Quality Of Medical Images Research Articles

Image Denoising Using Deblur Generative Adversarial Network Denoising U-Net

Convolutional neural networks (CNNs) are becoming increasingly popular for image denoising. U-Nets, a type of CNN architecture, have been shown to be effective for this task. However, the impact of shallow layers on deeper layers decreases as the depth of the network increases. To address this issue, the authors propose a new image denoising method called DGANDU-Net. DGANDU-Net combines the DeblurGAN design with a U-Net architecture. This combination allows DGANDU-Net to effectively remove noise from images while preserving fine details. The authors also propose the use of two loss functions, mean square error (MSE) and perceptual loss, to improve the performance of DGANDU-Net. MSE is used to learn and improve the extracted features, while perceptual loss is used to produce the final denoised image. The authors evaluate the performance of DGANDU-Net on a variety of noise levels and find that it outperforms other state-of-the-art denoising algorithms in terms of both visual quality and two evaluation indices, including peak signal-to-noise ratio (PSNR) and Structural Similarity Index Measure (SSIM). Specifically, for extremely noisy environments with a noise standard deviation of 75, DGANDU-Net achieves an average PSNR of 37.39[Formula: see text]dB in the test dataset. The authors conclude that DGANDU-Net is a promising new method for image denoising that has the potential to significantly improve the quality of medical images used for diagnosis and treatment.

Quality and Safety in Medical Imaging During Pregnancy and Lactation — Part 2

Quality and Safety in Medical Imaging During Pregnancy and Lactation — Part 2

Quality and Safety in Medical Imaging During Pregnancy and Lactation — Part I

Quality and Safety in Medical Imaging During Pregnancy and Lactation — Part I

Quality and Safety in Medical Imaging During Pregnancy and Lactation — Part 2

Quality and Safety in Medical Imaging During Pregnancy and Lactation — Part 2

Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances.

Artificial intelligence (AI) is providing novel answers to long-standing clinical problems, and it is quickly changing pediatric urology. This thorough analysis focuses on current developments in AI technologies that improve pediatric urology diagnosis, treatment planning, and surgery results. Deep learning algorithms help detect problems with previously unheard-of precision in disorders including hydronephrosis, pyeloplasty, and vesicoureteral reflux, where AI-powered prediction models have demonstrated promising outcomes in boosting diagnostic accuracy. AI-enhanced image processing methods have significantly improved the quality and interpretation of medical images. Examples of these methods are deep-learning-based segmentation and contrast limited adaptive histogram equalization (CLAHE). These methods guarantee higher precision in the identification and classification of pediatric urological disorders, and AI-driven ground truth construction approaches aid in the standardization of and improvement in training data, resulting in more resilient and consistent segmentation models. AI is being used for surgical support as well. AI-assisted navigation devices help with difficult operations like pyeloplasty by decreasing complications and increasing surgical accuracy. AI also helps with long-term patient monitoring, predictive analytics, and customized treatment strategies, all of which improve results for younger patients. However, there are practical, ethical, and legal issues with AI integration in pediatric urology that need to be carefully navigated. To close knowledge gaps, more investigation is required, especially in the areas of AI-driven surgical methods and standardized ground truth datasets for pediatric radiologic image segmentation. In the end, AI has the potential to completely transform pediatric urology by enhancing patient care, increasing the effectiveness of treatments, and spurring more advancements in this exciting area.

Enhancing Medical Image Quality Using Fractional Order Denoising Integrated with Transfer Learning

In medical imaging, noise can significantly obscure critical details, complicating diagnosis and treatment. Traditional denoising techniques often struggle to maintain a balance between noise reduction and detail preservation. To address this challenge, we propose an “Efficient Transfer-Learning-Based Fractional Order Image Denoising Approach in Medical Image Analysis (ETLFOD)” method. Our approach uniquely integrates transfer learning with fractional order techniques, leveraging pre-trained models such as DenseNet121 to adapt to the specific needs of medical image denoising. This method enhances denoising performance while preserving essential image details. The ETLFOD model has demonstrated superior performance compared to state-of-the-art (SOTA) techniques. For instance, our DenseNet121 model achieved an accuracy of 98.01%, precision of 98%, and recall of 98%, significantly outperforming traditional denoising methods. Specific results include a 95% accuracy, 98% precision, 99% recall, and 96% F1-score for MRI brain datasets, and an 88% accuracy, 91% precision, 95% recall, and 88% F1-score for COVID-19 lung data. X-ray pneumonia results in the lung CT dataset showed a 92% accuracy, 97% precision, 98% recall, and 93% F1-score. It is important to note that while we report performance metrics in this paper, the primary evaluation of our approach is based on the comparison of original noisy images with the denoised outputs, ensuring a focus on image quality enhancement rather than classification performance.

Enhancing Diagnostic Accuracy: A Hybrid Approach to Mitigate Speckle and Rician Noise in Medical Images

This research explores the detrimental effects of Rician and speckle noise in medical imaging, particularly within MRI and radiographic modalities. Such noise can significantly obscure critical details, potentially leading to misdiagnoses and inaccurate measurements. Rician noise candidly dangles on the signal strength. Removing this noise affects the origial information in image that makes this noise removal perplexing. On other hand speckle noise is visualised like granular clog in ultrasound imageries. It creates ambiguous details and edges in the images. To address this challenge, the study introduces a novel hybrid method implemented in Java. The method employs a frequency-based decomposition approach for speckle noise and separates images into magnitude and phase components for Rician noise. This allows for targeted filtering, utilizing customized versions of median and Gaussian filters, to effectively remove noise while preserving crucial image features. The proposed method was rigorously assessed using 200 contaminated brain and lung MRIs and radiographs, demonstrating superior performance compared to traditional filters based on crucial system of measurement like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Squared Error (MSE). This research presents a promising solution for enhancing the quality and reliability of medical images, potentially leading to improved diagnostic accuracy and patient outcomes.

Optimization of optical spectroscopy classification algorithms for limited data scenarios in the food industry: Tomato sauce samples case

- This study addresses the problem of training deep learning models with limited datasets, a significant challenge in sectors like medical imaging and food quality analysis. To tackle this issue, generative adversarial networks (GANs) will be employed to augment the available data and improve model performance. An innovative approach is introduced here, integrating semi-supervised learning and generative modeling to maximize the use of small datasets in developing robust models. The method involves reversing the conventional distribution of training and testing data to focus on model evaluation and generalization from limited samples. Wasserstein GANs (WGANs) and Semi-Supervised GANs (SGANs), are utilized to supplement datasets with synthetic but realistic examples, enhancing the training process in scenarios of data scarcity. These techniques are applied in the context of visible reflectance spectroscopy to analyze tomato sauces, demonstrating the method's effectiveness in non-invasively assessing key quality parameters such as oil content, °Brix, and pH. The results show significant improvements in model performance metrics: for %Oil content, overall accuracy increased from 0.47 to 0.66; for °Bx, it rose from 0.65 to 0.71; and for pH measurement, accuracy improved from 0.43 to 0.62. These outcomes highlight the model's improved capability to generalize and maintain accuracy with limited data.

Automated Quality Assessment of Medical Images in Echocardiography Using Neural Networks with Adaptive Ranking and Structure-Aware Learning.

The quality of medical images is crucial for accurately diagnosing and treating various diseases. However, current automated methods for assessing image quality are based on neural networks, which often focus solely on pixel distortion and overlook the significance of complex structures within the images. This study introduces a novel neural network model designed explicitly for automated image quality assessment that addresses pixel and semantic distortion. The model introduces an adaptive ranking mechanism enhanced with contrast sensitivity weighting to refine the detection of minor variances in similar images for pixel distortion assessment. More significantly, the model integrates a structure-aware learning module employing graph neural networks. This module is adept at deciphering the intricate relationships between an image's semantic structure and quality. When evaluated on two ultrasound imaging datasets, the proposed method outshines existing leading models in performance. Additionally, it boasts seamless integration into clinical workflows, enabling real-time image quality assessment, crucial for precise disease diagnosis and treatment.

A Collaborative Self-supervised Domain Adaptation for Low-Quality Medical Image Enhancement.

Medical image analysis techniques have been employed in diagnosing and screening clinical diseases. However, both poor medical image quality and illumination style inconsistency increase uncertainty in clinical decision-making, potentially resulting in clinician misdiagnosis. The majority of current image enhancement methods primarily concentrate on enhancing medical image quality by leveraging high-quality reference images, which are challenging to collect in clinical applications. In this study, we address image quality enhancement within a fully self-supervised learning setting, wherein neither high-quality images nor paired images are required. To achieve this goal, we investigate the potential of self-supervised learning combined with domain adaptation to enhance the quality of medical images without the guidance of high-quality medical images. We design a Domain Adaptation Self-supervised Quality Enhancement framework, called DASQE. More specifically, we establish multiple domains at the patch level through a designed rule-based quality assessment scheme and style clustering. To achieve image quality enhancement and maintain style consistency, we formulate the image quality enhancement as a collaborative self-supervised domain adaptation task for disentangling the low-quality factors, medical image content, and illumination style characteristics by exploring intrinsic supervision in the low-quality medical images. Finally, we perform extensive experiments on six benchmark datasets of medical images, and the experimental results demonstrate that DASQE attains state-of-the-art performance. Furthermore, we explore the impact of the proposed method on various clinical tasks, such as retinal fundus vessel/lesion segmentation, nerve fiber segmentation, polyp segmentation, skin lesion segmentation, and disease classification. The results demonstrate that DASQE is advantageous for diverse downstream image analysis tasks.

G2NPAN: GAN-guided nuance perceptual attention network for multimodal medical fusion image quality assessment.

Multimodal medical fusion images (MMFI) are formed by fusing medical images of two or more modalities with the aim of displaying as much valuable information as possible in a single image. However, due to the different strategies of various fusion algorithms, the quality of the generated fused images is uneven. Thus, an effective blind image quality assessment (BIQA) method is urgently required. The challenge of MMFI quality assessment is to enable the network to perceive the nuances between fused images of different qualities, and the key point for the success of BIQA is the availability of valid reference information. To this end, this work proposes a generative adversarial network (GAN) -guided nuance perceptual attention network (G2NPAN) to implement BIQA for MMFI. Specifically, we achieve the blind evaluation style via the design of a GAN and develop a Unique Feature Warehouse module to learn the effective features of fused images from the pixel level. The redesigned loss function guides the network to perceive the image quality. In the end, the class activation mapping supervised quality assessment network is employed to obtain the MMFI quality score. Extensive experiments and validation have been conducted in a database of medical fusion images, and the proposed method is superior to the state-of-the-art BIQA method.

Enhancing Diagnostic Accuracy: A Hybrid Approach to Mitigate Speckle and Rician Noise in Medical Images

This research explores the detrimental effects of Rician and speckle noise in medical imaging, particularly within MRI and radiographic modalities. Such noise can significantly obscure critical details, potentially leading to misdiagnoses and inaccurate measurements. Rician noise candidly dangles on the signal strength. Removing this noise affects the origial information in image that makes this noise removal perplexing. On other hand speckle noise is visualised like granular clog in ultrasound imageries. It creates ambiguous details and edges in the images. To address this challenge, the study introduces a novel hybrid method implemented in Java. The method employs a frequency-based decomposition approach for speckle noise and separates images into magnitude and phase components for Rician noise. This allows for targeted filtering, utilizing customized versions of median and Gaussian filters, to effectively remove noise while preserving crucial image features. The proposed method was rigorously assessed using 200 contaminated brain and lung MRIs and radiographs, demonstrating superior performance compared to traditional filters based on crucial system of measurement like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Squared Error (MSE). This research presents a promising solution for enhancing the quality and reliability of medical images, potentially leading to improved diagnostic accuracy and patient outcomes.

Ultrasound channel attention residual network for medical plane wave echo data-based average sound speed estimation

The average sound speed estimation is crucial for ultrasound imaging quality and diagnostic. In this article, the deep learning techniques were utilized and an innovative Ultrasound Channel Attention Residual Network (UCA-ResNet) was proposed. The UCA-ResNet incorporated a specially designed Ultrasound Channel Attention (UCA) block, which effectively enhanced relevant ultrasound channel features and convolutional channel features. For evaluation, the simulation, phantom, and in vivo experiments were conducted. In the simulation experiment, UCA-ResNet achieved remarkable results, with a mean absolute error (MAE) of 0.40 m/s, root mean square error (RMSE) of 1.25 m/s, standard deviation of error (SDE) of 1.25 m/s, and a one-time estimation time of 3.67 ms. Moreover, the phantom and in vivo experiments further validated the high accuracy and low computational cost of UCA-ResNet. The UCA-ResNet can accurately estimate the average sound speed using a single plane wave echo data while maintaining low computational cost. It has potential in enhancing medical ultrasound imaging quality and providing novel diagnostic insights. (Code and data will be available upon acceptance of the manuscript.)

Advancements In Multi-Modality Medical Image Fusion: A Comprehensive Review

Multimodality medical image fusion involves the amalgamation of multiple images acquired through single or multiple imaging modalities. The purpose of medical image fusion methods is to enhance the quality of medical images by effectively capturing the key features within the fused output. This consequently enhances the practicality of medical images for diagnostic assessment and issue identification. Medical imaging modalities like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) have been developed and are extensively utilized for clinical diagnoses. These multimodality images harbor vital information crucial for precise and efficient diagnosis of brain ailments. As a result, amalgamating diverse image modalities in the medical domain to create a distinct image brimming with intricate anatomical details and heightened spectral information has become exceedingly valuable in clinical diagnosis. This manuscript offers an exhaustive examination of existing literature on image fusion, elucidating the fundamental concepts and materials essential for a comprehensive grasp of diverse medical fusion techniques.

Applications of Artificial Intelligence in Pancreatic Cystic Lesion Imaging

As the detection rate of pancreatic cystic lesions(PCL)increases,artificial intelligence(AI)has made breakthroughs in the imaging workflow of PCL,including image post-processing,lesion detection,segmentation,diagnosis and differential diagnosis.AI-based image post-processing can optimize the quality of medical images and AI-assisted models for lesion detection,segmentation,diagnosis and differential diagnosis significantly enhance the work efficiency of radiologists.This article reviews the application progress of AI in PCL imaging and provides prospects for future research directions.

Enhancing Diagnostic Images to Improve the Performance of the Segment Anything Model in Medical Image Segmentation.

Medical imaging serves as a crucial tool in current cancer diagnosis. However, the quality of medical images is often compromised to minimize the potential risks associated with patient image acquisition. Computer-aided diagnosis systems have made significant advancements in recent years. These systems utilize computer algorithms to identify abnormal features in medical images, assisting radiologists in improving diagnostic accuracy and achieving consistency in image and disease interpretation. Importantly, the quality of medical images, as the target data, determines the achievable level of performance by artificial intelligence algorithms. However, the pixel value range of medical images differs from that of the digital images typically processed via artificial intelligence algorithms, and blindly incorporating such data for training can result in suboptimal algorithm performance. In this study, we propose a medical image-enhancement scheme that integrates generic digital image processing and medical image processing modules. This scheme aims to enhance medical image data by endowing them with high-contrast and smooth characteristics. We conducted experimental testing to demonstrate the effectiveness of this scheme in improving the performance of a medical image segmentation algorithm.

ENHANCING THE MEDICAL IMAGES QUALITY USING ADAPTIVE GENETIC ALGORITHM

It is obvious that there is a need for a Medical Decisiveness Determine System (MDDS) should be able to diagnose abnormalities in medical imaging. This is because the medical diagnosis system in health care sectors requires assistants to serve as secondary opinions for medical practitioners. During the process of picture acquisition, it is common practice to adjust the contrast level of medical images in order to prevent image degradation. Contrast enhancement in medical images is typically seen as an optimisation problem, and the Adaptive Genetic Algorithm (AGA) algorithm is utilised in order to arrive at the best possible answer. The findings of the comparison are established between the Adaptive Genetic Algorithm that has been proposed and other algorithms that are already in existence. A number of different performance indicators, including PSNR, SSIM, MSSIM, IFC, VIF, VSNR, MSE, SDME, and NAE, are utilised in order to make comparisons between the results. Methods that have been developed and those that already exist are evaluated using a variety of cancer pictures. As a result, the contrast and quality of medical images can be improved through the utilisation of AGA, which also offers a higher contrast level of medical images, hence facilitating improved decision-making by medical professionals.

Doctors' perspectives on the quality of medical imaging in public hospitals in eThekwini District.

There is a paucity of literature on perspectives of referring doctors about the quality of medical imaging services and this study closes this gap in literature. This quality assurance (QA) study aimed to explore the perspectives of doctors on the quality of medical imaging services in selected regional hospitals within eThekwini District of KwaZulu-Natal. The study was conducted in four public regional hospitals. An exploratory descriptive qualitative research design involving 30 min-45 min of in-depth individual interviews was used. A purposive sampling technique was used to select research participants and hospitals to ensure adequate responses to the research questions. The sample involved nine participants and was guided by data saturation. Responses were recorded through notes and voice recordings and thematic analysis was used to analyse data. Three main themes (timeliness of examinations, communication and radiology reports and image quality) and eight subthemes (waiting times, shortage of radiographers, workload, communication between doctors and radiographers, requisition forms, unavailability of radiology reports, clarity of images and image acquisition protocols) emerged from the data. Challenges experienced were exacerbated by high workload and shortage of radiologists and radiographers. Doctors in the data collection sites were mainly dissatisfied with services provided by the medical imaging departments. Regular engagements between medical imaging departments and doctors are important in enhancing the provision of quality care to patients. In-service training of radiographers and employment of additional radiographers and finding solutions to mitigate shortage of radiologists are recommended. This quality assurance (QA) study focused on experiences of doctors while many other medical imaging QA studies in South Africa are equipment based. In-service training of radiographers is recommended to improve image quality and communication skills.

Research on improved black widow algorithm for medical image denoising

Improving the quality of medical images is crucial for accurate clinical diagnosis; however, medical images are often disrupted by various types of noise, posing challenges to the reliability and diagnostic accuracy of the images. This study aims to enhance the Black Widow optimization algorithm and apply it to the task of denoising medical images to improve both the quality of medical images and the accuracy of diagnostic results. By introducing Tent mapping, we refined the Black Widow optimization algorithm to better adapt to the complex features of medical images. The algorithm's denoising capabilities for various types of noise were enhanced through the combination of multiple filters, all without the need for training each time to achieve preset goals. Simulation results, based on processing a dataset containing 1588 images with Gaussian, salt-and-pepper, Poisson, and speckle noise, demonstrated a reduction in Mean Squared Error (MSE) by 0.439, an increase in Peak Signal-to-Noise Ratio (PSNR) by 4.315, an improvement in Structural Similarity Index (SSIM) by 0.132, an enhancement in Edge-to-Noise Ratio (ENL) by 0.402, and an increase in Edge Preservation Index (EPI) by 0.614. Simulation experiments verified that the proposed algorithm has a certain advantage in terms of computational efficiency. The improvement, incorporating Tent mapping and a combination of multiple filters, successfully elevated the performance of the Black Widow algorithm in medical image denoising, providing an effective solution for enhancing medical image quality and diagnostic accuracy.

Evaluation of radiation attenuation properties of breast equivalent phantom material made of gelatin material in mammographic X-ray energies

The work discusses the development of a novel breast phantom material using local gelatin for medical imaging and patient-specific quality assurance. Various gelatin samples were fabricated, differing in hardness (200, 240, and 250 Bloom values) and gelatin-to-water ratios (15%, 20%, and 25%). These samples were evaluated for density, which ranged from 1.04 to 1.08 g/cm³, and for mechanical properties, revealing that higher hardness and gelatin-to-water ratios led to increased stress-strain values. The study also measured the linear and mass attenuation coefficients (μ, μm) of these samples under different X-ray energies (20–40 kV), finding that samples S7, S8, and S9 exhibited the highest μ values across all energies. The properties of these gelatin samples were compared with data on human breast tissue of different ages from the XCOM database. Samples S1 to S6 closely matched the breast tissue of the young age group, with a minimal difference of less than 0.33. Fitness tests (χ2) indicated that samples with 20% and 25% gelatin content were particularly effective in mimicking young breast tissue. This research suggests the potential of using gelatin samples with at least 20% gelatin powder as a promising material for mimicking young breast tissue in medical phantoms.