. Full article

(This article belongs to the Special Issue Methodologies and Applications of Image Understanding in Cultural and Artistic Heritage)

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20 pages, 2401 KiB

Open AccessArticle

Impact of Color Space and Color Resolution on Vehicle Recognition Models

by Sally Ghanem and John H. Holliman II

J. Imaging 2024, 10(7), 155; https://doi.org/10.3390/jimaging10070155 - 26 Jun 2024

Viewed by 971

Abstract

In this study, we analyze both linear and nonlinear color mappings by training on versions of a curated dataset collected in a controlled campus environment. We experiment with color space and color resolution to assess model performance in vehicle recognition tasks. Color encodings [...] Read more.

In this study, we analyze both linear and nonlinear color mappings by training on versions of a curated dataset collected in a controlled campus environment. We experiment with color space and color resolution to assess model performance in vehicle recognition tasks. Color encodings can be designed in principle to highlight certain vehicle characteristics or compensate for lighting differences when assessing potential matches to previously encountered objects. The dataset used in this work includes imagery gathered under diverse environmental conditions, including daytime and nighttime lighting. Experimental results inform expectations for possible improvements with automatic color space selection through feature learning. Moreover, we find there is only a gradual decrease in model performance with degraded color resolution, which suggests the need for simplified data collection and processing. By focusing on the most critical features, we could see improved model generalization and robustness, as the model becomes less prone to overfitting to noise or irrelevant details in the data. Such a reduction in resolution will lower computational complexity, leading to quicker training and inference times. Full article

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30 pages, 37493 KiB

Open AccessReview

What to Expect (and What Not) from Dual-Energy CT Imaging Now and in the Future?

by Roberto García-Figueiras, Laura Oleaga, Jordi Broncano, Gonzalo Tardáguila, Gabriel Fernández-Pérez, Eliseo Vañó, Eloísa Santos-Armentia, Ramiro Méndez, Antonio Luna and Sandra Baleato-González

J. Imaging 2024, 10(7), 154; https://doi.org/10.3390/jimaging10070154 - 26 Jun 2024

Viewed by 1347

Abstract

Dual-energy CT (DECT) imaging has broadened the potential of CT imaging by offering multiple postprocessing datasets with a single acquisition at more than one energy level. DECT shows profound capabilities to improve diagnosis based on its superior material differentiation and its quantitative value. [...] Read more.

Dual-energy CT (DECT) imaging has broadened the potential of CT imaging by offering multiple postprocessing datasets with a single acquisition at more than one energy level. DECT shows profound capabilities to improve diagnosis based on its superior material differentiation and its quantitative value. However, the potential of dual-energy imaging remains relatively untapped, possibly due to its intricate workflow and the intrinsic technical limitations of DECT. Knowing the clinical advantages of dual-energy imaging and recognizing its limitations and pitfalls is necessary for an appropriate clinical use. The aims of this paper are to review the physical and technical bases of DECT acquisition and analysis, to discuss the advantages and limitations of DECT in different clinical scenarios, to review the technical constraints in material labeling and quantification, and to evaluate the cutting-edge applications of DECT imaging, including artificial intelligence, qualitative and quantitative imaging biomarkers, and DECT-derived radiomics and radiogenomics. 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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18 pages, 879 KiB

Open AccessArticle

A Study on Data Selection for Object Detection in Various Lighting Conditions for Autonomous Vehicles

by Hao Lin, Ashkan Parsi, Darragh Mullins, Jonathan Horgan, Enda Ward, Ciaran Eising, Patrick Denny, Brian Deegan, Martin Glavin and Edward Jones

J. Imaging 2024, 10(7), 153; https://doi.org/10.3390/jimaging10070153 - 22 Jun 2024

Viewed by 800

Abstract

In recent years, significant advances have been made in the development of Advanced Driver Assistance Systems (ADAS) and other technology for autonomous vehicles. Automated object detection is a crucial component of autonomous driving; however, there are still known issues that affect its performance. [...] Read more.

In recent years, significant advances have been made in the development of Advanced Driver Assistance Systems (ADAS) and other technology for autonomous vehicles. Automated object detection is a crucial component of autonomous driving; however, there are still known issues that affect its performance. For automotive applications, object detection algorithms are required to perform at a high standard in all lighting conditions; however, a major problem for object detection is poor performance in low-light conditions due to objects being less visible. This study considers the impact of training data composition on object detection performance in low-light conditions. In particular, this study evaluates the effect of different combinations of images of outdoor scenes, from different times of day, on the performance of deep neural networks, and considers the different challenges encountered during the training of a neural network. Through experiments with a widely used public database, as well as a number of commonly used object detection architectures, we show that more robust performance can be obtained with an appropriate balance of classes and illumination levels in the training data. The results also highlight the potential of adding images obtained in dusk and dawn conditions for improving object detection performance in day and night. Full article

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

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13 pages, 38675 KiB

Open AccessArticle

Efficient Wheat Head Segmentation with Minimal Annotation: A Generative Approach

by Jaden Myers, Keyhan Najafian, Farhad Maleki and Katie Ovens

J. Imaging 2024, 10(7), 152; https://doi.org/10.3390/jimaging10070152 - 21 Jun 2024

Viewed by 845

Abstract

Deep learning models have been used for a variety of image processing tasks. However, most of these models are developed through supervised learning approaches, which rely heavily on the availability of large-scale annotated datasets. Developing such datasets is tedious and expensive. In the [...] Read more.

Deep learning models have been used for a variety of image processing tasks. However, most of these models are developed through supervised learning approaches, which rely heavily on the availability of large-scale annotated datasets. Developing such datasets is tedious and expensive. In the absence of an annotated dataset, synthetic data can be used for model development; however, due to the substantial differences between simulated and real data, a phenomenon referred to as domain gap, the resulting models often underperform when applied to real data. In this research, we aim to address this challenge by first computationally simulating a large-scale annotated dataset and then using a generative adversarial network (GAN) to fill the gap between simulated and real images. This approach results in a synthetic dataset that can be effectively utilized to train a deep-learning model. Using this approach, we developed a realistic annotated synthetic dataset for wheat head segmentation. This dataset was then used to develop a deep-learning model for semantic segmentation. The resulting model achieved a Dice score of 83.4% on an internal dataset and Dice scores of 79.6% and 83.6% on two external datasets from the Global Wheat Head Detection datasets. While we proposed this approach in the context of wheat head segmentation, it can be generalized to other crop types or, more broadly, to images with dense, repeated patterns such as those found in cellular imagery. Full article

(This article belongs to the Special Issue Imaging Applications in Agriculture)

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27 pages, 8812 KiB

Open AccessArticle

Robust PCA with L_w,∗ and L_2,1 Norms: A Novel Method for Low-Quality Retinal Image Enhancement

by Habte Tadesse Likassa, Ding-Geng Chen, Kewei Chen, Yalin Wang and Wenhui Zhu

J. Imaging 2024, 10(7), 151; https://doi.org/10.3390/jimaging10070151 - 21 Jun 2024

Viewed by 837

Abstract

Nonmydriatic retinal fundus images often suffer from quality issues and artifacts due to ocular or systemic comorbidities, leading to potential inaccuracies in clinical diagnoses. In recent times, deep learning methods have been widely employed to improve retinal image quality. However, these methods often [...] Read more.

Nonmydriatic retinal fundus images often suffer from quality issues and artifacts due to ocular or systemic comorbidities, leading to potential inaccuracies in clinical diagnoses. In recent times, deep learning methods have been widely employed to improve retinal image quality. However, these methods often require large datasets and lack robustness in clinical settings. Conversely, the inherent stability and adaptability of traditional unsupervised learning methods, coupled with their reduced reliance on extensive data, render them more suitable for real-world clinical applications, particularly in the limited data context of high noise levels or a significant presence of artifacts. However, existing unsupervised learning methods encounter challenges such as sensitivity to noise and outliers, reliance on assumptions like cluster shapes, and difficulties with scalability and interpretability, particularly when utilized for retinal image enhancement. To tackle these challenges, we propose a novel robust PCA (RPCA) method with low-rank sparse decomposition that also integrates affine transformations

τ_{i}

, weighted nuclear norm, and the

L_{2, 1}

norms, aiming to overcome existing method limitations and to achieve image quality improvement unseen by these methods. We employ the weighted nuclear norm

(L_{w, *})

to assign weights to singular values to each retinal images and utilize the

L_{2, 1}

norm to eliminate correlated samples and outliers in the retinal images. Moreover,

τ_{i}

is employed to enhance retinal image alignment, making the new method more robust to variations, outliers, noise, and image blurring. The Alternating Direction Method of Multipliers (ADMM) method is used to optimally determine parameters, including

τ_{i}

, by solving an optimization problem. Each parameter is addressed separately, harnessing the benefits of ADMM. Our method introduces a novel parameter update approach and significantly improves retinal image quality, detecting cataracts, and diabetic retinopathy. Simulation results confirm our method’s superiority over existing state-of-the-art methods across various datasets. Full article

(This article belongs to the Special Issue Advances in Retinal Image Processing)

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15 pages, 3248 KiB

Open AccessArticle

Color Biomimetics in Textile Design: Reproduction of Natural Plant Colors through Instrumental Colorant Formulation

by Isabel Cabral, Amanda Schuch and Fernanda Steffens

J. Imaging 2024, 10(7), 150; https://doi.org/10.3390/jimaging10070150 - 21 Jun 2024

Viewed by 813

Abstract

This paper explores the intersection of colorimetry and biomimetics in textile design, focusing on mimicking natural plant colors in dyed textiles via instrumental colorant formulation. The experimental work was conducted with two polyester substrates dyed with disperse dyes using the exhaustion process. Textiles [...] Read more.

This paper explores the intersection of colorimetry and biomimetics in textile design, focusing on mimicking natural plant colors in dyed textiles via instrumental colorant formulation. The experimental work was conducted with two polyester substrates dyed with disperse dyes using the exhaustion process. Textiles dyed with different dye colors and concentrations were measured in a spectrophotometer and a database was created in Datacolor Match Textile software version 2.4.1 (0) with the samples’ colorimetric properties. Colorant recipe formulation encompassed the definition and measurement of the pattern colors (along four defined natural plants), the selection of the colorants, and the software calculation of the recipes. After textile dyeing with the lowest expected CIELAB color difference (ΔE*) value recipe for each pattern color, a comparative analysis was conducted by spectral reflectance and visual assessment. Scanning electron microscopy and white light interferometry were also used to characterize the surface of the natural elements. Samples dyed with the formulated recipe attained good chromatic similarity with the respective natural plants’ colors, and the majority of the samples presented ΔE* between 1.5 and 4.0. Additionally, recipe optimization can also be conducted based on the colorimetric evaluation. This research contributes a design framework for biomimicking colors in textile design, establishing a systematic method based on colorimetry and color theory that enables the reproduction of nature’s color palette through the effective use of colorants. Full article

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J. Imaging, Volume 10, Issue 7 (July 2024) – 24 articles

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