Journal of Imaging

20 pages, 18284 KiB

Open AccessArticle

Enhancing COVID-19 CT Image Segmentation: A Comparative Study of Attention and Recurrence in UNet Models

by Rossana Buongiorno, Giulio Del Corso, Danila Germanese, Leonardo Colligiani, Lorenzo Python, Chiara Romei and Sara Colantonio

J. Imaging 2023, 9(12), 283; https://doi.org/10.3390/jimaging9120283 - 18 Dec 2023

Viewed by 1543

Abstract

Imaging plays a key role in the clinical management of Coronavirus disease 2019 (COVID-19) as the imaging findings reflect the pathological process in the lungs. The visual analysis of High-Resolution Computed Tomography of the chest allows for the differentiation of parenchymal abnormalities of [...] Read more.

Imaging plays a key role in the clinical management of Coronavirus disease 2019 (COVID-19) as the imaging findings reflect the pathological process in the lungs. The visual analysis of High-Resolution Computed Tomography of the chest allows for the differentiation of parenchymal abnormalities of COVID-19, which are crucial to be detected and quantified in order to obtain an accurate disease stratification and prognosis. However, visual assessment and quantification represent a time-consuming task for radiologists. In this regard, tools for semi-automatic segmentation, such as those based on Convolutional Neural Networks, can facilitate the detection of pathological lesions by delineating their contour. In this work, we compared four state-of-the-art Convolutional Neural Networks based on the encoder–decoder paradigm for the binary segmentation of COVID-19 infections after training and testing them on 90 HRCT volumetric scans of patients diagnosed with COVID-19 collected from the database of the Pisa University Hospital. More precisely, we started from a basic model, the well-known UNet, then we added an attention mechanism to obtain an Attention-UNet, and finally we employed a recurrence paradigm to create a Recurrent–Residual UNet (R2-UNet). In the latter case, we also added attention gates to the decoding path of an R2-UNet, thus designing an R2-Attention UNet so as to make the feature representation and accumulation more effective. We compared them to gain understanding of both the cognitive mechanism that can lead a neural model to the best performance for this task and the good compromise between the amount of data, time, and computational resources required. We set up a five-fold cross-validation and assessed the strengths and limitations of these models by evaluating the performances in terms of Dice score, Precision, and Recall defined both on 2D images and on the entire 3D volume. From the results of the analysis, it can be concluded that Attention-UNet outperforms the other models by achieving the best performance of 81.93%, in terms of 2D Dice score, on the test set. Additionally, we conducted statistical analysis to assess the performance differences among the models. Our findings suggest that integrating the recurrence mechanism within the UNet architecture leads to a decline in the model’s effectiveness for our particular application. Full article

(This article belongs to the Section Medical Imaging)

► Show Figures

Figure 1

14 pages, 2317 KiB

Open AccessArticle

Enhanced U-Net with GridMask (EUGNet): A Novel Approach for Robotic Surgical Tool Segmentation

by Mostafa Daneshgar Rahbar and Seyed Ziae Mousavi Mojab

J. Imaging 2023, 9(12), 282; https://doi.org/10.3390/jimaging9120282 - 18 Dec 2023

Viewed by 1487

Abstract

This study proposed enhanced U-Net with GridMask (EUGNet) image augmentation techniques focused on pixel manipulation, emphasizing GridMask augmentation. This study introduces EUGNet, which incorporates GridMask augmentation to address U-Net’s limitations. EUGNet features a deep contextual encoder, residual connections, class-balancing loss, adaptive feature fusion, [...] Read more.

This study proposed enhanced U-Net with GridMask (EUGNet) image augmentation techniques focused on pixel manipulation, emphasizing GridMask augmentation. This study introduces EUGNet, which incorporates GridMask augmentation to address U-Net’s limitations. EUGNet features a deep contextual encoder, residual connections, class-balancing loss, adaptive feature fusion, GridMask augmentation module, efficient implementation, and multi-modal fusion. These innovations enhance segmentation accuracy and robustness, making it well-suited for medical image analysis. The GridMask algorithm is detailed, demonstrating its distinct approach to pixel elimination, enhancing model adaptability to occlusions and local features. A comprehensive dataset of robotic surgical scenarios and instruments is used for evaluation, showcasing the framework’s robustness. Specifically, there are improvements of 1.6 percentage points in balanced accuracy for the foreground, 1.7 points in intersection over union (IoU), and 1.7 points in mean Dice similarity coefficient (DSC). These improvements are highly significant and have a substantial impact on inference speed. The inference speed, which is a critical factor in real-time applications, has seen a noteworthy reduction. It decreased from 0.163 milliseconds for the U-Net without GridMask to 0.097 milliseconds for the U-Net with GridMask. Full article

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

► Show Figures

Figure 1

28 pages, 1488 KiB

Open AccessArticle

Measuring 3D Video Quality of Experience (QoE) Using A Hybrid Metric Based on Spatial Resolution and Depth Cues

by Sahin Coskun, Gokce Nur Yilmaz, Federica Battisti, Musaed Alhussein and Saiful Islam

J. Imaging 2023, 9(12), 281; https://doi.org/10.3390/jimaging9120281 - 18 Dec 2023

Viewed by 1688

Abstract

A three-dimensional (3D) video is a special video representation with an artificial stereoscopic vision effect that increases the depth perception of the viewers. The quality of a 3D video is generally measured based on the similarity to stereoscopic vision obtained with the human [...] Read more.

A three-dimensional (3D) video is a special video representation with an artificial stereoscopic vision effect that increases the depth perception of the viewers. The quality of a 3D video is generally measured based on the similarity to stereoscopic vision obtained with the human vision system (HVS). The reason for the usage of these high-cost and time-consuming subjective tests is due to the lack of an objective video Quality of Experience (QoE) evaluation method that models the HVS. In this paper, we propose a hybrid 3D-video QoE evaluation method based on spatial resolution associated with depth cues (i.e., motion information, blurriness, retinal-image size, and convergence). The proposed method successfully models the HVS by considering the 3D video parameters that directly affect depth perception, which is the most important element of stereoscopic vision. Experimental results show that the measurement of the 3D-video QoE by the proposed hybrid method outperforms the widely used existing methods. It is also found that the proposed method has a high correlation with the HVS. Consequently, the results suggest that the proposed hybrid method can be conveniently utilized for the 3D-video QoE evaluation, especially in real-time applications. Full article

(This article belongs to the Special Issue Modelling of Human Visual System in Image Processing)

► Show Figures

Figure 1

0 pages, 2118 KiB

Open AccessArticle

Convolutional Neural Network Model for Segmentation and Classification of Clear Cell Renal Cell Carcinoma Based on Multiphase CT Images

by Vlad-Octavian Bolocan, Mihaela Secareanu, Elena Sava, Cosmin Medar, Loredana Sabina Cornelia Manolescu, Alexandru-Ștefan Cătălin Rașcu, Maria Glencora Costache, George Daniel Radavoi, Robert-Andrei Dobran and Viorel Jinga

J. Imaging 2023, 9(12), 280; https://doi.org/10.3390/jimaging9120280 - 14 Dec 2023

Cited by 1 | Viewed by 1795 | Correction

Abstract

(1) Background: Computed tomography (CT) imaging challenges in diagnosing renal cell carcinoma (RCC) include distinguishing malignant from benign tissues and determining the likely subtype. The goal is to show the algorithm’s ability to improve renal cell carcinoma identification and treatment, improving patient outcomes. [...] Read more.

(1) Background: Computed tomography (CT) imaging challenges in diagnosing renal cell carcinoma (RCC) include distinguishing malignant from benign tissues and determining the likely subtype. The goal is to show the algorithm’s ability to improve renal cell carcinoma identification and treatment, improving patient outcomes. (2) Methods: This study uses the European Deep-Health toolkit’s Convolutional Neural Network with ECVL, (European Computer Vision Library), and EDDL, (European Distributed Deep Learning Library). Image segmentation utilized U-net architecture and classification with resnet101. The model’s clinical efficiency was assessed utilizing kidney, tumor, Dice score, and renal cell carcinoma categorization quality. (3) Results: The raw dataset contains 457 healthy right kidneys, 456 healthy left kidneys, 76 pathological right kidneys, and 84 pathological left kidneys. Preparing raw data for analysis was crucial to algorithm implementation. Kidney segmentation performance was 0.84, and tumor segmentation mean Dice score was 0.675 for the suggested model. Renal cell carcinoma classification was 0.885 accurate. (4) Conclusion and key findings: The present study focused on analyzing data from both healthy patients and diseased renal patients, with a particular emphasis on data processing. The method achieved a kidney segmentation accuracy of 0.84 and mean Dice scores of 0.675 for tumor segmentation. The system performed well in classifying renal cell carcinoma, achieving an accuracy of 0.885, results which indicates that the technique has the potential to improve the diagnosis of kidney pathology. Full article

► Show Figures

Figure 1

21 pages, 8234 KiB

Open AccessArticle

Coarse Alignment Methodology of Point Cloud Based on Camera Position/Orientation Estimation Model

by Suhong Yoo and Namhoon Kim

J. Imaging 2023, 9(12), 279; https://doi.org/10.3390/jimaging9120279 - 14 Dec 2023

Viewed by 1389

Abstract

This study presents a methodology for the coarse alignment of light detection and ranging (LiDAR) point clouds, which involves estimating the position and orientation of each station using the pinhole camera model and a position/orientation estimation algorithm. Ground control points are obtained using [...] Read more.

This study presents a methodology for the coarse alignment of light detection and ranging (LiDAR) point clouds, which involves estimating the position and orientation of each station using the pinhole camera model and a position/orientation estimation algorithm. Ground control points are obtained using LiDAR camera images and the point clouds are obtained from the reference station. The estimated position and orientation vectors are used for point cloud registration. To evaluate the accuracy of the results, the positions of the LiDAR and the target were measured using a total station, and a comparison was carried out with the results of semi-automatic registration. The proposed methodology yielded an estimated mean LiDAR position error of 0.072 m, which was similar to the semi-automatic registration value of 0.070 m. When the point clouds of each station were registered using the estimated values, the mean registration accuracy was 0.124 m, while the semi-automatic registration accuracy was 0.072 m. The high accuracy of semi-automatic registration is due to its capability for performing both coarse alignment and refined registration. The comparison between the point cloud with refined alignment using the proposed methodology and the point-to-point distance analysis revealed that the average distance was measured at 0.0117 m. Moreover, 99% of the points exhibited distances within the range of 0.0696 m. Full article

(This article belongs to the Special Issue Visual Localization—Volume II)

► Show Figures

Figure 1

2 pages, 161 KiB

Open AccessEditorial

Editorial for the Special Issue on Industrial Machine Learning Applications

by Paolo Rota, Miguel Angel Guevara Lopez and Francesco Setti

J. Imaging 2023, 9(12), 278; https://doi.org/10.3390/jimaging9120278 - 14 Dec 2023

Viewed by 1239

Abstract

In the rapidly evolving field of industrial machine learning, this Special Issue on Industrial Machine Learning Applications aims to shed light on the innovative strides made toward more intelligent, more efficient, and adaptive industrial processes [...] Full article

(This article belongs to the Special Issue Industrial Machine Learning Application)

33 pages, 3096 KiB

Open AccessArticle

Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets

by Jiajun Zhang, Georgina Cosma, Sarah Bugby and Jason Watkins

J. Imaging 2023, 9(12), 277; https://doi.org/10.3390/jimaging9120277 - 13 Dec 2023

Viewed by 1471

Abstract

Image retrieval is the process of searching and retrieving images from a datastore based on their visual content and features. Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or healthcare images by extracting features from the images, [...] Read more.

Image retrieval is the process of searching and retrieving images from a datastore based on their visual content and features. Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or healthcare images by extracting features from the images, such as deep features, colour-based features, shape-based features, and local features. This has applications across a spectrum of industries, including fault inspection, disease diagnosis, and maintenance prediction. This paper proposes an image retrieval framework to search for images containing similar irregular patterns by extracting a set of morphological features (DefChars) from images. The datasets employed in this paper contain wind turbine blade images with defects, chest computerised tomography scans with COVID-19 infections, heatsink images with defects, and lake ice images. The proposed framework was evaluated with different feature extraction methods (DefChars, resized raw image, local binary pattern, and scale-invariant feature transforms) and distance metrics to determine the most efficient parameters in terms of retrieval performance across datasets. The retrieval results show that the proposed framework using the DefChars and the Manhattan distance metric achieves a mean average precision of 80% and a low standard deviation of ±0.09 across classes of irregular patterns, outperforming alternative feature–metric combinations across all datasets. Our proposed ImR framework performed better (by 8.71%) than Super Global, a state-of-the-art deep-learning-based image retrieval approach across all datasets. Full article

(This article belongs to the Special Issue Advances and Challenges in Multimodal Machine Learning 2nd Edition)

► Show Figures

Figure 1

16 pages, 483 KiB

Open AccessArticle

ViTSTR-Transducer: Cross-Attention-Free Vision Transformer Transducer for Scene Text Recognition

by Rina Buoy, Masakazu Iwamura, Sovila Srun and Koichi Kise

J. Imaging 2023, 9(12), 276; https://doi.org/10.3390/jimaging9120276 - 13 Dec 2023

Viewed by 1727

Abstract

Attention-based encoder–decoder scene text recognition (STR) architectures have been proven effective in recognizing text in the real world, thanks to their ability to learn an internal language model. Nevertheless, the cross-attention operation that is used to align visual and linguistic features during decoding [...] Read more.

Attention-based encoder–decoder scene text recognition (STR) architectures have been proven effective in recognizing text in the real world, thanks to their ability to learn an internal language model. Nevertheless, the cross-attention operation that is used to align visual and linguistic features during decoding is computationally expensive, especially in low-resource environments. To address this bottleneck, we propose a cross-attention-free STR framework that still learns a language model. The framework we propose is ViTSTR-Transducer, which draws inspiration from ViTSTR, a vision transformer (ViT)-based method designed for STR and the recurrent neural network transducer (RNN-T) initially introduced for speech recognition. The experimental results show that our ViTSTR-Transducer models outperform the baseline attention-based models in terms of the required decoding floating point operations (FLOPs) and latency while achieving a comparable level of recognition accuracy. Compared with the baseline context-free ViTSTR models, our proposed models achieve superior recognition accuracy. Furthermore, compared with the recent state-of-the-art (SOTA) methods, our proposed models deliver competitive results. Full article

► Show Figures

Figure 1

38 pages, 20530 KiB

Open AccessReview

A Systematic Review of Recent Deep Learning Approaches for 3D Human Pose Estimation

by Amal El Kaid and Karim Baïna

J. Imaging 2023, 9(12), 275; https://doi.org/10.3390/jimaging9120275 - 12 Dec 2023

Viewed by 3238

Abstract

Three-dimensional human pose estimation has made significant advancements through the integration of deep learning techniques. This survey provides a comprehensive review of recent 3D human pose estimation methods, with a focus on monocular images, videos, and multi-view cameras. Our approach stands out through [...] Read more.

Three-dimensional human pose estimation has made significant advancements through the integration of deep learning techniques. This survey provides a comprehensive review of recent 3D human pose estimation methods, with a focus on monocular images, videos, and multi-view cameras. Our approach stands out through a systematic literature review methodology, ensuring an up-to-date and meticulous overview. Unlike many existing surveys that categorize approaches based on learning paradigms, our survey offers a fresh perspective, delving deeper into the subject. For image-based approaches, we not only follow existing categorizations but also introduce and compare significant 2D models. Additionally, we provide a comparative analysis of these methods, enhancing the understanding of image-based pose estimation techniques. In the realm of video-based approaches, we categorize them based on the types of models used to capture inter-frame information. Furthermore, in the context of multi-person pose estimation, our survey uniquely differentiates between approaches focusing on relative poses and those addressing absolute poses. Our survey aims to serve as a pivotal resource for researchers, highlighting state-of-the-art deep learning strategies and identifying promising directions for future exploration in 3D human pose estimation. Full article

(This article belongs to the Special Issue Image Processing and Computer Vision: Algorithms and Applications)

► Show Figures

Figure 1

16 pages, 6174 KiB

Open AccessSystematic Review

Diagnostic Accuracy of PET with Different Radiotracers versus Bone Scintigraphy for Detecting Bone Metastases of Breast Cancer: A Systematic Review and a Meta-Analysis

by Maryam Zamanian, Giorgio Treglia and Iraj Abedi

J. Imaging 2023, 9(12), 274; https://doi.org/10.3390/jimaging9120274 - 08 Dec 2023

Viewed by 1575

Abstract

Due to the importance of correct and timely diagnosis of bone metastases in advanced breast cancer (BrC), we performed a meta-analysis evaluating the diagnostic accuracy of [¹⁸F]FDG, or Na[¹⁸F]F PET, PET(/CT), and (/MRI) versus [^99mTc]Tc-diphosphonates bone scintigraphy [...] Read more.

Due to the importance of correct and timely diagnosis of bone metastases in advanced breast cancer (BrC), we performed a meta-analysis evaluating the diagnostic accuracy of [¹⁸F]FDG, or Na[¹⁸F]F PET, PET(/CT), and (/MRI) versus [^99mTc]Tc-diphosphonates bone scintigraphy (BS). The PubMed, Embase, Scopus, and Scholar electronic databases were searched. The results of the selected studies were analyzed using pooled sensitivity and specificity, diagnostic odds ratio (DOR), positive–negative likelihood ratio (LR⁺–LR⁻), and summary receiver–operating characteristic (SROC) curves. Eleven studies including 753 BrC patients were included in the meta-analysis. The patient-based pooled values of sensitivity, specificity, and area under the SROC curve (AUC) for BS (with 95% confidence interval values) were 90% (86–93), 91% (87–94), and 0.93, respectively. These indices for [¹⁸F]FDG PET(/CT) were 92% (88–95), 99% (96–100), and 0.99, respectively, and for Na[¹⁸F]F PET(/CT) were 96% (90–99), 81% (72–88), and 0.99, respectively. BS has good diagnostic performance in detecting BrC bone metastases. However, due to the higher and balanced sensitivity and specificity of [¹⁸F]FDG PET(/CT) compared to BS and Na[¹⁸F]F PET(/CT), and its advantage in evaluating extra-skeletal lesions, [¹⁸F]FDG PET(/CT) should be the preferred multimodal imaging method for evaluating bone metastases of BrC, if available. Full article

(This article belongs to the Section Medical Imaging)

► Show Figures

Figure 1

13 pages, 4444 KiB

Open AccessArticle

Go-Game Image Recognition Based on Improved Pix2pix

by Yanxia Zheng and Xiyuan Qian

J. Imaging 2023, 9(12), 273; https://doi.org/10.3390/jimaging9120273 - 07 Dec 2023

Viewed by 1423

Abstract

Go is a game that can be won or lost based on the number of intersections surrounded by black or white pieces. The traditional method is a manual counting method, which is time-consuming and error-prone. In addition, the generalization of the current Go-image-recognition [...] Read more.

Go is a game that can be won or lost based on the number of intersections surrounded by black or white pieces. The traditional method is a manual counting method, which is time-consuming and error-prone. In addition, the generalization of the current Go-image-recognition methods is poor, and accuracy needs to be further improved. To solve these problems, a Go-game image recognition based on an improved pix2pix was proposed. Firstly, a channel-coordinate mixed-attention (CCMA) mechanism was designed by combining channel attention and coordinate attention effectively; therefore, the model could learn the target feature information. Secondly, in order to obtain the long-distance contextual information, a deep dilated-convolution (DDC) module was proposed, which densely linked the dilated convolution with different dilated rates. The experimental results showed that compared with other existing Go-image-recognition methods, such as DenseNet, VGG-16, and Yolo v5, the proposed method could effectively improve the generalization ability and accuracy of a Go-image-recognition model, and the average accuracy rate was over 99.99%. Full article

(This article belongs to the Topic Applied Computer Vision and Pattern Recognition: 2nd Volume)

► Show Figures

Figure 1

13 pages, 5882 KiB

Open AccessArticle

Noise Reduction for a Virtual Grid Using a Generative Adversarial Network in Breast X-ray Images

by Sewon Lim, Hayun Nam, Hyemin Shin, Sein Jeong, Kyuseok Kim and Youngjin Lee

J. Imaging 2023, 9(12), 272; https://doi.org/10.3390/jimaging9120272 - 07 Dec 2023

Viewed by 1508

Abstract

In this study, we aimed to address the issue of noise amplification after scatter correction when using a virtual grid in breast X-ray images. To achieve this, we suggested an algorithm for estimating noise level and developed a noise reduction algorithm based on [...] Read more.

In this study, we aimed to address the issue of noise amplification after scatter correction when using a virtual grid in breast X-ray images. To achieve this, we suggested an algorithm for estimating noise level and developed a noise reduction algorithm based on generative adversarial networks (GANs). Synthetic scatter in breast X-ray images were collected using Sizgraphy equipment and scatter correction was performed using dedicated software. After scatter correction, we determined the level of noise using noise-level function plots and trained a GAN using 42 noise combinations. Subsequently, we obtained the resulting images and quantitatively evaluated their quality by measuring the contrast-to-noise ratio (CNR), coefficient of variance (COV), and normalized noise–power spectrum (NNPS). The evaluation revealed an improvement in the CNR by approximately 2.80%, an enhancement in the COV by 12.50%, and an overall improvement in the NNPS across all frequency ranges. In conclusion, the application of our GAN-based noise reduction algorithm effectively reduced noise and demonstrated the acquisition of improved-quality breast X-ray images. Full article

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

► Show Figures

Figure 1

13 pages, 1782 KiB

Open AccessBrief Report

Brain Age Prediction Using 2D Projections Based on Higher-Order Statistical Moments and Eigenslices from 3D Magnetic Resonance Imaging Volumes

by Johan Jönemo and Anders Eklund

J. Imaging 2023, 9(12), 271; https://doi.org/10.3390/jimaging9120271 - 06 Dec 2023

Viewed by 1417

Abstract

Brain age prediction from 3D MRI volumes using deep learning has recently become a popular research topic, as brain age has been shown to be an important biomarker. Training deep networks can be very computationally demanding for large datasets like the U.K. Biobank [...] Read more.

Brain age prediction from 3D MRI volumes using deep learning has recently become a popular research topic, as brain age has been shown to be an important biomarker. Training deep networks can be very computationally demanding for large datasets like the U.K. Biobank (currently 29,035 subjects). In our previous work, it was demonstrated that using a few 2D projections (mean and standard deviation along three axes) instead of each full 3D volume leads to much faster training at the cost of a reduction in prediction accuracy. Here, we investigated if another set of 2D projections, based on higher-order statistical central moments and eigenslices, leads to a higher accuracy. Our results show that higher-order moments do not lead to a higher accuracy, but that eigenslices provide a small improvement. We also show that an ensemble of such models provides further improvement. Full article

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

► Show Figures

Figure 1

9 pages, 2411 KiB

Open AccessBrief Report

WindowNet: Learnable Windows for Chest X-ray Classification

by Alessandro Wollek, Sardi Hyska, Bastian Sabel, Michael Ingrisch and Tobias Lasser

J. Imaging 2023, 9(12), 270; https://doi.org/10.3390/jimaging9120270 - 06 Dec 2023

Viewed by 1789

Abstract

Public chest X-ray (CXR) data sets are commonly compressed to a lower bit depth to reduce their size, potentially hiding subtle diagnostic features. In contrast, radiologists apply a windowing operation to the uncompressed image to enhance such subtle features. While it has been [...] Read more.

Public chest X-ray (CXR) data sets are commonly compressed to a lower bit depth to reduce their size, potentially hiding subtle diagnostic features. In contrast, radiologists apply a windowing operation to the uncompressed image to enhance such subtle features. While it has been shown that windowing improves classification performance on computed tomography (CT) images, the impact of such an operation on CXR classification performance remains unclear. In this study, we show that windowing strongly improves the CXR classification performance of machine learning models and propose WindowNet, a model that learns multiple optimal window settings. Our model achieved an average AUC score of 0.812 compared with the 0.759 score of a commonly used architecture without windowing capabilities on the MIMIC data set. Full article

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

► Show Figures

Figure 1

13 pages, 1939 KiB

Open AccessArticle

The Influence of Substrate on the Optical Properties of Gold Nanoslits

by Ammara Ammara, Ghulam Abbas, Francesco V. Pepe, Muhammad Afzaal, Muhammad Qamar and Abdul Ghuffar

J. Imaging 2023, 9(12), 269; https://doi.org/10.3390/jimaging9120269 - 03 Dec 2023

Viewed by 1353

Abstract

Nanoslits have various applications, including localized surface plasmon resonance (LSPR)-based nanodevices, optical biosensors, superfocusing, high-efficiency refractive index sensors and chip-based protein detection. In this study, the effect of substrates on the optical properties of gold nanoslits placed in free space is discussed; for [...] Read more.

Nanoslits have various applications, including localized surface plasmon resonance (LSPR)-based nanodevices, optical biosensors, superfocusing, high-efficiency refractive index sensors and chip-based protein detection. In this study, the effect of substrates on the optical properties of gold nanoslits placed in free space is discussed; for this purpose, glass BK7 and Al

_{2}

O

_{3}

are used as substrates and the wavelength of incident light is supposed to be 650 nm. The optical properties, power flow and electric field enhancement for gold nanoslits are investigated by using the finite element method (FEM) in COMSOL Multiphysics software. The effect of polarization of an incident electromagnetic wave as it propagates from a gold nanoslit is also analyzed. As special case, the effect of glass and alumina substrate on magnetic field, power flow and electric field enhancement is discussed. The goal of this research is to investigate the phenomenon of power flow and electric field enhancement. The study of power flow in gold nanoslits provides valuable insights into the behavior of light at the nanoscale and offers opportunities for developing novel applications in the field of nanophotonics and plasmonics. The consequences of this study show the significance of gold nanoslits as optical nanosensors. Full article

► Show Figures

Figure 1

14 pages, 1335 KiB

Open AccessArticle

Automated Coronary Artery Tracking with a Voronoi-Based 3D Centerline Extraction Algorithm

by Rodrigo Dalvit Carvalho da Silva, Ramin Soltanzadeh and Chase R. Figley

J. Imaging 2023, 9(12), 268; https://doi.org/10.3390/jimaging9120268 - 01 Dec 2023

Viewed by 1541

Abstract

Coronary artery disease is one of the leading causes of death worldwide, and medical imaging methods such as coronary artery computed tomography are vitally important in its detection. More recently, various computational approaches have been proposed to automatically extract important artery coronary features [...] Read more.

Coronary artery disease is one of the leading causes of death worldwide, and medical imaging methods such as coronary artery computed tomography are vitally important in its detection. More recently, various computational approaches have been proposed to automatically extract important artery coronary features (e.g., vessel centerlines, cross-sectional areas along vessel branches, etc.) that may ultimately be able to assist with more accurate and timely diagnoses. The current study therefore validated and benchmarked a recently developed automated 3D centerline extraction method for coronary artery centerline tracking using synthetically segmented coronary artery models based on the widely used Rotterdam Coronary Artery Algorithm Evaluation Framework (RCAAEF) training dataset. Based on standard accuracy metrics and the ground truth centerlines of all 32 coronary vessel branches in the RCAAEF training dataset, this 3D divide and conquer Voronoi diagram method performed exceptionally well, achieving an average overlap accuracy (OV) of 99.97%, overlap until first error (OF) of 100%, overlap of the clinically relevant portion of the vessel (OT) of 99.98%, and an average error distance inside the vessels (AI) of only 0.13 mm. Accuracy was also found to be exceptionally for all four coronary artery sub-types, with average OV values of 99.99% for right coronary arteries, 100% for left anterior descending arteries, 99.96% for left circumflex arteries, and 100% for large side-branch vessels. These results validate that the proposed method can be employed to quickly, accurately, and automatically extract 3D centerlines from segmented coronary arteries, and indicate that it is likely worthy of further exploration given the importance of this topic. Full article

(This article belongs to the Special Issue Image Processing and Computer Vision: Algorithms and Applications)

► Show Figures

Figure 1

15 pages, 3531 KiB

Open AccessArticle

Patient Dose Estimation in Computed Tomography-Guided Biopsy Procedures

by Evangelia Siomou, Dimitrios K. Filippiadis, Efstathios P. Efstathopoulos, Ioannis Antonakos and George S. Panayiotakis

J. Imaging 2023, 9(12), 267; https://doi.org/10.3390/jimaging9120267 - 30 Nov 2023

Viewed by 2047

Abstract

This study establishes typical Diagnostic Reference Levels (DRL) values and assesses patient doses in computed tomography (CT)-guided biopsy procedures. The Effective Dose (ED), Entrance Skin Dose (ESD), and Size-Specific Dose Estimate (SSDE) were calculated using the relevant literature-derived conversion factors. A retrospective analysis [...] Read more.

This study establishes typical Diagnostic Reference Levels (DRL) values and assesses patient doses in computed tomography (CT)-guided biopsy procedures. The Effective Dose (ED), Entrance Skin Dose (ESD), and Size-Specific Dose Estimate (SSDE) were calculated using the relevant literature-derived conversion factors. A retrospective analysis of 226 CT-guided biopsies across five categories (Iliac bone, liver, lung, mediastinum, and para-aortic lymph nodes) was conducted. Typical DRL values were computed as median distributions, following guidelines from the International Commission on Radiological Protection (ICRP) Publication 135. DRLs for helical mode CT acquisitions were set at 9.7 mGy for Iliac bone, 8.9 mGy for liver, 8.8 mGy for lung, 7.9 mGy for mediastinal mass, and 9 mGy for para-aortic lymph nodes biopsies. In contrast, DRLs for biopsy acquisitions were 7.3 mGy, 7.7 mGy, 5.6 mGy, 5.6 mGy, and 7.4 mGy, respectively. Median SSDE values varied from 7.6 mGy to 10 mGy for biopsy acquisitions and from 11.3 mGy to 12.6 mGy for helical scans. Median ED values ranged from 1.6 mSv to 5.7 mSv for biopsy scans and from 3.9 mSv to 9.3 mSv for helical scans. The study highlights the significance of using DRLs for optimizing CT-guided biopsy procedures, revealing notable variations in radiation exposure between helical scans covering entire anatomical regions and localized biopsy acquisitions. Full article

(This article belongs to the Section Medical Imaging)

► Show Figures

Figure 1

19 pages, 30457 KiB

Open AccessArticle

YOLO-PAM: Parasite-Attention-Based Model for Efficient Malaria Detection

by Luca Zedda, Andrea Loddo and Cecilia Di Ruberto

J. Imaging 2023, 9(12), 266; https://doi.org/10.3390/jimaging9120266 - 30 Nov 2023

Viewed by 1969

Abstract

Malaria is a potentially fatal infectious disease caused by the Plasmodium parasite. The mortality rate can be significantly reduced if the condition is diagnosed and treated early. However, in many underdeveloped countries, the detection of malaria parasites from blood smears is still performed [...] Read more.

Malaria is a potentially fatal infectious disease caused by the Plasmodium parasite. The mortality rate can be significantly reduced if the condition is diagnosed and treated early. However, in many underdeveloped countries, the detection of malaria parasites from blood smears is still performed manually by experienced hematologists. This process is time-consuming and error-prone. In recent years, deep-learning-based object-detection methods have shown promising results in automating this task, which is critical to ensure diagnosis and treatment in the shortest possible time. In this paper, we propose a novel Transformer- and attention-based object-detection architecture designed to detect malaria parasites with high efficiency and precision, focusing on detecting several parasite sizes. The proposed method was tested on two public datasets, namely MP-IDB and IML. The evaluation results demonstrated a mean average precision exceeding 83.6% on distinct Plasmodium species within MP-IDB and reaching nearly 60% on IML. These findings underscore the effectiveness of our proposed architecture in automating malaria parasite detection, offering a potential breakthrough in expediting diagnosis and treatment processes. Full article

(This article belongs to the Section Medical Imaging)

► Show Figures

Figure 1

21 pages, 5789 KiB

Open AccessArticle

SCOLIONET: An Automated Scoliosis Cobb Angle Quantification Using Enhanced X-ray Images and Deep Learning Models

by Renato R. Maaliw III

J. Imaging 2023, 9(12), 265; https://doi.org/10.3390/jimaging9120265 - 30 Nov 2023

Viewed by 1823

Abstract

The advancement of medical prognoses hinges on the delivery of timely and reliable assessments. Conventional methods of assessments and diagnosis, often reliant on human expertise, lead to inconsistencies due to professionals’ subjectivity, knowledge, and experience. To address these problems head-on, we harnessed artificial [...] Read more.

The advancement of medical prognoses hinges on the delivery of timely and reliable assessments. Conventional methods of assessments and diagnosis, often reliant on human expertise, lead to inconsistencies due to professionals’ subjectivity, knowledge, and experience. To address these problems head-on, we harnessed artificial intelligence’s power to introduce a transformative solution. We leveraged convolutional neural networks to engineer our SCOLIONET architecture, which can accurately identify Cobb angle measurements. Empirical testing on our pipeline demonstrated a mean segmentation accuracy of 97.50% (Sorensen–Dice coefficient) and 96.30% (Intersection over Union), indicating the model’s proficiency in outlining vertebrae. The level of quantification accuracy was attributed to the state-of-the-art design of the atrous spatial pyramid pooling to better segment images. We also compared physician’s manual evaluations against our machine driven measurements to validate our approach’s practicality and reliability further. The results were remarkable, with a p-value (t-test) of 0.1713 and an average acceptable deviation of 2.86 degrees, suggesting insignificant difference between the two methods. Our work holds the premise of enabling medical practitioners to expedite scoliosis examination swiftly and consistently in improving and advancing the quality of patient care. Full article

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

► Show Figures

Graphical abstract

16 pages, 4419 KiB

Open AccessArticle

The Dose Optimization and Evaluation of Image Quality in the Adult Brain Protocols of Multi-Slice Computed Tomography: A Phantom Study

by Thawatchai Prabsattroo, Kanokpat Wachirasirikul, Prasit Tansangworn, Puengjai Punikhom and Waraporn Sudchai

J. Imaging 2023, 9(12), 264; https://doi.org/10.3390/jimaging9120264 - 28 Nov 2023

Viewed by 2021

Abstract

Computed tomography examinations have caused high radiation doses for patients, especially for CT scans of the brain. This study aimed to optimize the radiation dose and image quality in adult brain CT protocols. Images were acquired using a Catphan 700 phantom. Radiation doses [...] Read more.

Computed tomography examinations have caused high radiation doses for patients, especially for CT scans of the brain. This study aimed to optimize the radiation dose and image quality in adult brain CT protocols. Images were acquired using a Catphan 700 phantom. Radiation doses were recorded as CTDI_vol and dose length product (DLP). CT brain protocols were optimized by varying parameters such as kVp, mAs, signal-to-noise ratio (SNR) level, and Clearview iterative reconstruction (IR). The image quality was also evaluated using AutoQA Plus v.1.8.7.0 software. CT number accuracy and linearity had a robust positive correlation with the linear attenuation coefficient (µ) and showed more inaccurate CT numbers when using 80 kVp. The modulation transfer function (MTF) showed a higher value in 100 and 120 kVp protocols (p < 0.001), while high-contrast spatial resolution showed a higher value in 80 and 100 kVp protocols (p < 0.001). Low-contrast detectability and the contrast-to-noise ratio (CNR) tended to increase when using high mAs, SNR, and the Clearview IR protocol. Noise decreased when using a high radiation dose and a high percentage of Clearview IR. CTDI_vol and DLP were increased with increasing kVp, mAs, and SNR levels, while the increasing percentage of Clearview did not affect the radiation dose. Optimized protocols, including radiation dose and image quality, should be evaluated to preserve diagnostic capability. The recommended parameter settings include kVp set between 100 and 120 kVp, mAs ranging from 200 to 300 mAs, SNR level within the range of 0.7–1.0, and an iterative reconstruction value of 30% Clearview to 60% or higher. Full article

(This article belongs to the Special Issue Brain Image Computation for Diagnosis and Treatment)

► Show Figures

Figure 1

14 pages, 2634 KiB

Open AccessArticle

Innovative Bacterial Colony Detection: Leveraging Multi-Feature Selection with the Improved Salp Swarm Algorithm

by Ahmad Ihsan, Khairul Muttaqin, Rahmatul Fajri, Mursyidah Mursyidah and Islam Md Rizwanul Fattah

J. Imaging 2023, 9(12), 263; https://doi.org/10.3390/jimaging9120263 - 28 Nov 2023

Viewed by 1385

Abstract

In this paper, we introduce a new and advanced multi-feature selection method for bacterial classification that uses the salp swarm algorithm (SSA). We improve the SSA’s performance by using opposition-based learning (OBL) and a local search algorithm (LSA). The proposed method has three [...] Read more.

In this paper, we introduce a new and advanced multi-feature selection method for bacterial classification that uses the salp swarm algorithm (SSA). We improve the SSA’s performance by using opposition-based learning (OBL) and a local search algorithm (LSA). The proposed method has three main stages, which automate the categorization of bacteria based on their unique characteristics. The method uses a multi-feature selection approach augmented by an enhanced version of the SSA. The enhancements include using OBL to increase population diversity during the search process and LSA to address local optimization problems. The improved salp swarm algorithm (ISSA) is designed to optimize multi-feature selection by increasing the number of selected features and improving classification accuracy. We compare the ISSA’s performance to that of several other algorithms on ten different test datasets. The results show that the ISSA outperforms the other algorithms in terms of classification accuracy on three datasets with 19 features, achieving an accuracy of 73.75%. Additionally, the ISSA excels at determining the optimal number of features and producing a better fit value, with a classification error rate of 0.249. Therefore, the ISSA method is expected to make a significant contribution to solving feature selection problems in bacterial analysis. Full article

(This article belongs to the Special Issue Image Processing and Computer Vision: Algorithms and Applications)

► Show Figures

Figure 1

12 pages, 3092 KiB

Open AccessArticle

Sign Language Dataset for Automatic Motion Generation

by María Villa-Monedero, Manuel Gil-Martín, Daniel Sáez-Trigueros, Andrzej Pomirski and Rubén San-Segundo

J. Imaging 2023, 9(12), 262; https://doi.org/10.3390/jimaging9120262 - 27 Nov 2023

Cited by 1 | Viewed by 2116

Abstract

Several sign language datasets are available in the literature. Most of them are designed for sign language recognition and translation. This paper presents a new sign language dataset for automatic motion generation. This dataset includes phonemes for each sign (specified in HamNoSys, a [...] Read more.

Several sign language datasets are available in the literature. Most of them are designed for sign language recognition and translation. This paper presents a new sign language dataset for automatic motion generation. This dataset includes phonemes for each sign (specified in HamNoSys, a transcription system developed at the University of Hamburg, Hamburg, Germany) and the corresponding motion information. The motion information includes sign videos and the sequence of extracted landmarks associated with relevant points of the skeleton (including face, arms, hands, and fingers). The dataset includes signs from three different subjects in three different positions, performing 754 signs including the entire alphabet, numbers from 0 to 100, numbers for hour specification, months, and weekdays, and the most frequent signs used in Spanish Sign Language (LSE). In total, there are 6786 videos and their corresponding phonemes (HamNoSys annotations). From each video, a sequence of landmarks was extracted using MediaPipe. The dataset allows training an automatic system for motion generation from sign language phonemes. This paper also presents preliminary results in motion generation from sign phonemes obtaining a Dynamic Time Warping distance per frame of 0.37. Full article

► Show Figures

Figure 1

14 pages, 2879 KiB

Open AccessArticle

Extended Analysis of Raman Spectra Using Artificial Intelligence Techniques for Colorectal Abnormality Classification

by Dimitris Kalatzis, Ellas Spyratou, Maria Karnachoriti, Maria Anthi Kouri, Ioannis Stathopoulos, Nikolaos Danias, Nikolaos Arkadopoulos, Spyros Orfanoudakis, Ioannis Seimenis, Athanassios G. Kontos and Efstathios P. Efstathopoulos

J. Imaging 2023, 9(12), 261; https://doi.org/10.3390/jimaging9120261 - 24 Nov 2023

Cited by 1 | Viewed by 1434

Abstract

Raman spectroscopy (RS) techniques are attracting attention in the medical field as a promising tool for real-time biochemical analyses. The integration of artificial intelligence (AI) algorithms with RS has greatly enhanced its ability to accurately classify spectral data in vivo. This combination has [...] Read more.

Raman spectroscopy (RS) techniques are attracting attention in the medical field as a promising tool for real-time biochemical analyses. The integration of artificial intelligence (AI) algorithms with RS has greatly enhanced its ability to accurately classify spectral data in vivo. This combination has opened up new possibilities for precise and efficient analysis in medical applications. In this study, healthy and cancerous specimens from 22 patients who underwent open colorectal surgery were collected. By using these spectral data, we investigate an optimal preprocessing pipeline for statistical analysis using AI techniques. This exploration entails proposing preprocessing methods and algorithms to enhance classification outcomes. The research encompasses a thorough ablation study comparing machine learning and deep learning algorithms toward the advancement of the clinical applicability of RS. The results indicate substantial accuracy improvements using techniques like baseline correction, L2 normalization, filtering, and PCA, yielding an overall accuracy enhancement of 15.8%. In comparing various algorithms, machine learning models, such as XGBoost and Random Forest, demonstrate effectiveness in classifying both normal and abnormal tissues. Similarly, deep learning models, such as 1D-Resnet and particularly the 1D-CNN model, exhibit superior performance in classifying abnormal cases. This research contributes valuable insights into the integration of AI in medical diagnostics and expands the potential of RS methods for achieving accurate malignancy classification. Full article

(This article belongs to the Special Issue Statistical Biomedical Signal and Image Processing and Understanding: 2nd Edition)

► Show Figures

Figure 1

20 pages, 44749 KiB

Open AccessArticle

Impact of ISP Tuning on Object Detection

by Dara Molloy, Brian Deegan, Darragh Mullins, Enda Ward, Jonathan Horgan, Ciaran Eising, Patrick Denny, Edward Jones and Martin Glavin

J. Imaging 2023, 9(12), 260; https://doi.org/10.3390/jimaging9120260 - 24 Nov 2023

Cited by 1 | Viewed by 3157

Abstract

In advanced driver assistance systems (ADAS) or autonomous vehicle research, acquiring semantic information about the surrounding environment generally relies heavily on camera-based object detection. Image signal processors (ISPs) in cameras are generally tuned for human perception. In most cases, ISP parameters are selected [...] Read more.

In advanced driver assistance systems (ADAS) or autonomous vehicle research, acquiring semantic information about the surrounding environment generally relies heavily on camera-based object detection. Image signal processors (ISPs) in cameras are generally tuned for human perception. In most cases, ISP parameters are selected subjectively and the resulting image differs depending on the individual who tuned it. While the installation of cameras on cars started as a means of providing a view of the vehicle’s environment to the driver, cameras are increasingly becoming part of safety-critical object detection systems for ADAS. Deep learning-based object detection has become prominent, but the effect of varying the ISP parameters has an unknown performance impact. In this study, we analyze the performance of 14 popular object detection models in the context of changes in the ISP parameters. We consider eight ISP blocks: demosaicing, gamma, denoising, edge enhancement, local tone mapping, saturation, contrast, and hue angle. We investigate two raw datasets, PASCALRAW and a custom raw dataset collected from an advanced driver assistance system (ADAS) perspective. We found that varying from a default ISP degrades the object detection performance and that the models differ in sensitivity to varying ISP parameters. Finally, we propose a novel methodology that increases object detection model robustness via ISP variation data augmentation. Full article

(This article belongs to the Special Issue Image Processing and Computer Vision: Algorithms and Applications)

► Show Figures

Figure 1

16 pages, 500 KiB

Open AccessArticle

CL3: Generalization of Contrastive Loss for Lifelong Learning

by Kaushik Roy, Christian Simon, Peyman Moghadam and Mehrtash Harandi

J. Imaging 2023, 9(12), 259; https://doi.org/10.3390/jimaging9120259 - 23 Nov 2023

Viewed by 1465

Abstract

Lifelong learning portrays learning gradually in nonstationary environments and emulates the process of human learning, which is efficient, robust, and able to learn new concepts incrementally from sequential experience. To equip neural networks with such a capability, one needs to overcome the problem [...] Read more.

Lifelong learning portrays learning gradually in nonstationary environments and emulates the process of human learning, which is efficient, robust, and able to learn new concepts incrementally from sequential experience. To equip neural networks with such a capability, one needs to overcome the problem of catastrophic forgetting, the phenomenon of forgetting past knowledge while learning new concepts. In this work, we propose a novel knowledge distillation algorithm that makes use of contrastive learning to help a neural network to preserve its past knowledge while learning from a series of tasks. Our proposed generalized form of contrastive distillation strategy tackles catastrophic forgetting of old knowledge, and minimizes semantic drift by maintaining a similar embedding space, as well as ensures compactness in feature distribution to accommodate novel tasks in a current model. Our comprehensive study shows that our method achieves improved performances in the challenging class-incremental, task-incremental, and domain-incremental learning for supervised scenarios. Full article

(This article belongs to the Topic Computer Vision and Image Processing)

► Show Figures

Figure 1

21 pages, 4044 KiB

Open AccessArticle

A Point-Cloud Segmentation Network Based on SqueezeNet and Time Series for Plants

by Xingshuo Peng, Keyuan Wang, Zelin Zhang, Nan Geng and Zhiyi Zhang

J. Imaging 2023, 9(12), 258; https://doi.org/10.3390/jimaging9120258 - 23 Nov 2023

Cited by 2 | Viewed by 1899

Abstract

The phenotyping of plant growth enriches our understanding of intricate genetic characteristics, paving the way for advancements in modern breeding and precision agriculture. Within the domain of phenotyping, segmenting 3D point clouds of plant organs is the basis of extracting plant phenotypic parameters. [...] Read more.

The phenotyping of plant growth enriches our understanding of intricate genetic characteristics, paving the way for advancements in modern breeding and precision agriculture. Within the domain of phenotyping, segmenting 3D point clouds of plant organs is the basis of extracting plant phenotypic parameters. In this study, we introduce a novel method for point-cloud downsampling that adeptly mitigates the challenges posed by sample imbalances. In subsequent developments, we architect a deep learning framework founded on the principles of SqueezeNet for the segmentation of plant point clouds. In addition, we also use the time series as input variables, which effectively improves the segmentation accuracy of the network. Based on semantic segmentation, the MeanShift algorithm is employed to execute instance segmentation on the point-cloud data of crops. In semantic segmentation, the average Precision, Recall, F1-score, and IoU of maize reached 99.35%, 99.26%, 99.30%, and 98.61%, and the average Precision, Recall, F1-score, and IoU of tomato reached 97.98%, 97.92%, 97.95%, and 95.98%. In instance segmentation, the accuracy of maize and tomato reached 98.45% and 96.12%. This research holds the potential to advance the fields of plant phenotypic extraction, ideotype selection, and precision agriculture. Full article

(This article belongs to the Special Issue Image Processing and Computer Vision: Algorithms and Applications)

► Show Figures

Figure 1

11 pages, 757 KiB

Open AccessArticle

Internal Morphology of Mandibular Second Premolars Using Micro-Computed Tomography

by Thomas Gerhard Wolf, Samuel Basmaci, Sven Schumann and Andrea Lisa Waber

J. Imaging 2023, 9(12), 257; https://doi.org/10.3390/jimaging9120257 - 23 Nov 2023

Cited by 1 | Viewed by 1480

Abstract

To examine root canal morphology of mandibular second premolars (Mn2P) of a mixed Swiss-German population by means of micro-computed tomography (micro-CT). Root canal configuration (RCC) of 102 Mn2P were investigated using micro-CT unit (µCT 40; SCANCO Medical AG, Brüttisellen, Switzerland) with [...] Read more.

To examine root canal morphology of mandibular second premolars (Mn2P) of a mixed Swiss-German population by means of micro-computed tomography (micro-CT). Root canal configuration (RCC) of 102 Mn2P were investigated using micro-CT unit (µCT 40; SCANCO Medical AG, Brüttisellen, Switzerland) with 3D software imaging (VGStudio Max 2.2; Volume Graphics GmbH, Heidelberg, Germany), described with a four-digit system code indicating the main root canal from coronal to apical thirds and the number of main foramina. A total of 12 different RCCs were detected. 1-1-1/1 (54.9%) was most frequently observed RCC, followed by 1-1-1/2 (14.7%), 1-1-2/2 (10.8%), 1-2-2/2 (4.9%), 1-1-3/3 (3.9%), 1-1-1/3 (2.9%), 2-1-1/1 (2.9%) and less frequently 1-1-2/3, 1-2-1/2, 2-1-2/2, 1-1-2/5, 1-1-1/4 with each 1.0%. No accessory foramina were present in 35.3%, one in 35.3%, two in 21.6%, three and four in 2.9%, and five in 2.0%. In 55.9% Mn2Ps, accessory root canals were present in apical third and 8.8% in middle third of a root. Connecting canals were observed less frequently (6.9%) in apical and 2.9% in the middle third, no accessory/connecting canals in coronal third. Every tenth tooth showed at least or more than three main foramina. Almost two thirds of the sample showed accessory root canals, predominantly in apical third. The mainly single-rooted sample of Mn2Ps showed less frequent morphological diversifications than Mn1Ps. Full article

► Show Figures

Figure 1

20 pages, 26287 KiB

Open AccessArticle

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

by Roser Viñals and Jean-Philippe Thiran

J. Imaging 2023, 9(12), 256; https://doi.org/10.3390/jimaging9120256 - 23 Nov 2023

Cited by 2 | Viewed by 1440

Abstract

Ultrafast ultrasound imaging, characterized by high frame rates, generates low-quality images. Convolutional neural networks (CNNs) have demonstrated great potential to enhance image quality without compromising the frame rate. However, CNNs have been mostly trained on simulated or phantom images, leading to suboptimal performance [...] Read more.

Ultrafast ultrasound imaging, characterized by high frame rates, generates low-quality images. Convolutional neural networks (CNNs) have demonstrated great potential to enhance image quality without compromising the frame rate. However, CNNs have been mostly trained on simulated or phantom images, leading to suboptimal performance on in vivo images. In this study, we present a method to enhance the quality of single plane wave (PW) acquisitions using a CNN trained on in vivo images. Our contribution is twofold. Firstly, we introduce a training loss function that accounts for the high dynamic range of the radio frequency data and uses the Kullback–Leibler divergence to preserve the probability distributions of the echogenicity values. Secondly, we conduct an extensive performance analysis on a large new in vivo dataset of 20,000 images, comparing the predicted images to the target images resulting from the coherent compounding of 87 PWs. Applying a volunteer-based dataset split, the peak signal-to-noise ratio and structural similarity index measure increase, respectively, from 16.466 ± 0.801 dB and 0.105 ± 0.060, calculated between the single PW and target images, to 20.292 ± 0.307 dB and 0.272 ± 0.040, between predicted and target images. Our results demonstrate significant improvements in image quality, effectively reducing artifacts. Full article

(This article belongs to the Special Issue Application of Machine Learning Using Ultrasound Images, Volume II)

► Show Figures

Figure 1

18 pages, 5809 KiB

Open AccessArticle

Understanding Error Patterns: An Analysis of Alignment Errors in Rigid 3D Body Scans

by Julian Meißner, Michael Kisiel, Nagarajan M. Thoppey, Michael M. Morlock and Sebastian Bannwarth

J. Imaging 2023, 9(12), 255; https://doi.org/10.3390/jimaging9120255 - 21 Nov 2023

Viewed by 1331

Abstract

Three-dimensional body scanners are attracting increasing interest in various application areas. To evaluate their accuracy, their 3D point clouds must be compared to a reference system by using a reference object. Since different scanning systems use different coordinate systems, an alignment is required [...] Read more.

Three-dimensional body scanners are attracting increasing interest in various application areas. To evaluate their accuracy, their 3D point clouds must be compared to a reference system by using a reference object. Since different scanning systems use different coordinate systems, an alignment is required for their evaluation. However, this process can result in translational and rotational misalignment. To understand the effects of alignment errors on the accuracy of measured circumferences of the human lower body, such misalignment is simulated in this paper and the resulting characteristic error patterns are analyzed. The results show that the total error consists of two components, namely translational and tilt. Linear correlations were found between the translational error (R² = 0.90, … 0.97) and the change in circumferences as well as between the tilt error (R² = 0.55, … 0.78) and the change in the body’s mean outline. Finally, by systematic analysis of the error patterns, recommendations were derived and applied to 3D body scans of human subjects resulting in a reduction of error by 67% and 84%. Full article

(This article belongs to the Special Issue Image Processing and Computer Vision: Algorithms and Applications)

► Show Figures

Figure 1

Journal Menu

Journal Browser

J. Imaging, Volume 9, Issue 12 (December 2023) – 29 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI