Journal of Imaging

12 pages, 721 KiB

Open AccessArticle

Multi-Frequency GPR Microwave Imaging of Sparse Targets through a Multi-Task Bayesian Compressive Sensing Approach

by Marco Salucci and Nicola Anselmi

J. Imaging 2021, 7(11), 247; https://doi.org/10.3390/jimaging7110247 - 21 Nov 2021

Cited by 1 | Viewed by 1655

An innovative inverse scattering (IS) method is proposed for the quantitative imaging of pixel-sparse scatterers buried within a lossy half-space. On the one hand, such an approach leverages on the wide-band nature of ground penetrating radar (GPR) data by [...] Read more.

An innovative inverse scattering (IS) method is proposed for the quantitative imaging of pixel-sparse scatterers buried within a lossy half-space. On the one hand, such an approach leverages on the wide-band nature of ground penetrating radar (GPR) data by jointly processing the multi-frequency (MF) spectral components of the collected radargrams. On the other hand, it enforces sparsity priors on the problem unknowns to yield regularized solutions of the fully non-linear scattering equations. Towards this end, a multi-task Bayesian compressive sensing (MT-BCS) methodology is adopted and suitably customized to take full advantage of the available frequency diversity and of the a-priori information on the class of imaged targets. Representative results are reported to assess the proposed MF-MT-BCS strategy also in comparison with competitive state-of-the-art alternatives. Full article

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29 pages, 12358 KiB

Open AccessArticle

A Continuity Flow Based Tomographic Reconstruction Algorithm for 4D Multi-Beam High Temporal—Low Angular Sampling

by Axel Henningsson and Stephen A. Hall

J. Imaging 2021, 7(11), 246; https://doi.org/10.3390/jimaging7110246 - 20 Nov 2021

Cited by 2 | Viewed by 1853

Abstract

A mathematical framework and accompanying numerical algorithm exploiting the continuity equation for 4D reconstruction of spatiotemporal attenuation fields from multi-angle full-field transmission measurements is presented. The algorithm is geared towards rotation-free dynamic multi-beam X-ray tomography measurements, for which angular information is sparse but [...] Read more.

A mathematical framework and accompanying numerical algorithm exploiting the continuity equation for 4D reconstruction of spatiotemporal attenuation fields from multi-angle full-field transmission measurements is presented. The algorithm is geared towards rotation-free dynamic multi-beam X-ray tomography measurements, for which angular information is sparse but the temporal information is rich. 3D attenuation maps are recovered by propagating an initial discretized density volume in time according to the advection equations using the Finite Volumes method with a total variation diminishing monotonic upstream-centered scheme (TVDMUSCL). The benefits and limitations of the algorithm are explored using dynamic granular system phantoms modelled via discrete elements and projected by an analytical ray model independent from the numerical ray model used in the reconstruction scheme. Three phantom scenarios of increasing complexity are presented and it is found that projections from only a few (unknowns:equations > 10) angles can be sufficient for characterisation of the 3D attenuation field evolution in time. It is shown that the artificial velocity field produced by the algorithm sub-iteration, which is used to propagate the attenuation field, can to some extent approximate the true kinematics of the system. Furthermore, it is found that the selection of a temporal interpolation scheme for projection data can have a significant impact on error build up in the reconstructed attenuation field. Full article

(This article belongs to the Special Issue X-ray Digital Radiography and Computed Tomography)

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25 pages, 5028 KiB

Open AccessArticle

Colour and Texture Descriptors for Visual Recognition: A Historical Overview

by Francesco Bianconi, Antonio Fernández, Fabrizio Smeraldi and Giulia Pascoletti

J. Imaging 2021, 7(11), 245; https://doi.org/10.3390/jimaging7110245 - 19 Nov 2021

Cited by 18 | Viewed by 4049

Abstract

Colour and texture are two perceptual stimuli that determine, to a great extent, the appearance of objects, materials and scenes. The ability to process texture and colour is a fundamental skill in humans as well as in animals; therefore, reproducing such capacity in [...] Read more.

Colour and texture are two perceptual stimuli that determine, to a great extent, the appearance of objects, materials and scenes. The ability to process texture and colour is a fundamental skill in humans as well as in animals; therefore, reproducing such capacity in artificial (‘intelligent’) systems has attracted considerable research attention since the early 70s. Whereas the main approach to the problem was essentially theory-driven (‘hand-crafted’) up to not long ago, in recent years the focus has moved towards data-driven solutions (deep learning). In this overview we retrace the key ideas and methods that have accompanied the evolution of colour and texture analysis over the last five decades, from the ‘early years’ to convolutional networks. Specifically, we review geometric, differential, statistical and rank-based approaches. Advantages and disadvantages of traditional methods vs. deep learning are also critically discussed, including a perspective on which traditional methods have already been subsumed by deep learning or would be feasible to integrate in a data-driven approach. Full article

(This article belongs to the Special Issue Color Texture Classification)

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14 pages, 2536 KiB

Open AccessArticle

Improved Coefficient Recovery and Its Application for Rewritable Data Embedding

by Alan Sii, Simying Ong and KokSheik Wong

J. Imaging 2021, 7(11), 244; https://doi.org/10.3390/jimaging7110244 - 18 Nov 2021

Viewed by 1236

Abstract

JPEG is the most commonly utilized image coding standard for storage and transmission purposes. It achieves a good rate–distortion trade-off, and it has been adopted by many, if not all, handheld devices. However, often information loss occurs due to transmission error or damage [...] Read more.

JPEG is the most commonly utilized image coding standard for storage and transmission purposes. It achieves a good rate–distortion trade-off, and it has been adopted by many, if not all, handheld devices. However, often information loss occurs due to transmission error or damage to the storage device. To address this problem, various coefficient recovery methods have been proposed in the past, including a divide-and-conquer approach to speed up the recovery process. However, the segmentation technique considered in the existing method operates with the assumption of a bi-modal distribution for the pixel values, but most images do not satisfy this condition. Therefore, in this work, an adaptive method was employed to perform more accurate segmentation, so that the real potential of the previous coefficient recovery methods can be unleashed. In addition, an improved rewritable adaptive data embedding method is also proposed that exploits the recoverability of coefficients. Discrete cosine transformation (DCT) patches and blocks for data hiding are judiciously selected based on the predetermined precision to control the embedding capacity and image distortion. Our results suggest that the adaptive coefficient recovery method is able to improve on the conventional method up to 27% in terms of CPU time, and it also achieved better image quality with most considered images. Furthermore, the proposed rewritable data embedding method is able to embed 20,146 bits into an image of dimensions

512 \times 512

. Full article

(This article belongs to the Special Issue Intelligent Media Processing)

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27 pages, 2345 KiB

Open AccessArticle

Conditional Invertible Neural Networks for Medical Imaging

by Alexander Denker, Maximilian Schmidt, Johannes Leuschner and Peter Maass

J. Imaging 2021, 7(11), 243; https://doi.org/10.3390/jimaging7110243 - 17 Nov 2021

Cited by 25 | Viewed by 3702

Abstract

Over recent years, deep learning methods have become an increasingly popular choice for solving tasks from the field of inverse problems. Many of these new data-driven methods have produced impressive results, although most only give point estimates for the reconstruction. However, especially in [...] Read more.

Over recent years, deep learning methods have become an increasingly popular choice for solving tasks from the field of inverse problems. Many of these new data-driven methods have produced impressive results, although most only give point estimates for the reconstruction. However, especially in the analysis of ill-posed inverse problems, the study of uncertainties is essential. In our work, we apply generative flow-based models based on invertible neural networks to two challenging medical imaging tasks, i.e., low-dose computed tomography and accelerated medical resonance imaging. We test different architectures of invertible neural networks and provide extensive ablation studies. In most applications, a standard Gaussian is used as the base distribution for a flow-based model. Our results show that the choice of a radial distribution can improve the quality of reconstructions. Full article

(This article belongs to the Special Issue Inverse Problems and Imaging)

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3 pages, 182 KiB

Open AccessEditorial

Image and Video Forensics

by Irene Amerini, Gianmarco Baldini and Francesco Leotta

J. Imaging 2021, 7(11), 242; https://doi.org/10.3390/jimaging7110242 - 17 Nov 2021

Cited by 1 | Viewed by 2046

Abstract

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security more and more [...] Full article

(This article belongs to the Special Issue Image and Video Forensics)

14 pages, 2376 KiB

Open AccessArticle

Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms

by Abdellatif Moussaid, Sanaa El Fkihi and Yahya Zennayi

J. Imaging 2021, 7(11), 241; https://doi.org/10.3390/jimaging7110241 - 17 Nov 2021

Cited by 10 | Viewed by 2738

Abstract

Smart agriculture is a new concept that combines agriculture and new technologies to improve the yield’s quality and quantity as well as facilitate many tasks for farmers in managing orchards. An essential factor in smart agriculture is tree crown segmentation, which helps farmers [...] Read more.

Smart agriculture is a new concept that combines agriculture and new technologies to improve the yield’s quality and quantity as well as facilitate many tasks for farmers in managing orchards. An essential factor in smart agriculture is tree crown segmentation, which helps farmers automatically monitor their orchards and get information about each tree. However, one of the main problems, in this case, is when the trees are close to each other, which means that it would be difficult for the algorithm to delineate the crowns correctly. This paper used satellite images and machine learning algorithms to segment and classify trees in overlapping orchards. The data used are images from the Moroccan Mohammed VI satellite, and the study region is the OUARGHA citrus orchard located in Morocco. Our approach starts by segmenting the rows inside the parcel and finding all the trees there, getting their canopies, and classifying them by size. In general, the model inputs the parcel’s image and other field measurements to classify the trees into three classes: missing/weak, normal, or big. Finally, the results are visualized in a map containing all the trees with their classes. For the results, we obtained a score of 0.93 F-measure in rows segmentation. Additionally, several field comparisons were performed to validate the classification, dozens of trees were compared and the results were very good. This paper aims to help farmers to quickly and automatically classify trees by crown size, even if there are overlapping orchards, in order to easily monitor each tree’s health and understand the tree’s distribution in the field. Full article

(This article belongs to the Special Issue Advances in Multi/Hyperspectral Imaging)

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26 pages, 6633 KiB

Open AccessArticle

Analysis, Design and Realization of a Furnace for In Situ Wettability Experiments at High Temperatures under X-ray Microtomography

by Roberto Fedele, Fareeha Hameed, Nicola Cefis and Gabriele Vergani

J. Imaging 2021, 7(11), 240; https://doi.org/10.3390/jimaging7110240 - 15 Nov 2021

Cited by 2 | Viewed by 2528

Abstract

In this study, we analyzed the problem of a compact furnace, to be used for in situ experiments in a cone-beam X-ray microtomography commercial system. The design process was accomplished and outlined through its main steps, until the realization of a prototype. The [...] Read more.

In this study, we analyzed the problem of a compact furnace, to be used for in situ experiments in a cone-beam X-ray microtomography commercial system. The design process was accomplished and outlined through its main steps, until the realization of a prototype. The furnace was conceived to carry out wettability experiments at temperatures up to 700 °C and under inert atmosphere on sessile droplets of a molten metal alloy, with a few millimeters diameter, posed on a thin ceramic substrate. X-ray imaging of the molten droplet is expected to permit an accurate three-dimensional reconstruction of the droplet profile and a robust estimation of the related quantities (such as the contact angle and the surface tension) utilized for the assessment of metal-ceramic joints by brazing. The challenges faced during this project, mostly related to the constraints of the setup, and the novel solutions implemented were discussed also with the support of analytical and numerical tools, in terms of interaction of X-rays with matter, geometry and working principle, heat transfer and insulation, material selection. Full article

(This article belongs to the Special Issue X-ray Digital Radiography and Computed Tomography)

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13 pages, 1403 KiB

Open AccessArticle

Discretization of Learned NETT Regularization for Solving Inverse Problems

by Stephan Antholzer and Markus Haltmeier

J. Imaging 2021, 7(11), 239; https://doi.org/10.3390/jimaging7110239 - 15 Nov 2021

Cited by 5 | Viewed by 1400

Abstract

Deep learning based reconstruction methods deliver outstanding results for solving inverse problems and are therefore becoming increasingly important. A recently invented class of learning-based reconstruction methods is the so-called NETT (for Network Tikhonov Regularization), which contains a trained neural network as regularizer in [...] Read more.

Deep learning based reconstruction methods deliver outstanding results for solving inverse problems and are therefore becoming increasingly important. A recently invented class of learning-based reconstruction methods is the so-called NETT (for Network Tikhonov Regularization), which contains a trained neural network as regularizer in generalized Tikhonov regularization. The existing analysis of NETT considers fixed operators and fixed regularizers and analyzes the convergence as the noise level in the data approaches zero. In this paper, we extend the frameworks and analysis considerably to reflect various practical aspects and take into account discretization of the data space, the solution space, the forward operator and the neural network defining the regularizer. We show the asymptotic convergence of the discretized NETT approach for decreasing noise levels and discretization errors. Additionally, we derive convergence rates and present numerical results for a limited data problem in photoacoustic tomography. Full article

(This article belongs to the Special Issue Inverse Problems and Imaging)

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15 pages, 612 KiB

Open AccessReview

Ultrasound Imaging in Dentistry: A Literature Overview

by Rodolfo Reda, Alessio Zanza, Andrea Cicconetti, Shilpa Bhandi, Gabriele Miccoli, Gianluca Gambarini and Dario Di Nardo

J. Imaging 2021, 7(11), 238; https://doi.org/10.3390/jimaging7110238 - 14 Nov 2021

Cited by 36 | Viewed by 4005

Abstract

(1) Background: the frequency with which diagnostic tests are prescribed with exposure to ionizing radiation, a cause of biological damage, has been studied, and with much more attention, patients are subjected to these diagnostic tests for diagnosis and follow-up. This review aimed, given [...] Read more.

(1) Background: the frequency with which diagnostic tests are prescribed with exposure to ionizing radiation, a cause of biological damage, has been studied, and with much more attention, patients are subjected to these diagnostic tests for diagnosis and follow-up. This review aimed, given the recent developments of this technology, to evaluate the possible use of ultrasound in different branches of dentistry. The possibility of applying ionizing-radiation-free diagnostic exams in dentistry, overcoming the limits of this application, has led scientific research in this area to obtain interesting results that bode well for the future. (2) Methods: a search for articles on the application of ultrasounds in dentistry was performed using the PubMed electronic database. (3) Results: only 32 studies were included, and these clearly stated that this examination is widely usable and in great progress. (4) Conclusions: regarding the modern application techniques of this diagnostic test, it is essential to consider technological evolution as an objective to reduce the damage and side effects of necessary diagnostic tests. The use of ultrasound in dentistry can represent a valid radiation-free alternative, in certain contexts, to the other most used exams. Full article

(This article belongs to the Special Issue New Frontiers of Advanced Imaging in Dentistry)

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4 pages, 200 KiB

Open AccessEditorial

Advanced Computational Methods for Oncological Image Analysis

by Leonardo Rundo, Carmelo Militello, Vincenzo Conti, Fulvio Zaccagna and Changhee Han

J. Imaging 2021, 7(11), 237; https://doi.org/10.3390/jimaging7110237 - 12 Nov 2021

Cited by 2 | Viewed by 1554

Abstract

The Special Issue “Advanced Computational Methods for Oncological Image Analysis”, published for the Journal of Imaging, covered original research papers about state-of-the-art and novel algorithms and methodologies, as well as applications of computational methods for oncological image analysis, ranging from radiogenomics to [...] Read more.

The Special Issue “Advanced Computational Methods for Oncological Image Analysis”, published for the Journal of Imaging, covered original research papers about state-of-the-art and novel algorithms and methodologies, as well as applications of computational methods for oncological image analysis, ranging from radiogenomics to deep learning [...] Full article

(This article belongs to the Special Issue Advanced Computational Methods for Oncological Image Analysis)

18 pages, 18210 KiB

Open AccessArticle

Automated Data Annotation for 6-DoF AI-Based Navigation Algorithm Development

by Javier Gibran Apud Baca, Thomas Jantos, Mario Theuermann, Mohamed Amin Hamdad, Jan Steinbrener, Stephan Weiss, Alexander Almer and Roland Perko

J. Imaging 2021, 7(11), 236; https://doi.org/10.3390/jimaging7110236 - 10 Nov 2021

Cited by 4 | Viewed by 2543

Abstract

Accurately estimating the six degree of freedom (6-DoF) pose of objects in images is essential for a variety of applications such as robotics, autonomous driving, and autonomous, AI, and vision-based navigation for unmanned aircraft systems (UAS). Developing such algorithms requires large datasets; however, [...] Read more.

Accurately estimating the six degree of freedom (6-DoF) pose of objects in images is essential for a variety of applications such as robotics, autonomous driving, and autonomous, AI, and vision-based navigation for unmanned aircraft systems (UAS). Developing such algorithms requires large datasets; however, generating those is tedious as it requires annotating the 6-DoF relative pose of each object of interest present in the image w.r.t. to the camera. Therefore, this work presents a novel approach that automates the data acquisition and annotation process and thus minimizes the annotation effort to the duration of the recording. To maximize the quality of the resulting annotations, we employ an optimization-based approach for determining the extrinsic calibration parameters of the camera. Our approach can handle multiple objects in the scene, automatically providing ground-truth labeling for each object and taking into account occlusion effects between different objects. Moreover, our approach can not only be used to generate data for 6-DoF pose estimation and corresponding 3D-models but can be also extended to automatic dataset generation for object detection, instance segmentation, or volume estimation for any kind of object. Full article

(This article belongs to the Special Issue Formal Verification of Imaging Algorithms for Autonomous System)

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13 pages, 1549 KiB

Open AccessArticle

Impacts of Phantom Off-Center Positioning on CT Numbers and Dose Index CTDIv: An Evaluation of Two CT Scanners from GE

by Xiaoming Zheng, Lachlan Gutsche, Yazan Al-Hayek, Johanna Stanton, Wiam Elshami and Kelsey Jensen

J. Imaging 2021, 7(11), 235; https://doi.org/10.3390/jimaging7110235 - 10 Nov 2021

Cited by 7 | Viewed by 1921

Abstract

The purpose of this work is to evaluate the impacts of body off-center positioning on CT numbers and dose index CTDIv of two scanners from GE. HD750 and APEX scanners were used to acquire a PBU60 phantom of Kagaku and a 062M phantom [...] Read more.

The purpose of this work is to evaluate the impacts of body off-center positioning on CT numbers and dose index CTDIv of two scanners from GE. HD750 and APEX scanners were used to acquire a PBU60 phantom of Kagaku and a 062M phantom of CIRS respectively. CT images were acquired at various off-center positions under automatic tube current modulation using various peak voltages. CTDIv were recorded for each of the acquisitions. An abdomen section of the PBU60 phantom was used for CT number analysis and tissue inserts of the 062M phantom were filled with water balloons to mimic the human abdomen. CT numbers of central regions of interests were averaged using the Fiji software. As phantoms were lifted above the iso-center, both CTDIv and CT numbers were increased for the HD750 scanner whilst they were approximately constant for the APEX scanner. The measured sizes of anterior-posterior projection images were also increased for both scanners whilst the sizes of lateral projection images were increased for the HD750 scanner but decreased for the APEX scanner. Off-center correction algorithms were implemented in the APEX scanner. Matching the X-ray projection center with the system’s iso-center could improve the accuracy of CT imaging. Full article

(This article belongs to the Special Issue X-ray Digital Radiography and Computed Tomography)

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10 pages, 616 KiB

Open AccessArticle

Recovering the Magnetic Image of Mars from Satellite Observations

by Igor Kolotov, Dmitry Lukyanenko, Inna Stepanova, Yanfei Wang and Anatoly Yagola

J. Imaging 2021, 7(11), 234; https://doi.org/10.3390/jimaging7110234 - 09 Nov 2021

Cited by 5 | Viewed by 1582

Abstract

One of the possible approaches to reconstructing the map of the distribution of magnetization parameters in the crust of Mars from the data of the Mars MAVEN orbiter mission is considered. Possible ways of increasing the accuracy of reconstruction of the magnetic image [...] Read more.

One of the possible approaches to reconstructing the map of the distribution of magnetization parameters in the crust of Mars from the data of the Mars MAVEN orbiter mission is considered. Possible ways of increasing the accuracy of reconstruction of the magnetic image of Mars are discussed. Full article

(This article belongs to the Special Issue Inverse Problems and Imaging)

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11 pages, 4958 KiB

Open AccessArticle

Illuminating the Imperceptible, Researching Mina’i Ceramics with Digital Imaging Techniques

by Dana Norris and Oliver Watson

J. Imaging 2021, 7(11), 233; https://doi.org/10.3390/jimaging7110233 - 08 Nov 2021

Cited by 1 | Viewed by 1906

Abstract

Mina’i ceramics dating to the late 12th and early 13th century made in the Kashan region of Iran represent a novel period of overglaze enamelling technology in ceramic history. New colours were used to produce stylistically attractive and dynamic polychrome motifs. Due to [...] Read more.

Mina’i ceramics dating to the late 12th and early 13th century made in the Kashan region of Iran represent a novel period of overglaze enamelling technology in ceramic history. New colours were used to produce stylistically attractive and dynamic polychrome motifs. Due to their archaeological context, and popularity in the art market since the mid-20th century, these objects often have complex conditions involving reconstruction and overpainting. The aesthetic and technological significance of these pieces warrants further study, but in practice, removing restorations can lead to structural destabilisation, requiring time-consuming and potentially unplanned for conservation treatment. To determine if it is possible to gain useful information from the study of these artworks without disturbing existing restorations, a group of objects were drawn from the Sarikhani and Ashmolean Museum of Art and Archaeology collections. The objective of this project was twofold, first to assess the merits of the imaging techniques for understanding condition, and second to propose a protocol for imaging with the aim of encouraging collaborative projects with international partners. The techniques used in this study include digital photography under visible and ultraviolet light, infrared reflectography, and radiography. The results show that important information invisible to the naked eye can be obtained about the decorative surfaces, using ultraviolet light and infrared reflectography. Digital radiography proved to be equally effective when studying the condition of the ceramic body. The results of this project were used to produce guidance on these techniques as a collaborative documentation package for the study of Mina’i ceramics. Full article

(This article belongs to the Special Issue X-ray Digital Radiography and Computed Tomography)

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9 pages, 22028 KiB

Open AccessArticle

The First Application of a Gd₃Al₂Ga₃O₁₂:Ce Single-Crystal Scintillator to Neutron Radiography

by Kazuhisa Isegawa, Daigo Setoyama, Hidehiko Kimura and Takenao Shinohara

J. Imaging 2021, 7(11), 232; https://doi.org/10.3390/jimaging7110232 - 02 Nov 2021

Cited by 4 | Viewed by 1780

Abstract

Neutron radiography is regarded as complementary to X-ray radiography in terms of transmittance through materials, but its spatial resolution is still insufficient. In order to achieve higher resolution in neutron imaging, several approaches have been adopted, such as optical magnification and event centroiding. [...] Read more.

Neutron radiography is regarded as complementary to X-ray radiography in terms of transmittance through materials, but its spatial resolution is still insufficient. In order to achieve higher resolution in neutron imaging, several approaches have been adopted, such as optical magnification and event centroiding. In this paper, the authors focused on modification of the scintillator. A Gd₃Al₂Ga₃O₁₂:Ce single-crystal scintillator was applied to neutron radiography for the first time and a spatial resolution of 10.5 μm was achieved. The results indicate that this material can be a powerful candidate for a new neutron scintillator providing a resolution in micrometer order by optimizing the optical system and increasing the scintillator luminosity. Full article

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29 pages, 25475 KiB

Open AccessArticle

An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset

by Wanyu Bian, Yunmei Chen, Xiaojing Ye and Qingchao Zhang

J. Imaging 2021, 7(11), 231; https://doi.org/10.3390/jimaging7110231 - 31 Oct 2021

Cited by 5 | Viewed by 2354

Abstract

This work aims at developing a generalizable Magnetic Resonance Imaging (MRI) reconstruction method in the meta-learning framework. Specifically, we develop a deep reconstruction network induced by a learnable optimization algorithm (LOA) to solve the nonconvex nonsmooth variational model of MRI image reconstruction. In [...] Read more.

This work aims at developing a generalizable Magnetic Resonance Imaging (MRI) reconstruction method in the meta-learning framework. Specifically, we develop a deep reconstruction network induced by a learnable optimization algorithm (LOA) to solve the nonconvex nonsmooth variational model of MRI image reconstruction. In this model, the nonconvex nonsmooth regularization term is parameterized as a structured deep network where the network parameters can be learned from data. We partition these network parameters into two parts: a task-invariant part for the common feature encoder component of the regularization, and a task-specific part to account for the variations in the heterogeneous training and testing data. We train the regularization parameters in a bilevel optimization framework which significantly improves the robustness of the training process and the generalization ability of the network. We conduct a series of numerical experiments using heterogeneous MRI data sets with various undersampling patterns, ratios, and acquisition settings. The experimental results show that our network yields greatly improved reconstruction quality over existing methods and can generalize well to new reconstruction problems whose undersampling patterns/trajectories are not present during training. Full article

(This article belongs to the Special Issue Inverse Problems and Imaging)

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29 pages, 44702 KiB

Open AccessArticle

Quantitative Evaluation of Soil Structure and Strain in Three Dimensions under Shear Using X-ray Computed Tomography Image Analysis

by Shintaro Nohara and Toshifumi Mukunoki

J. Imaging 2021, 7(11), 230; https://doi.org/10.3390/jimaging7110230 - 29 Oct 2021

Cited by 2 | Viewed by 2287

Abstract

The objective of this study is to quantitatively evaluate the soil structure behavior when under shear stress to understand the mechanism of shear zone formation using a micro-focus X-ray computed tomography (CT) scanner to visualize the internal samples without causing disturbance. A new [...] Read more.

The objective of this study is to quantitatively evaluate the soil structure behavior when under shear stress to understand the mechanism of shear zone formation using a micro-focus X-ray computed tomography (CT) scanner to visualize the internal samples without causing disturbance. A new image-analysis method was proposed to systematically evaluate the particle length and direction by fitting the particle as an ellipsoid. Subsequently, a direct shear experiment was conducted on soil materials, and shear band was scanned using a micro-focus X-ray CT scanner. After validating the proposed method, the soil structure was evaluated in the shear zone via image analysis on the CT images. Furthermore, the strain inside the specimen was evaluated using digital image correlation. The results showed that a partial change in the particle direction occurred when the volume expansion inside the shear zone exceeded the peak. In addition, the width of the shear zone was ~7.1 times the median grain size of the sand used; however, the region exhibiting a change in the direction of the particles was narrow and confined to the vicinity of the shear plane. Full article

(This article belongs to the Special Issue Recent Advances in Image-Based Geotechnics)

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13 pages, 6268 KiB

Open AccessArticle

X-ray Imaging Investigation on the Gilding Technique of an Ancient Egyptian Taweret Wooden Statuette

by Luisa Vigorelli, Alessandro Re, Laura Guidorzi, Tiziana Cavaleri, Paola Buscaglia, Marco Nervo, Federica Facchetti, Matilde Borla, Sabrina Grassini and Alessandro Lo Giudice

J. Imaging 2021, 7(11), 229; https://doi.org/10.3390/jimaging7110229 - 29 Oct 2021

Cited by 10 | Viewed by 2539

Abstract

Diagnostic physical methods are increasingly applied to Cultural Heritage both for scientific investigations and conservation purposes. In particular, the X-ray imaging techniques of computed tomography (CT) and digital radiography (DR) are non-destructive investigation methods to study an object, being able to give information [...] Read more.

Diagnostic physical methods are increasingly applied to Cultural Heritage both for scientific investigations and conservation purposes. In particular, the X-ray imaging techniques of computed tomography (CT) and digital radiography (DR) are non-destructive investigation methods to study an object, being able to give information on its inner structure. In this paper, we present the results of the X-ray imaging study on an ancient Egyptian statuette (Late Period 722–30 BCE) belonging to the collection of Museo Egizio in Torino and representing an Egyptian goddess called Taweret, carved on wood and gilded with some colored details. Since few specific studies have been focused on materials and techniques used in Ancient Egypt for gilding, a detailed investigation was started in order to verify the technical features of the decoration in this sculpture. Specifically, DR and CT analyses have been performed at the Centro Conservazione e Restauro “La Venaria Reale” (CCR), with a new high resolution flat-panel detector, that allowed us to perform tomographic analysis reaching a final resolution better than the one achievable with the previous apparatus operating in the CCR. Full article

(This article belongs to the Special Issue X-ray Digital Radiography and Computed Tomography)

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15 pages, 16522 KiB

Open AccessArticle

On a Variational and Convex Model of the Blake–Zisserman Type for Segmentation of Low-Contrast and Piecewise Smooth Images

by Liam Burrows, Anis Theljani and Ke Chen

J. Imaging 2021, 7(11), 228; https://doi.org/10.3390/jimaging7110228 - 28 Oct 2021

Cited by 1 | Viewed by 1750

Abstract

This paper proposes a new variational model for segmentation of low-contrast and piecewise smooth images. The model is motivated by the two-stage image segmentation work of Cai–Chan–Zeng (2013) for the Mumford–Shah model. To deal with low-contrast images more effectively, especially in treating higher-order [...] Read more.

This paper proposes a new variational model for segmentation of low-contrast and piecewise smooth images. The model is motivated by the two-stage image segmentation work of Cai–Chan–Zeng (2013) for the Mumford–Shah model. To deal with low-contrast images more effectively, especially in treating higher-order discontinuities, we follow the idea of the Blake–Zisserman model instead of the Mumford–Shah. Two practical ideas are introduced here: first, a convex relaxation idea is used to derive an implementable formulation, and second, a game reformulation is proposed to reduce the strong dependence of coupling parameters. The proposed model is then analysed for existence and further solved by an ADMM solver. Numerical experiments can show that the new model outperforms the current state-of-the-art models for some challenging and low-contrast images. Full article

(This article belongs to the Special Issue Inverse Problems and Imaging)

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13 pages, 2420 KiB

Open AccessArticle

A Temporal Boosted YOLO-Based Model for Birds Detection around Wind Farms

by Hiba Alqaysi, Igor Fedorov, Faisal Z. Qureshi and Mattias O’Nils

J. Imaging 2021, 7(11), 227; https://doi.org/10.3390/jimaging7110227 - 27 Oct 2021

Cited by 11 | Viewed by 4201

Abstract

Object detection for sky surveillance is a challenging problem due to having small objects in a large volume and a constantly changing background which requires high resolution frames. For example, detecting flying birds in wind farms to prevent their collision with the wind [...] Read more.

Object detection for sky surveillance is a challenging problem due to having small objects in a large volume and a constantly changing background which requires high resolution frames. For example, detecting flying birds in wind farms to prevent their collision with the wind turbines. This paper proposes a YOLOv4-based ensemble model for bird detection in grayscale videos captured around wind turbines in wind farms. In order to tackle this problem, we introduce two datasets—(1) Klim and (2) Skagen—collected at two locations in Denmark. We use Klim training set to train three increasingly capable YOLOv4 based models. Model 1 uses YOLOv4 trained on the Klim dataset, Model 2 introduces tiling to improve small bird detection, and the last model uses tiling and temporal stacking and achieves the best mAP values on both Klim and Skagen datasets. We used this model to set up an ensemble detector, which further improves mAP values on both datasets. The three models achieve testing mAP values of 82%, 88%, and 90% on the Klim dataset. mAP values for Model 1 and Model 3 on the Skagen dataset are 60% and 92%. Improving object detection accuracy could mitigate birds’ mortality rate by choosing the locations for such establishment and the turbines location. It can also be used to improve the collision avoidance systems used in wind energy facilities. Full article

(This article belongs to the Special Issue Mobile Camera-Based Image and Video Processing)

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26 pages, 4540 KiB

Open AccessArticle

Fast Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling and Anatomical Priors

by Marica Pesce, Audrey Repetti, Anna Auría, Alessandro Daducci, Jean-Philippe Thiran and Yves Wiaux

J. Imaging 2021, 7(11), 226; https://doi.org/10.3390/jimaging7110226 - 27 Oct 2021

Cited by 2 | Viewed by 1760

Abstract

High spatio-angular resolution diffusion MRI (dMRI) has been shown to provide accurate identification of complex neuronal fiber configurations, albeit, at the cost of long acquisition times. We propose a method to recover intra-voxel fiber configurations at high spatio-angular resolution relying on a 3D [...] Read more.

High spatio-angular resolution diffusion MRI (dMRI) has been shown to provide accurate identification of complex neuronal fiber configurations, albeit, at the cost of long acquisition times. We propose a method to recover intra-voxel fiber configurations at high spatio-angular resolution relying on a 3D kq-space under-sampling scheme to enable accelerated acquisitions. The inverse problem for the reconstruction of the fiber orientation distribution (FOD) is regularized by a structured sparsity prior promoting simultaneously voxel-wise sparsity and spatial smoothness of fiber orientation. Prior knowledge of the spatial distribution of white matter, gray matter, and cerebrospinal fluid is also leveraged. A minimization problem is formulated and solved via a stochastic forward–backward algorithm. Simulations and real data analysis suggest that accurate FOD mapping can be achieved from severe kq-space under-sampling regimes potentially enabling high spatio-angular resolution dMRI in the clinical setting. Full article

(This article belongs to the Special Issue Inverse Problems and Imaging)

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16 pages, 648 KiB

Open AccessArticle

Feature Selection Using Correlation Analysis and Principal Component Analysis for Accurate Breast Cancer Diagnosis

by Sara Ibrahim, Saima Nazir and Sergio A. Velastin

J. Imaging 2021, 7(11), 225; https://doi.org/10.3390/jimaging7110225 - 26 Oct 2021

Cited by 23 | Viewed by 4283

Abstract

Breast cancer is one of the leading causes of death among women, more so than all other cancers. The accurate diagnosis of breast cancer is very difficult due to the complexity of the disease, changing treatment procedures and different patient population samples. Diagnostic [...] Read more.

Breast cancer is one of the leading causes of death among women, more so than all other cancers. The accurate diagnosis of breast cancer is very difficult due to the complexity of the disease, changing treatment procedures and different patient population samples. Diagnostic techniques with better performance are very important for personalized care and treatment and to reduce and control the recurrence of cancer. The main objective of this research was to select feature selection techniques using correlation analysis and variance of input features before passing these significant features to a classification method. We used an ensemble method to improve the classification of breast cancer. The proposed approach was evaluated using the public WBCD dataset (Wisconsin Breast Cancer Dataset). Correlation analysis and principal component analysis were used for dimensionality reduction. Performance was evaluated for well-known machine learning classifiers, and the best seven classifiers were chosen for the next step. Hyper-parameter tuning was performed to improve the performances of the classifiers. The best performing classification algorithms were combined with two different voting techniques. Hard voting predicts the class that gets the majority vote, whereas soft voting predicts the class based on highest probability. The proposed approach performed better than state-of-the-art work, achieving an accuracy of 98.24%, high precision (99.29%) and a recall value of 95.89%. Full article

(This article belongs to the Special Issue Advanced Computational Methods for Oncological Image Analysis)

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9 pages, 3408 KiB

Open AccessArticle

Neutron and Synchrotron Imaging Studies of Preservation State of Metal of Cultural Heritage Objects

by Ekaterina Kovalenko, Mikhail Murashev, Konstantin Podurets, Elena Tereschenko and Ekaterina Yatsishina

J. Imaging 2021, 7(11), 224; https://doi.org/10.3390/jimaging7110224 - 26 Oct 2021

Cited by 1 | Viewed by 1797

Abstract

This paper analyzes the results of studies carried out at the National Research Center “Kurchatov Institute”, Moscow, using the methods of neutron and X-ray synchrotron tomography from the point of view of the preservation state of metal objects. Objects damaged by corrosion and [...] Read more.

This paper analyzes the results of studies carried out at the National Research Center “Kurchatov Institute”, Moscow, using the methods of neutron and X-ray synchrotron tomography from the point of view of the preservation state of metal objects. Objects damaged by corrosion and exposure to fire were the focus of this study. To identify regions of metal preservation, the diffraction contrast on grains of metal, observed in tomographic projections, was used. The simultaneous use of neutron and synchrotron imaging is shown to be a powerful tool for identification of the constituents of an object. Full article

(This article belongs to the Special Issue X-ray Digital Radiography and Computed Tomography)

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26 pages, 5462 KiB

Open AccessArticle

Fast Blob and Air Line Defects Detection for High Speed Glass Tube Production Lines

by Gabriele Antonio De Vitis, Antonio Di Tecco, Pierfrancesco Foglia and Cosimo Antonio Prete

J. Imaging 2021, 7(11), 223; https://doi.org/10.3390/jimaging7110223 - 25 Oct 2021

Cited by 4 | Viewed by 2451

Abstract

During the production of pharmaceutical glass tubes, a machine-vision based inspection system can be utilized to perform the high-quality check required by the process. The necessity to improve detection accuracy, and increase production speed determines the need for fast solutions for defects detection. [...] Read more.

During the production of pharmaceutical glass tubes, a machine-vision based inspection system can be utilized to perform the high-quality check required by the process. The necessity to improve detection accuracy, and increase production speed determines the need for fast solutions for defects detection. Solutions proposed in literature cannot be efficiently exploited due to specific factors that characterize the production process. In this work, we have derived an algorithm that does not change the detection quality compared to state-of-the-art proposals, but does determine a drastic reduction in the processing time. The algorithm utilizes an adaptive threshold based on the Sigma Rule to detect blobs, and applies a threshold to the variation of luminous intensity along a row to detect air lines. These solutions limit the detection effects due to the tube’s curvature, and rotation and vibration of the tube, which characterize glass tube production. The algorithm has been compared with state-of-the-art solutions. The results demonstrate that, with the algorithm proposed, the processing time of the detection phase is reduced by 86%, with an increase in throughput of 268%, achieving greater accuracy in detection. Performance is further improved by adopting Region of Interest reduction techniques. Moreover, we have developed a tuning procedure to determine the algorithm’s parameters in the production batch change. We assessed the performance of the algorithm in a real environment using the “certification” functionality of the machine. Furthermore, we observed that out of 1000 discarded tubes, nine should not have been discarded and a further seven should have been discarded. Full article

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

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10 pages, 1717 KiB

Open AccessArticle

A Radioactive-Free Method for the Thorough Analysis of the Kinetics of Cell Cytotoxicity

by Claudia Coronnello, Rosalia Busà, Luca Cicero, Albert Comelli and Ester Badami

J. Imaging 2021, 7(11), 222; https://doi.org/10.3390/jimaging7110222 - 23 Oct 2021

Cited by 2 | Viewed by 2789

Abstract

The cytotoxic activity of T cells and Natural Killer cells is usually measured with the chromium release assay (CRA), which involves the use of 51Chromium (⁵¹Cr), a radioactive substance dangerous to the operator and expensive to handle and dismiss. The accuracy [...] Read more.

The cytotoxic activity of T cells and Natural Killer cells is usually measured with the chromium release assay (CRA), which involves the use of 51Chromium (⁵¹Cr), a radioactive substance dangerous to the operator and expensive to handle and dismiss. The accuracy of the measurements depends on how well the target cells incorporate ⁵¹Cr during labelling which, in turn, depends on cellular division. Due to bystander metabolism, the target cells spontaneously release ⁵¹Cr, producing a high background noise. Alternative radioactive-free methods have been developed. Here, we compare a bioluminescence (BLI)-based and a carboxyfluorescein succinimidyl ester (CFSE)-based cytotoxicity assay to the standard radioactive CRA. In the first assay, the target cells stably express the enzyme luciferase, and vitality is measured by photon emission upon the addition of the substrate d-luciferin. In the second one, the target cells are labelled with CFSE, and the signal is detected by Flow Cytometry. We used these two protocols to measure cytotoxicity induced by treatment with NK cells. The cytotoxicity of NK cells was determined by adding increasing doses of human NK cells. The results obtained with the BLI method were consistent with those obtained with the CRA- or CFSE-based assays 4 hours after adding the NK cells. Most importantly, with the BLI assay, the kinetic of NK cells’ killing was thoroughly traced with multiple time point measurements, in contrast with the single time point measurement the other two methods allow, which unveiled additional information on NK cell killing pathways. Full article

(This article belongs to the Topic Medical Image Analysis)

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10 pages, 4728 KiB

Open AccessArticle

Single-Shot Multicontrast X-ray Imaging for In Situ Visualization of Chemical Reaction Products

by Margarita Zakharova, Andrey Mikhaylov, Vitor Vlnieska and Danays Kunka

J. Imaging 2021, 7(11), 221; https://doi.org/10.3390/jimaging7110221 - 23 Oct 2021

Cited by 3 | Viewed by 2767

Abstract

We present the application of single-shot multicontrast X-ray imaging with an inverted Hartmann mask to the time-resolved in situ visualization of chemical reaction products. The real-time monitoring of an illustrative chemical reaction indicated the formation of the precipitate by the absorption, differential phase, [...] Read more.

We present the application of single-shot multicontrast X-ray imaging with an inverted Hartmann mask to the time-resolved in situ visualization of chemical reaction products. The real-time monitoring of an illustrative chemical reaction indicated the formation of the precipitate by the absorption, differential phase, and scattering contrast images obtained from a single projection. Through these contrast channels, the formation of the precipitate along the mixing line of the reagents, the border between the solid and the solution, and the presence of the scattering structures of 100–200 nm sizes were observed. The measurements were performed in a flexible and robust setup, which can be tailored to various imaging applications at different time scales. Full article

(This article belongs to the Special Issue X-ray Digital Radiography and Computed Tomography)

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18 pages, 4771 KiB

Open AccessArticle

Chart Classification Using Siamese CNN

by Filip Bajić and Josip Job

J. Imaging 2021, 7(11), 220; https://doi.org/10.3390/jimaging7110220 - 21 Oct 2021

Cited by 7 | Viewed by 2598

Abstract

In recovering information from the chart image, the first step should be chart type classification. Throughout history, many approaches have been used, and some of them achieve results better than others. The latest articles are using a Support Vector Machine (SVM) in combination [...] Read more.

In recovering information from the chart image, the first step should be chart type classification. Throughout history, many approaches have been used, and some of them achieve results better than others. The latest articles are using a Support Vector Machine (SVM) in combination with a Convolutional Neural Network (CNN), which achieve almost perfect results with the datasets of few thousand images per class. The datasets containing chart images are primarily synthetic and lack real-world examples. To overcome the problem of small datasets, to our knowledge, this is the first report of using Siamese CNN architecture for chart type classification. Multiple network architectures are tested, and the results of different dataset sizes are compared. The network verification is conducted using Few-shot learning (FSL). Many of described advantages of Siamese CNNs are shown in examples. In the end, we show that the Siamese CNN can work with one image per class, and a 100% average classification accuracy is achieved with 50 images per class, where the CNN achieves only average classification accuracy of 43% for the same dataset. Full article

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

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20 pages, 75935 KiB

Open AccessArticle

Single Cell Center of Mass for the Analysis of BMP Receptor Heterodimers Distributions

by Hendrik Boog, Rebecca Medda and Elisabetta Ada Cavalcanti-Adam

J. Imaging 2021, 7(11), 219; https://doi.org/10.3390/jimaging7110219 - 20 Oct 2021

Cited by 1 | Viewed by 1993

Abstract

At the plasma membrane, transmembrane receptors are at the interface between cells and their environment. They allow sensing and transduction of chemical and mechanical extracellular signals. The spatial distribution of receptors and the specific recruitment of receptor subunits to the cell membrane is [...] Read more.

At the plasma membrane, transmembrane receptors are at the interface between cells and their environment. They allow sensing and transduction of chemical and mechanical extracellular signals. The spatial distribution of receptors and the specific recruitment of receptor subunits to the cell membrane is crucial for the regulation of signaling and cell behavior. However, it is challenging to define what regulates such spatial patterns for receptor localization, as cell shapes are extremely diverse when cells are maintained in standard culture conditions. Bone morphogenetic protein receptors (BMPRs) are serine-threonine kinases, which build heteromeric complexes of BMPRI and II. These are especially interesting targets for receptor distribution studies, since the signaling pathways triggered by BMPR-complexes depends on their dimerization mode. They might exist as preformed complexes, or assemble upon binding of BMP, triggering cell signaling which leads to differentiation or migration. In this work we analyzed BMPR receptor distributions in single cells grown on micropatterns, which allow not only to control cell shape, but also the distribution of intracellular organelles and protein assemblies. We developed a script called ComRed (Center Of Mass Receptor Distribution), which uses center of mass calculations to analyze the shift and spread of receptor distributions according to the different cell shapes. ComRed was tested by simulating changes in experimental data showing that shift and spread of distributions can be reliably detected. Our ComRed-based analysis of BMPR-complexes indicates that receptor distribution depends on cell polarization. The absence of a coordinated internalization after addition of BMP suggests that a rapid and continual recycling of BMPRs might occur. Receptor complexes formation and localization in cells induced by BMP might yield insights into the local regulation of different signaling pathways. Full article

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J. Imaging, Volume 7, Issue 11 (November 2021) – 29 articles

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