Image Noise Research Articles

An automated pheochromocytoma and paraganglioma lesion segmentation AI-model at whole-body 68Ga- DOTATATE PET/CT.

Somatostatin receptor (SSR) targeting radiotracer 68Ga-DOTATATE is used for Positron Emission Tomography (PET)/Computed Tomography (CT) imaging to assess patients with Pheochromocytoma and paraganglioma (PPGL), rare types of Neuroendocrine tumor (NET) which can metastasize thereby becoming difficult to quantify. The goal of this study is to develop an artificial intelligence (AI) model for automated lesion segmentation on whole-body 3D DOTATATE-PET/CT and to automate the tumor burden calculation. 132 68Ga-DOTATATE PET/CT scans from 38 patients with metastatic and inoperable PPGL, were split into 70, and 62 scans, from 20, and 18 patients for training, and test sets, respectively. The training set was further divided into patient-stratified 5 folds for cross-validation. 3D-full resolution nnUNet configuration was trained with 5-fold cross-validation. The model's detection performance was evaluated at both scan and lesion levels for the PPGL test set and two other clinical cohorts with NET (n = 9) and olfactory neuroblastoma (ONB, n = 5). Additionally, quantitative statistical analysis of PET parameters including SUVmax, total lesion uptake (TLU), and total tumor volume (TTV), was conducted. The nnUNet AI model achieved an average 5-fold validation dice similarity coefficient of 0.84 at the scan level. The model achieved dice similarity coefficients (DSC) of 0.88, 0.6, and 0.67 at the scan level, the sensitivity of 86%, 61.13%, and 61.64%, and a positive predictive value of 89%, 74%, and 86.54% at the lesion level for the PPGL test, NET and ONB cohorts, respectively. For PPGL cohorts, smaller lesions with low uptake were missed by the AI model (p < 0.001). Anatomical region-based failure analysis showed most of the false negative and false positive lesions within the liver for all the cohorts, mainly due to the high physiologic liver background activity and image noise on 68Ga- DOTATATE PET scans. The developed deep learning-based AI model showed reliable performance for automated segmentation of metastatic PPGL lesions on whole-body 68Ga-DOTATATE-PET/CT images, which may be beneficial for tumor burden estimation for objective evaluation during therapy follow-up. https://www. gov/study/NCT03206060 , https://www. gov/study/NCT04086485 , https://www. gov/study/NCT05012098 .

Processing of Idiopathic Pulmonary Fibrosis images based on spatial interpolation using DFT, FFT, DCT and LPF

The work focuses on the study of existing issues with some medical images obtained from patients with Idiopathic Pulmonary Fibrosis (IPF) who have survived this COVID-19 pandemic. This study analyzes potential causes of incorrect medical diagnoses with this disease. In this regard, we employ numerical algorithms such as DFT (Discrete Fourier Transform), FFT (Fast Fourier Transform), DCT (Discrete Cosine Transform), and LPF (Low-pass filter). The main objective of this work is to demonstrate that with these numerical algorithms based on Continuous and Discrete Fourier Theory, it is possible to filter existing noise in IPF radiography images. Furthermore, it is possible to compress, enhance, and improve their resolution so that better decisions can be made in a medical protocol. In this way, it contributes to the understanding of the true images that would be used for an optimal medical diagnosis.

Analysis of sagittal plane cine magnetic resonance imaging for measurement of pancreatic tumor residual motion during breath hold and evaluation of gating margins used in radiotherapy treatment.

In pancreatic radiotherapy, residual tumor motion during treatment increases the risk of toxicity. Cine imaging acquired during magnetic resonance guided radiotherapy (MRgRT) enables real-time treatment gating in response to anatomical motion, which can reduce this risk; however, treatment gating can negatively impact the efficiency of treatment. This study aimed to quantify the extent of residual tumor motion during breath hold and evaluate the appropriateness of the treatment gating margins used in current clinical practice. Cine imaging acquired during pancreatic MRgRT of 11 patients on the ViewRay MRIdian was analyzed. The total duration of treatment analyzed was 12 h 13 min. Improved methods for processing and analyzing cine imaging were developed: breath holds were systematically separated with frequency analysis, residual motion was measured with consideration of both the tracking structure contour and centroid, and residual motion measurements were supported by phantom measurements of image scaling, resolution, and noise. Residual motion was measured at angles 0°, 45°, 90°, and 135° to the superior-inferior (SI) direction. Total residual motion was measured by combining directional measurements. The minimum tracking structure displacement resolvable through cine imaging was found to be 1.5mm; therefore, residual motion analysis was limited to 1.5mm spatial resolution. Total residual motion was contained within margins ±1.5, ±3, and ±4.5mm with mean percentage frequencies of 97.0%, 91.1%, and 67.8%. Most residual motion was observed in the SI direction, and significantly more residual motion was measured for the tracking structure contour than the centroid. The results demonstrate that patients are largely able to maintain breath hold positions to within a 3mm margin, thus provide evidence that supports the use of a 3mm gating margin in clinical practice. Residual motion frequently exceeded 1.5mm so a reduction in gating margin would have an undesirable impact on treatment efficiency.

Evaluation of Image Segmentation Methods for In Situ Quality Assessment in Additive Manufacturing

Additive manufacturing (AM), or 3D printing, has revolutionized the fabrication of complex parts, but assessing their quality remains a challenge. Quality assessment, especially for the interior part geometry, relies on post-print inspection techniques unsuitable for real-time in situ analysis. Vision-based approaches could be employed to capture images of any layer during fabrication, and then segmentation methods could be used to identify in-layer features in order to establish dimensional conformity and detect defects for in situ evaluation of the overall part quality. This research evaluated five image segmentation methods (simple thresholding, adaptive thresholding, Sobel edge detector, Canny edge detector, and watershed transform) on the same platform for their effectiveness in isolating and identifying features in 3D-printed layers under different contrast conditions for in situ quality assessment. The performance metrics used are accuracy, precision, recall, and the Jaccard index. The experimental set-up is based on an open-frame fused filament fabrication printer augmented with a vision system. The control system software for printing and imaging (acquisition and processing) was custom developed in Python running on a Raspberry Pi. Most of the segmentation methods reliably segmented the external geometry and high-contrast internal features. The simple thresholding, Canny edge detector, and watershed transform methods did not perform well with low-contrast parts and could not reliably segment internal features when the previous layer was visible. The adaptive thresholding and Sobel edge detector methods segmented high- and low-contrast features. However, the segmentation outputs were heavily affected by textural and image noise. The research identified factors affecting the performance and limitations of these segmentation methods and contributing to the broader effort of improving in situ quality assessment in AM, such as automatic dimensional analysis of internal and external features and the overall geometry.

A genetic programming Rician noise reduction and explainable deep learning model for Alzheimer's diseases severity prediction.

Degradation of magnetic resonance imaging (MRI) remains a challenging issue, with noise being a key damaging component introduced due to a variety of environmental and mechanical factors. The aim of this research work is to addresses the issue of noise reduction and to predict Alzheimer's disease detection efficiently. First, we present a genetic programming (GP) technique for reducing Rician noise in MRI images to pre-process the dataset. To effectively reduce Rician noise, this GP approach combines a Feature Extraction component, GP Optimal Expression, and an Optimum Removal Estimation component. In the second phase, we design and develop an explainable Deep Learning framework. This framework uses a local data-driven interpretation technique based on SHAP values to investigate the relationship between the neural network's estimated AD diagnosis and the input MRI images. In addition, we handle class distribution by combining an oversampling strategy with a minority approach. Several assessment metrics are used to analyze the performance of our proposed model. The proposed method is tested on a variety of medical samples, and the results are compared to those obtained using other comparable approaches. We also test and compare our model to three cutting-edge models: DenseNet169, VGGNet15, and Inceptionv3. The empirical results show that our proposed model outperforms others, particularly in handling basic structures with limited spectral features, lower computational complexity, and less overfitting. This research worked addressed Rician noise issue in MRI images and predict AD severity prediction using explainable deep learning framework.

Performance and Robustness of Physiological Controllers With Integrated Pressure Sensors on Inflow Cannula.

The control of left ventricular assist devices (LVADs) requires sensors and/or estimators to account for the physiological state of the patient and apply advanced controllers. Sensor characteristics are a challenge when using implantable pressure sensors because they influence the quality of physiological control and the robustness of the controlled system. The objective of this work is to investigate the performance and robustness of LVAD controllers that operate based on LVAD integrated pressure sensors. Four pressure-based LVAD controllers are tested with a HeartMate 3 that has an integrated pressure sensor. Controller sensitivity as well as robustness to sensor drift and noise are evaluated based on controller response to large changes in preload and afterload. A fail-safe strategy for sensor failure used also to investigate the reliable operation of the LVAD in realistic conditions. All tested controllers are sensitive to drifting pressure signals, which can lead to unstable behavior. The results show that two controllers are robust to noise. The other two controllers show high deviation and oscillation in cardiac output even for small noise. Additionally, a noticeable difference of the controller's response between simulated and measured pressure input was observed, indicating the need for a robust controller design. Reliable operation even in the event of a sensor failure was achieved on the basis of a fail-safe control system.

Deep Variational Network Toward Blind Image Restoration.

Blind image restoration (IR) is a common yet challenging problem in computer vision. Classical model-based methods and recent deep learning (DL)-based methods represent two different methodologies for this problem, each with their own merits and drawbacks. In this paper, we propose a novel blind image restoration method, aiming to integrate both the advantages of them. Specifically, we construct a general Bayesian generative model for the blind IR, which explicitly depicts the degradation process. In this proposed model, a pixel-wise non-i.i.d. Gaussian distribution is employed to fit the image noise. It is with more flexibility than the simple i.i.d. Gaussian or Laplacian distributions as adopted in most of conventional methods, so as to handle more complicated noise types contained in the image degradation. To solve the model, we design a variational inference algorithm where all the expected posteriori distributions are parameterized as deep neural networks to increase their model capability. Notably, such an inference algorithm induces a unified framework to jointly deal with the tasks of degradation estimation and image restoration. Further, the degradation information estimated in the former task is utilized to guide the latter IR process. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.

A systematic review of deep learning-based denoising for low-dose computed tomography from a perceptual quality perspective.

Low-dose computed tomography (LDCT) scans are essential in reducing radiation exposure but often suffer from significant image noise that can impair diagnostic accuracy. While deep learning approaches have enhanced LDCT denoising capabilities, the predominant reliance on objective metrics like PSNR and SSIM has resulted in over-smoothed images that lack critical detail. This paper explores advanced deep learning methods tailored specifically to improve perceptual quality in LDCT images, focusing on generating diagnostic-quality images preferred in clinical practice. We review and compare current methodologies, including perceptual loss functions and generative adversarial networks, addressing the significant limitations of current benchmarks and the subjective nature of perceptual quality evaluation. Through a systematic analysis, this study underscores the urgent need for developing methods that balance both perceptual and diagnostic quality, proposing new directions for future research in the field.

Image Deblurring Using Non Linear Activation Free Network

Abstract: While picture restoration has made significantstrides in recent years, the sophisticated techniques employed are becoming more intricate. This makes a proper comparison difficult. In this research, we propose a straightforward approachthat outperforms these intricate ones in terms of accuracy while requiring less computational resources. We discovered that complex activation functions such as GELU, Softmax, ReLU, andSigmoid are not really needed. Therefore, we developed a more basic version that does not need them, which we call NAFNet.We put our approach to the test on challenging tasks such as noise reduction and image correction. It performed much better, far better than the sophisticated techniques, and used a lot less processing power.

High-Resolution SAR-to-Multispectral Image Translation Based on S2MS-GAN

Synthetic aperture radar (SAR) has been extensively applied in remote sensing applications. Nevertheless, it is a challenge to process and interpret SAR images. The key to interpreting SAR images lies in transforming them into other forms of remote sensing images to extract valuable hidden remote sensing information. Currently, the conversion of SAR images to optical images produces low-quality results and incomplete spectral information. To address these problems, an end-to-end network model, S2MS-GAN, is proposed for converting SAR images into multispectral images. In this process, to tackle the issues of noise and image generation quality, a TV-BM3D module is introduced into the generator model. Through TV regularization, block-matching, and 3D filtering, these two modules can preserve the edges and reduce the speckle noise in SAR images. In addition, spectral attention is added to improve the spectral features of the generated MS images. Furthermore, we construct a very high-resolution SAR-to-MS image dataset, S2MS-HR, with a spatial resolution of 0.3 m, which is currently the most comprehensive dataset available for high-resolution SAR-to-MS image interpretation. Finally, a series of experiments are conducted on the relevant dataset. Both quantitative and qualitative evaluations demonstrate that our method outperforms several state-of-the-art models in translation performance. The solution effectively facilitates high-quality transitions of SAR images across different types.

Simulation study on magneto-acoustic concentration tomography of magnetic nanoparticles based on vectorial acoustic source and BICGSTAB method

Abstract To overcome the vulnerability to noise of the reconstructed image and simplify the cumbersome iteration process of algorithm in the magneto-acoustic concentration tomography with magnetic induction (MACT-MI) for magnetic nanoparticles (MNPs), we established the matrix relationship between the concentration of MNPs and the first-order derivative of sound pressure based on the reconstruction method of vectorial acoustic source, and proposed the application of BICGSTAB method in solving the concentration distribution. Firstly, a simulation model was established in COMSOL Multiphysics. Secondly, the obtained data were substituted into the derived formula for imaging reconstruction. Finally, the quality of the reconstructed image was analyzed. The effects of MNP radius, shape, asymptotic concentration, and SNR on the reconstruction results were studied. Simulation results show that under the same noise condition, compared with the reconstruction method based on the LSQR-trapezoidal method, the average correlation coefficient increased by 32.9%, the average relative error decreased by 48.5%, the average structural similarity increased by 48.2%, and the average iterations decreased by 58.5%. The proposed method shows superior imaging quality and noise immunity. The research provides a theoretical basis for subsequent experimental research.&amp;#xD;

ANALYSIS OF CANNY EDGE DETECTION METHOD FOR FACIAL RECOGNITION IN DIGITAL IMAGE PROCESSING

The document explores the application of the Canny Edge Detection method in facial recognition systems, specifically for identifying edge patterns in digital images. In the context of technological advancements, the focus is on enhancing data processing through efficient image analysis techniques. The research addresses how different edge detection methods, including Roberts, Prewitt, Sobel, and Canny, function, with the latter being highlighted for its superior ability to minimize error and deliver accurate edge detection results. The study outlines the development of a system designed to identify optimal edge detection parameters using the Canny method, focusing on facial images captured from the front. The system is limited to edge identification in such images, and performance is measured using a correlation coefficient. The process involves several technical steps, such as pre-processing the image (grayscale conversion and noise reduction) and using Gaussian filters and hysteresis thresholding to refine the detection. The research's ultimate aim is to optimize Canny's performance for identifying edges, contributing to advancements in facial recognition technology.

Learning Compressed Artifact for JPEG Manipulation Localization Using Wide-Receptive-Field Network

JPEG image manipulation localization aims to accurately classify and locate tampered regions in JPEG images. Existing image manipulation localization schemes usually consider diverse data streams of spatial domain, e.g. noise inconsistency and local content inconsistency. They, however, easily ignore an objective scenario: data stream features of spatial domain are hard to directly apply to compressed image format, e.g., JPEG, because tampered JPEG images may contain severe re-compression inconsistency and re-compression artifacts, when they are re-compressed to JPEG format. As a result, the traditional localization schemes relying on general data streams of spatial domain may result in a large number of false detection of tampered region in JPEG images. To address the above problem, we a new JPEG image manipulation localization scheme, in which a wide-receptive-field attention network is designed to effectively learn JPEG compressed artifacts. We firstly introduce the wide-receptive-field attention mechanism to re-construct U-Net network, which can effectively capture contextual information of JPEG images and analyze tampering traces from different image regions. Furthermore, a flexible JPEG compressed artifact learning module is designed to capture the image noise caused by JPEG compression, in which the weights can be adjusted flexibly based on image quality, without the need for decompression operations on JPEG images. Our proposed method can significantly strength the differentiation capability of detection model for tampered and non-tampered regions. A series of experiments are performed over different image sets, and the results demonstrate that the proposed scheme can achieve an overall localization performance for multi-scale JPEG manipulation regions and outperform most of state-of-the-art schemes in terms of detection accuracy, generalization and robustness.

Noise & mottle suppression methods for cumulative Cherenkov images of radiation therapy delivery.

Cherenkov imaging during radiotherapy provides a real time visualization of beam delivery on patient tissue, which can be used dynamically for incident detection or to review a summary of the delivered surface signal for treatment verification. Very few photons form the images, and one limitation is that the noise level per frame can be quite high, and mottle in the cumulative processed images can cause mild overall noise. This work focused on removing or suppressing noise via image postprocessing. Images were analyzed for peak-signal-to-noise and spatial frequencies present, and several established noise/mottle reduction algorithms were chosen based upon these observations. These included total variation minimization (TV-L1), non-local means filter (NLM), block-matching 3D (BM3D), alpha (adaptive) trimmed mean (ATM), and bilateral filtering. Each were applied to images acquired using a BeamSite camera (DoseOptics) imaged signal from 6x photons from a TrueBeam linac delivering dose at 600 MU/min incident on an anthropomorphic phantom and tissue slab phantom in various configurations and beam angles. The standard denoised images were tested for PSNR, noise power spectrum (NPS) and image sharpness. The average peak-signal-to-noise ratio (PSNR) increase was 17.4% for TV-L1. NLM denoising increased the average PSNR by 19.1%, BM3D processing increased it by12.1% and the bilateral filter increased the average PSNR by 19.0%. Lastly, the ATM filter resulted in the lowest average PSNR increase of 10.9%. Of all of these, the NLM and bilateral filters produced improved edge sharpness with, generally, the lowest NPS curve. For cumulative image Cherenkov data, NLM and the bilateral filter yielded optimal denoising with the TV-L1 algorithm giving comparable results. Single video frame Cherenkov images exhibit much higher noise levels compared to cumulative images. Noise suppression algorithms for these frame rates will likely be a different processing pipeline involving these filters incorporated with machine learning.

Evaluation of a Vendor-Agnostic Deep Learning Model for Noise Reduction and Image Quality Improvement in Dental CBCT

Background/Objectives: To assess the impact of a vendor-agnostic deep learning model (DLM) on image quality parameters and noise reduction in dental cone-beam computed tomography (CBCT) reconstructions. Methods: This retrospective study was conducted on CBCT scans of 93 patients (41 males and 52 females, mean age 41.2 years, SD 15.8 years) from a single center using the inclusion criteria of standard radiation dose protocol images. Objective and subjective image quality was assessed in three predefined landmarks through contrast-to-noise ratio (CNR) measurements and visual assessment using a 5-point scale by three experienced readers. The inter-reader reliability and repeatability were calculated. Results: Eighty patients (30 males and 50 females; mean age 41.5 years, SD 15.94 years) were included in this study. The CNR in DLM reconstructions was significantly greater than in native reconstructions, and the mean CNR in regions of interest 1-3 (ROI1-3) in DLM images was 11.12 ± 9.29, while in the case of native reconstructions, it was 7.64 ± 4.33 (p &lt; 0.001). The noise level in native reconstructions was significantly higher than in the DLM reconstructions, and the mean noise level in ROI1-3 in native images was 45.83 ± 25.89, while in the case of DLM reconstructions, it was 35.61 ± 24.28 (p &lt; 0.05). Subjective image quality assessment revealed no statistically significant differences between native and DLM reconstructions. Conclusions: The use of deep learning-based image reconstruction algorithms for CBCT imaging of the oral cavity can improve image quality by enhancing the CNR and lowering the noise.

The edge artifact extraction method for Si3N4 ceramic bearing rolling element microcracks characterization

Aiming at the problem that the edge artifacts of Si3N4 ceramic bearing rolling element microcracks have low contrast, contain noise, and easily merge with the background, making it difficult to segment. A method based on 2D discrete wavelet transform and Otsu threshold segmentation is designed to achieve the extraction of microcrack edge artifact features. Wavelet decomposition is used to remove noise, while wavelet reconstruction features are used to restore lost details. Creation of 2D discrete wavelet transform functional equations combining wavelet reconstruction and wavelet decomposition to improve contrast and eliminate noise in images featuring edge artifacts. For the problem of feature edge artifacts that are difficult to remove, the threshold segmentation function equation is designed to maximize the interclass variance, and the optimal threshold value is selected to remove the edge artifacts. The experimental results show that the average PSNR of the Si3N4 ceramic bearing rolling body point, line, and surface microcrack edge artifact feature images enhanced by the method in this paper is close to 62.69 dB, and the average SSIM is about 0.77. The method in this paper improves the contrast of microcrack edge artifact features of Si3N4 ceramic bearing rolling bodies and makes the feature extraction effect of point, line, and surface microcrack edge artifacts more complete.

Significant impact of a novel whole heart motion correction algorithm on cardiac computed tomography and aortic valve calcium scores

Abstract Introduction A new whole-heart motion-correction algorithm has been available in cardiac computed tomography (CT), and it has already revealed the higher diagnostic accuracy of significant coronary artery stenosis in cases with higher heart rate at the CT scan. Recently, aortic valve calcium scores (AVCS) are often measured for the decision-making of surgical treatment of aortic valve stenosis. However, the measurement of AVCS of the aortic valve has potential risks of motion artifact of the aortic valve. Therefore, we would like to know the clinical impact of a new whole-heart motion correction algorithm for accurately measuring aortic valve AVCS without motion artifacts. Purpose The purpose of this study is to evaluate the impact of a new whole-heart motion-correction algorithm on non-contrast cardiac CT images and the AVCS in trans-catheter aortic valve regurgitation (TAVR) candidates with severe aortic valve stenosis. Materials and methods Forty-two consecutive TAVR candidates (83 ± 7 years old, 21 males) who underwent ECG-gated CT using 256-row detector CT from November 2023 to February 2024 at our hospital were analyzed. The best phase non-contrast cardiac images were automatically selected, and they were reconstructed with both the standard (STD) algorithm and the new whole-heart motion-correction algorithm. AVCS was calculated using semi-automatic commercially available software. The aortic valve's image noise was also measured. Results Non-contrast best cardiac phase images were successfully reconstructed using the new motion correction algorithm in 36 patients (86%). The best cardiac phase was mainly the diastolic phase in 29 patients (81%). The overall Agatston score and calcified volume of the aortic valve in SSF2 were 1779 ± 896, and 1403 ± 689, respectively, and they were lower than 2195 ± 1132 and 1656 ± 851 of STD (all P &amp;lt; 0.01). Conclusion The new whole-heart motion-correction algorithm worked in almost all TAVR candidates, and revealed lower AVCS and shows a potential for increasing AVCS accuracy.

Pediatric contrast-enhanced chest CT on a photon-counting detector CT: radiation dose and image quality compared to energy-integrated detector CT.

Photon counting detector (PCD) CT benefits from reduced noise compared with conventional energy-integrating detector (EID) CT, which should translate to improved image quality and reduced radiation exposure for pediatric patients undergoing chest CT with IV contrast. To determine the differences in radiation exposure and image quality of PCD CT and EID CT in pediatric chest CT with intravenous (IV) contrast. In this institutional review board-approved retrospective observational study, 20 scan pairs (20 PCD CT; 20 EID CT) for children who underwent chest CT with IV contrast on both a PCD CT (Siemens NAEOTOM Alpha) and an EID CT (Siemens SOMATOM Definition Edge or Force) within 12months were reviewed independently by three pediatric radiologists for three subjective quality features on 5-point Likert scales: overall quality, small structure delineation, and motion artifact. Objective measures of image quality (image noise, signal-to-noise ratio, and contrast-to-noise ratio) were assessed by a single radiologist in several locations in the chest through region of interest measurement of Hounsfield units (HU) and standard deviation. Patient-related and radiation exposure parameters were collected for each scan and summarized with median and interquartile range (IQR). The Wilcoxon rank-sum test was utilized to compare groups. A P < 0.05 indicated statistical significance. Inter-observer agreement of subjective image quality metrics was analyzed using weighted kappa. Age (14.2years vs 13.8years, P= 0.15), height (P= 0.13), weight (P= 0.21), and BMI (P = 0.24) did not significantly differ between groups. There were 10 male and 3 female patients. Compared to EID CT, PCD CT showed lower radiation exposure parameters including volumetric CT dose index, 1.7mGy (IQR 1.1-2.4mGy) vs 3.8mGy (IQR 2.0-4.7mGy) (P< 0.01), and size-specific dose estimate, 2.6mGy (IQR 1.8-3.1mGy) vs 5.0mGy (IQR 3.3-6.2mGy) (P< 0.01). Objective image quality of lung parenchyma was improved on the PCD CT scanner, including image noise 119.5 HU (IQR 95.4-135.7 HU) vs 143.1 HU (IQR 125.4-169.8 HU) (P < 0.01), signal-to-noise ratio (SNR) -6.1 (IQR -8.4 to -4.8) vs -4.9 (IQR -5.6 to -3.8) (P= 0.01), and contrast-to-noise ratio -63.9 (-84.1 to -57.5) vs -60.5 (-76.3 to -52.5) (P = 0.01). Motion artifact was improved on the PCD CT scanner (P< 0.01). No significant differences in overall image quality or small structure delineation were identified (P= 0.06 and P= 0.31). PCD CT pediatric chest CT had significantly reduced radiation exposure, improved image quality, and reduced motion artifact compared with EID CT.

Detection of optic disc in human retinal images utilizing the Bitterling Fish Optimization (BFO) algorithm

Early detection and correct identification of the optic disc (OD) on scanned retinal images are significant for diagnosing and treating several ophthalmic conditions, including glaucoma and diabetic retinopathy. Conventional methods for detecting the OD often struggle with processing retinal images due to noise, changes in illumination, and complex overlapping images. This study presents the development of effective and accurate fixation of the optic disc using the Bitterling Fish Optimization (BFO) algorithm, which enhances the processes of OD imaging in speed and precision. The proposed method begins with image enhancement and noise suppression for preprocessing, followed by applying the BFO algorithm to locate and delineate the OD region. The performance evaluation of the algorithm was conducted within several public domain retinal images, including DRIVE, STARE, ORIGA, DRISHTI-GS, DiaRetDB0, and DiaRetDB1 datasets about some internal metrics: sensitivity (SE), specificity (SP), accuracy (ACC), DICE overlap coefficient, overlap and time of processing respectively. The technique based on BFO provided better results, with 99.33%, 99.94%, and 98.22% accuracy achieved for OD in DRIVE, DRISHTI-GS, and DiaRetDB 1, respectively. The approach also demonstrated high overlaps and good DICE results, with a DICE coefficient of 0.9501 for the DRISHTI-GS database. On average, the processing time per image was less than 2.5 s, proving the approach’s efficiency in computations. The BFO approach has demonstrated its effectiveness and scalability in detecting optic discs in retinal images in an automated manner. It showed impressive performance levels in terms of computation time and accuracy and was variation resistant irrespective of the quality of the image and the pathology present on it. This method holds significant potential for clinical use, providing a meaningful way of diagnosing and managing ocular disease at an early stage.

Sensitivity of Wavelet-Based Optical Flow Velocimetry (wOFV) to Common Experimental Error Sources

Abstract The influence of several potential error sources and non-ideal experimental effects on the accuracy of a wavelet-based optical flow velocimetry (wOFV) method when applied to tracer particle images is evaluated using data from a series of synthetic flows. Out-of-plane particle displacements, severe image noise, laser sheet thickness reduction, and image intensity non-uniformity are shown to decrease the accuracy of wOFV in a similar manner to correlation-based PIV. For the error sources tested, wOFV displays a similar or slightly increased sensitivity compared to PIV, but the wOFV results are still more accurate than PIV when the magnitude of the non-ideal effects remain within expected experimental bounds. For the majority of test cases, the results are significantly improved by using image pre-processing filters and the magnitude of improvement is consistent between wOFV and PIV. Flow divergence does not appear to have an appreciable effect on the accuracy of wOFV velocity estimation, even though the underlying fluid transport equation on which wOFV is based implicitly assumes that the motion is divergence-free. This is a significant finding for the broader applicability of planar velocimetry measurements using wOFV. Finally, it is noted that the accuracy of wOFV is not reduced notably in regions of the image between tracer particles, as long as the overall seeding density is not too sparse i.e., below 0.02 particles per pixel. This explicitly demonstrates that wOFV (when applied to particle images) yields an accurate whole field measurement, and not only at or adjacent to the discrete particle locations.