Quality Of Medical Images Research Articles

Channel-Specific and Spatial Residual Attention Network for Medical Image Denoising.

Medical image quality is crucial for physicians to ensure accurate diagnosis and therapeutic strategies. However, due to the interference of noise, there are often various types of noise and artifacts in medical images. This not only damages the visual clarity of images, but also reduces the accuracy of information extraction. Considering that the edges of medical images are rich in high-frequency information, to enhance the quality of medical images, a dual attention mechanism, the channel-specific and spatial residual attention network (CSRAN) in the U-Net framework is proposed. The CSRAN seamlessly integrates the U-Net architecture with channel-wise and spatial feature attention (CSAR) modules, as well as low-frequency channel attention modules. Combined with the two modules, the ability of medical image processing to extract high-frequency features is improved, thereby significantly improving the edge effects and clarity of reconstructed images. This model can present better performance in capturing high-frequency information and spatial structures in medical image denoising and super-resolution reconstruction tasks. It cannot only enhance the ability to extract high-frequency features and strengthen its nonlinear representation capability, but also endow strong edge detection capabilities of the model. The experimental results further prove the superiority of CSRAN in medical image denoising and super-resolution reconstruction tasks.

Image Enhancement Network Architecture for Multidimensional Fusion of Medical Imaging Data under Intense Light Interference

Abstract Due to the limitations of imaging equipment and environment, the acquired medical images usually have a certain degree of noise and artifacts, which leads to the degradation of the quality of medical images and affects the doctors’ clinical diagnosis of the condition. In this paper, the Gauss-Laplace operator is used to perform normalized filtering on medical images to reduce the influence of noise and improve the convolution effect of images. Through the CLAHE algorithm, the histogram is optimized for equalization, and the network architecture of the image is designed in this way. The quality of the enhanced image is evaluated through experimental design and dataset processing. In the evaluation of subjective and objective metrics, the PSNR and SSIM metrics of the images in SR × 2 are improved by 1.576 dB and 0.997 dB, respectively, on the BraTS dataset. This algorithm’s subjective score is the most high among the four enhancement algorithms, with an average score of 8.25, which aligns with the objective evaluation results. Among the image enhancement results, this paper’s algorithm better adjusts the histogram distribution with h(k) distribution ranging from 0.526-4.215, which is better than other enhancement algorithms in detail enhancement.

Encryption-then-embedding-based hybrid data hiding scheme for medical images

The smart healthcare system plays a vital role in modern healthcare, facilitating the exchange of Electronic Patient Records (EPR), and improving medical care. Nevertheless, safeguarding the inherent security and confidentiality of EPR data persists as a formidable challenge. This issue demanding rigorous attention and innovative solutions. Digital watermarking safeguards the genuineness and integrity of digital images, and is widely employed. In medical imaging, it is vital to guarantee the confidentiality and security of patient data. In this paper, we propose an Encryption-Then-Embedding-Based data hiding scheme for medical images that combines cryptography and watermarking techniques. The proposed technique first encrypts the patient information using Advanced Encryption Standard-Galois/Counter Mode (AES-GCM) before embedding. The intensity-based image segmentation method is then used to select the Region of Non-interest (RONI) for embedding the encrypted patient information and the watermark using a Fused Transform-Based Method (FTBM). The experiments conducted in this study utilize a large dataset of medical data, The Cancer Genome Atlas Lung Adenocarcinoma Collection (TCGA-LUAD). The findings of this study demonstrate the efficacy of the proposed technique in safeguarding patient data while simultaneously preserving the quality of medical images. In comparison to existing techniques, the proposed approach demonstrates superior performance in terms of security, authenticity, and integrity.

Improving Assaulted Medical Image Quality using Improved Adaptive Filtering Network

Improving Assaulted Medical Image Quality using Improved Adaptive Filtering Network

Improving assaulted medical image quality using improved adaptive filtering network

Improving assaulted medical image quality using improved adaptive filtering network

Review of Graph Neural Networks for Medical Image

As deep learning continues to evolve, more and more applications are generating data from non-Euclidean domains and representing them as graphs with complex relationships and interdependencies between objects. This poses a significant challenge to deep learning algorithms. Because, due to the uniqueness of graphs, applying deep learning to ubiquitous graph data is not an easy task. To solve the problem in non-Euclidean domains, graph Neural Networks (GNNs) have emerged. A graph neural network (GNN) is a neural model that captures dependencies between graphs by passing messages between graph nodes. With the continuous development of medical image technology, medical image diagnosis plays a crucial role in clinical practice. However, in practice, medical images are often affected by noise, artifacts, and other interfering factors, which may lead to inaccurate and unstable diagnostic results. Therefore, image-denoising techniques become especially critical in medical image processing. Therefore, researchers have proposed innovative methods based on graph neural networks for effective noise removal, preserving the key features of the image and improving the quality and usability of medical images. This paper reviews the research progress of graph neural networks in the field of medical image denoising. It also summarises the problems and challenges of current research and looks at the future direction of medical image-denoising research.

Voxel representation of brain images inpainting via Regional Pixel Semantic Network and pyramidal attention AE - Quantile differential mechanism model

Medical image inpainting holds significant importance in enhancing the quality of medical images by restoring missing areas, thereby rendering them suitable for diagnostic purposes. While several techniques have been previously proposed for medical image inpainting, they are not suitable for distorted images containing metallic implants due to their limited consideration of known shaped masking. To overcome this limitation, a novel Vectorized Box Interpolation with Arbitrary Auto-Rand Augment Masking technique has been proposed which involves scaling and vectorizing images to expand their details and generating asymmetrically shaped masking in an automatic random format. One of the challenging tasks in this regard is the precise detection of lost regions, which is addressed through the introduction of the Regional Pixel Semantic Network. This technique employs the locally shared features (LSF) based region sensing with FCN (fully convolutional network) segmentation, which performs automatic segmentation based on neighboring pixel local dependency and regional features to determine the location of masked regions. During the reconstruction of missing parts, a significant challenge posed is the inability to recognize proximity in encoding owing to the generation of shadow-like regions on the feature map. To address this issue, a novel Multilayered DRC Regularized Pyramidal Attention AE Model has been proposed which employs dilated convolution with coherent pyramidal attention for feature extraction and improves image resolution using a Laplacian convolutional layer. Moreover, the realness of the generated image is determined using the Quantile Differential Mechanism model, where in the Quantile Differential Partial Convolutional Discriminator utilizes the hyperbolic tangent activation function in the partial convolutional layer to calculate recognition accuracy. As a result, the proposed method achieves high percentages for accuracy (98 %), precision (97 %), sensitivity (96 %), recall (95 %), and F-measure (96 %) thereby outperforming existing methods. Overall, this proposed method effectively handles distorted images with metallic implants, accurately detects lost regions, and improves the reconstructed image quality.

Quality assurance framework for rapid automatic analysis deployment in medical imaging

PurposeAutomatic image analysis algorithms have an increasing role in clinical quality assurance (QA) in medical imaging. Although the implementation of QA calculation algorithms may be straightforward at the development level, actual deployment of a new method to clinical routine may require substantial additional effort from supporting services. We sought to develop a multimodal system that enables rapid implementation of new QA analysis methods in clinical practice. MethodsThe QA system was built using freely available open-source software libraries. The included features were results database, database interface, interactive user interface, e-mail error dispatcher, data processing backend, and DICOM server. An in-house database interface was built, providing the developers of analyses with simple access to the results database. An open-source DICOM server was used for image traffic and automatic initiation of modality-specific QA image analyses. ResultsThe QA framework enabled rapid adaptation of new analysis methods to automatic image processing workflows. The system provided online data review via an easily accessible user interface. In case of deviations, the system supported simultaneous review of the results for the user and QA expert to trigger corrective actions. In particular, embedded error thresholds, trend analyses, and error-feedback channels were provided to facilitate continuous monitoring and to enable pre-emptive corrective actions. ConclusionAn effective and novel QA framework incorporating easy adaptation and scalability to automated image analysis methods was developed. The framework provides an efficient and responsive web-based tool to manage the normal operation, trends, errors, and abnormalities in medical image quality.

Comparative study of different fiber materials based PCF for enhancing medical imaging quality

The image's quality is a crucial consideration in medical imaging, therefore selecting a fiber that offers more benefits is important. The primary objective of this work is to explore the imperative applications of Photonic Crystal Fiber (PCF) in medical imaging. Numerical studies utilizing the finite element method and a normal mesh have been performed on the proposed PCF with several different types of fiber materials in the core and background. More broadly in terms of wavelength, from 0.5 μm to 1.6 μm, some of the optical parameters investigated, including confinement loss, nonlinearity, effective area, and numerical aperture (NA). Changing PCF’s background material from BK7, fused silica, or ZBLAN has resulted in a shift in the parameters. Here, BK7 is discovered to be a promising possibility in medical imaging, and a thorough comparative analysis of PCF parameters has been performed. The comparison reveals that at an optical wavelength of 0.5 μm, the proposed PCF with BK7 exhibits strong nonlinearity of 119 W−1Km−1, low confinement loss of 2.97 × 10−12 dB/m, a numerical aperture of 0.15, along with a very low effective area of 4.18 × 10−12 m2. It is therefore anticipated that the suggested PCF with BK7 will be more suitable for the imaging application.

Image Enhancement of Colon Cancer Images using a Two-Stage Hybrid Approach of TV and Shift-Invariant Filtering

Medical imaging holds a critical position in both disease diagnosis and treatment strategies, including colon cancer. However, the quality of medical images can often be compromised by noise and artifacts, making accurate interpretation challenging. Here, we suggest a innovative two-stage hybrid method aimed at enhancing colon cancer images, leveraging the strengths of Total Variation (TV) denoising and shift-invariant filtering techniques. The primary objective of this study is to increase visual superiority as well as diagnostic accurateness of colon cancer image while preserving crucial anatomical information.The first stage of our approach employs Total Variation (TV) denoising to reduce noise and enhance image contrast. TV regularization is known for its ability to preserve edges and fine details, making it well-suited for medical image enhancement. In the second stage, we apply shift-invariant filtering to further enhance the image quality. This technique is designed to address the limitations of traditional filtering methods and adapt to the specific characteristics of colon cancer images.
 To evaluate the effectiveness of our hybrid approach, we conducted a comprehensive set of experiments using a relevant dataset. We employed a range of quantitative metrics, including the Global Relative Error (EGRAS), Root Mean Squared Error (RMSE), Universal Image Quality Index (UQI), and Pixel-Based Visual Information Fidelity (VIFP), to assess the quality and fidelity of enhanced images. Our results demonstrate that the hybrid combination consistently outperforms existing methods, yielding superior image quality and diagnostic potential. This study makes a valuable contribution to the realm of medical imaging by introducing a robust and effective method to improve the quality of colon cancer images. Findings suggest that the proposed two-stage hybrid method holds promise for improving the accuracy of diagnosis and treatment planning. Further research in this direction may lead to advancements in medical image enhancement techniques, ultimately benefiting patient care and medical research.

Myocardial perfusion segmentation and partitioning methods in personalized models of coronary blood flow

Abstract In this work we present methods and algorithms for construction of a personalized model of coronary haemodynamics based on computed tomography images. This model provides estimations of fractional flow reserve, coronary flow reserve, and instantaneous wave-free ratio taking into account transmural perfusion ratio indices obtained from perfusion images. The presented pipeline consists of the following steps: aorta segmentation, left ventricle wall segmentation, coronary arteries segmentation, construction of 1D network of vessels, partitioning of left ventricle wall, and personalization of the model parameters. We focus on a new technique, which generates specific perfusion zones and computes transmural perfusion ratio according to the quality of available medical images with a limited number of visible terminal coronary vessels. Numerical experiments show that accurate evaluation of stenosis before precutaneous coronary intervention should take into account both fractional flow reserve indices and myocardial perfusion, as well as other indices, in order to avoid misdiagnosis. The presented model provides better understanding of the background of clinical recommendations for possible surgical treatment of a stenosed coronary artery.

Medical image super-resolution for smart healthcare applications: A comprehensive survey

The digital transformation in healthcare, propelled by the integration of deep learning models and the Internet of Things (IoT), is creating unprecedented opportunities for improving patient care. However, the utilization of low-resolution images, often generated by IoT devices, introduces biases in the deep learning models, thereby affecting the overall clinical decision-making process. While super-resolution techniques have been extensively employed to transform low-resolution images into high-resolution counterparts, the challenge of achieving highly accurate image restoration remains unresolved. This is especially critical in the medical imaging domain, where even minor inaccuracies can lead to significant biases in model training and, consequently, impact clinical outcomes. Although existing surveys have explored various super-resolution methods and their applications across different fields, a comprehensive review emphasizing the accuracy of image restoration in medical imaging and its subsequent influence on deep learning models is notably lacking. This survey seeks to bridge this gap by offering a systematic review of current state-of-the-art models, highlighting the limitations of existing surveys, and underscoring open questions that merit further research. Specifically, we delve into the intricacies of medical image restoration, identify research gaps and unmet challenges in achieving optimal restoration of medical images, and emphasize the crucial role of developing more precise and resilient super-resolution methods to enhance the quality of medical images and, consequently, the performance of deep learning models in healthcare applications. Ultimately, this survey fosters a deeper comprehension of the prevailing challenges and unresolved questions in the field, thus setting the stage for future research efforts focused on refining medical image restoration and, subsequently, boosting the efficacy of deep learning models in healthcare.

A NOVEL CARDIAC IMAGE SEGMENTATION METHOD USING AN OPTIMIZED 3D U-NET MODEL

Medical image segmentation holds significant importance for doctors, patients, and the entire health care industry. For doctors, it provides more accurate information about cardiac structures, aiding in improving diagnoses and treatment decisions. For patients, segmentation techniques enable personalized medical care, enhancing treatment outcomes and satisfaction. The entire health care sector benefits from the advancement of this technology, driving the development of medical science and contributing to better health care quality and patient well-being. Additionally, segmentation plays a crucial role in research and education, facilitating the accumulation and dissemination of medical knowledge. In summary, the application of medical image segmentation has profound implications for progress in the medical field and patient welfare. In recent years, with technological advancements and innovative algorithms, medical image quality has greatly improved, with higher resolution and reduced noise and artifacts. Simultaneously, the application of deep learning techniques has made the automatic analysis and diagnosis of medical images more precise and efficient. However, due to the complex structures and diversity often present in medical images, models tend to have limited generalization across different datasets, leading to unstable segmentation performance. Considering the excellent image segmentation performance of the three-dimensional (3D) U-Net model, this study introduces an improved spatial attention mechanism on the basis of the 3D U-Net model to enhance its segmentation performance. The spatial attention mechanism enhances the model’s feature extraction capabilities. The enhanced network can capture dependencies among features across both channel and spatial dimensions in the entire global scope. Additionally, it can strengthen any two correlated features within the input feature vector, thereby enhancing the model’s representational capacity. Through detailed experimental validation, the effectiveness of the proposed model is thoroughly demonstrated. Its superiority in performance and computational efficiency positions it as a significant breakthrough in the medical image segmentation field, providing a strong foundation for future research and clinical practice in medical image processing.

Efficient multi-objective evolutionary neural architecture search for U-Nets with diamond atrous convolution and Transformer for medical image segmentation

Deep encoder–decoder neural networks like U-Nets have made significant contributions to the development of computer vision applications such as image segmentation. Neural architecture search (NAS) has the potential to further automatically adjust the architectures of U-Nets for various medical image segmentation tasks. Most of the NAS techniques focus on optimizing segmentation accuracies of network architectures. In real-world medical image segmentation scenarios, two main challenges are poor medical image quality and diverse deployment devices with different computing capabilities. A large architecture designed only for the high segmentation accuracy is difficult to run on various deployment devices. To address these challenges, this paper proposes a multi-objective evolutionary neural architecture search method (CTU-NAS) for U-Nets with diamond atrous convolution and Transformer for medical image segmentation. A hybrid U-Net architecture (CTU-Net) with diamond atrous convolution and Transformer modules is designed as the supernet of CTU-NAS. Then a channel search strategy based on sorting and selection is applied to speed up the search for subnets by precisely selecting and training the most important channels more times. In addition, CTU-NAS employs a dual acceleration mechanism based on weight sharing and surrogate model to lower the cost of evaluations of subnets. CTU-NAS applies a multi-objective evolutionary algorithm to balance between the segmentation accuracy and the number of parameters. Experimental results on two medical segmentation datasets show that CTU-NAS is capable of quickly generating a group of excellent network architectures with different sizes and their performances also outperform or come close to those of the manually designed networks.

Enhancing medical ultrasound imaging through fractional mathematical modeling of ultrasound bubble dynamics

The classical mathematical modeling of ultrasound acoustic bubble is so far using to improve the medical imaging quality. A clear and visible medical ultrasound image relies on bubble’s diameter, wavelength and intensity of the scattered sound. A bubble with diameter much smaller than the sound wavelength is regarded as highly efficient source of sound scattering. The dynamical equation for a medical ultrasound bubble is primarily modeled in classical integer-order differential equation. Then a reduction of order technique is used to convert the modeled dynamic equation for the bubble surface into a system of incommensurate fractional-orders. The incommensurate fractional-order values are calculated directly, by using Riemann stability region. On the basis of stability the convergence and accuracy of the numerical scheme is also discussed in detail. It has been found that the system will remain stable and chaotic for the incommensurate values α1<0.737 and α2<2.80, respectively.

Empowering ultrasound image filtering precision by reducing speckles and preserving edge cues

AbstractUltrasound speckle is an interference pattern that reduces the quality of medical ultrasound images and decreases the capability of interpreting the medical information included in these images. Therefore, speckle suppression is vital whenever ultrasound imaging is used. In this research, an efficient adaptive ultrasound image filter is proposed to reduce ultrasound speckle while maintaining the edge cues in the image. In particular, the pixels that correspond to tissue boundaries are identified by computing the edge map of ultrasound image, and the noisy pixels are determined by calculating the image radiation. The proposed filtering method is designed to adaptively apply different window sizes and bandwidths based on the edge map and image radiation. The proposed method is employed to process a set of ultrasound images and the filtering performance is assessed using a set of broadly accepted filtering metrics. These metrics aim to evaluate the filtering performance, including the capability of the filter to preserve the edges and maintain the image quality. The performance of the proposed filtering method is compared with eight existing image filtering methods. The experimental results indicated that the proposed filtering method enables effective edge preservation and retains image quality. These results suggest the potential of the proposed filtering method to achieve effective speckle suppression in ultrasound images.

Bilateral tone mapping scheme for color correction and contrast adjustment in nearly invisible medical images

AbstractMedical images are commonly used to diagnose and screen various diseases. However, medical images taken by operators with different skill levels have a considerable variation in visual quality. Poor illumination of images increases uncertainty in clinical treatment and lead to the risk of misdiagnosis. This paper proposes a novel bilateral tone‐mapped gamma correction to enhance the visual quality of medical images without affecting the natural color quality. First, the proposed tone‐mapped gamma correction is employed to enhance the intensity values of poorly illuminated medical images. Then, bilateral enhancement curve adjustment is performed to effectively improve the global luminance content of the input image with features and richer information. The image quality assessment measures like entropy, blind/reference less image spatial quality evaluator, natural image quality evaluator, perception‐based image quality evaluator, contrast enhancement based image quality parameter, and color contrast fog density evaluates the superior performance of the proposed technique. The experimental results significantly enhance the intensity difference between dark and bright areas and can aid clinical experts and intelligent health fields.

Multi-Scale Attention Generative Adversarial Network for Medical Image Enhancement

High quality medical images are not only an important basis for doctors to carry out clinical diagnosis and treatment, but also conducive to downstream tasks such as image analysis. Although many medical image enhancement methods have achieved good results, some of them still have shortcomings in homogenizing illumination distribution and maintaining texture details, and even introduce boundary artifact noise. In order to deal with these problems, this paper proposes a multi-scale attention generative adversarial network (MAGAN) for medical image enhancement, which is suitable for unpaired images. Our MAGAN is trained in the confrontation between two generators and two discriminators. It tries to fuse multi-scale information in feature extraction by establishing feature pyramid, and filters irrelevant activation to highlight important regions based on attention distribution, which is positive for imaging. Moreover, MAGAN strengthens the constraints on the quality of enhanced image from the perspectives of illumination distribution, texture details, deep semantic features and smoothness, so as to improve the enhancement effect. Compared with six state-of-the-art methods, the experimental results show that MAGAN has the most significant image enhancement effect, and also performs best in the downstream task of image segmentation.

A Survey on the Machine Learning Techniques for Automated Diagnosis from Ultrasound Images.

Medical diagnostic systems has recently been very popular and reliable because of possible automatic detections. The machine learning algorithm is evolved as a core tool of computer-aided diagnosis (CAD) for automatic early and accurate disease detections. The algorithm follows region of interest (ROI) selection followed by specific feature extractions and selection from medical images. The selected features are then fed to suitable classifiers for disease identification. The machine learning algorithm's performance depends on the features selected and the classifiers employed for the job. This paper reviews different feature extraction selection and classification techniques for CAD from ultrasound images. Ultrasonography (USG), due to its portability and its non-invasive nature, is the prime choice of doctors for prescribing as an imaging test. A survey on the USG imaging based on four major diseases is performed in this paper, whose diagnosis followed by automatic detection. Various techniques applied for feature extraction, selection, and classification by different authors to achieve improved accuracy are tabulated. For medical images, we found texture based gray-level extracted features and SVM (support vector machine) classifiers to be more significant in improving classification accuracy, even achieving 100% accuracy in many research articles. However, many research articles also suggest the importance of student's t-test in improving classification accuracy by selecting significant features from extracted features. The proposed algorithm's accuracy also depends on the quality of medical images, which are frequently degraded by the introduction of noise and artifacts while imaging acquisition. So, challenges in denoising are added in this paper as a separate topic to highlight the role of the machine learning algorithm in removing noise and artifacts from the USG images.

PSSGAN: Towards spectrum shift based perceptual quality enhancement for fluorescence imaging.

Fluorescence imaging has demonstrated great potential for malignant tissue inspection. However, poor imaging quality of medical fluorescent images inevitably brings challenges to disease diagnosis. Though improvement of image quality can be achieved by translating the images from low-quality domain to high-quality domain, fewer scholars have studied the spectrum translation and the prevalent cycle-consistent generative adversarial network (CycleGAN) is powerless to grasp local and semantic details, leading to produce unsatisfactory translated images. To enhance the visual quality by shifting spectrum and alleviate the under-constraint problem of CycleGAN, this study presents the design and construction of the perception-enhanced spectrum shift GAN (PSSGAN). Besides, by introducing the constraint of perceptual module and relativistic patch, the model learns effective biological structure details of image translation. Moreover, the interpolation technique is innovatively employed to validate that PSSGAN can vividly show the enhancement process and handle the perception-fidelity trade-off dilemma of fluorescent images. A novel no reference quantitative analysis strategy is presented for medical images. On the open data and collected sets, PSSGAN provided 15.32% ∼ 35.19% improvement in structural similarity and 21.55% ∼ 27.29% improvement in perceptual quality over the leading method CycleGAN. Extensive experimental results indicated that our PSSGAN achieved superior performance and exhibited vital clinical significance.