Quality Of Medical Images Research Articles

Метрики качества медицинских изображений

Метрики качества медицинских изображений

The Study on Interpretation of the Scatter Degradation Factor using an additional Filter in a Medical Imaging System

X-rays used for diagnosis have a continuous energy distribution. However, photons with low energy not only reduce image contrast, but also contribute to the patient’s radiation exposure. Therefore, clinics currently use filters made of aluminum. Such filters are advantageous because they can reduce the exposure of the patient to radiation. However, they may have negative effects on imaging quality, as they lead to increases in the scattered dose. In this study, we investigated the effects of the scattered dose generated by an aluminum filter on medical image quality. We used the relative standard deviation and the scatter degradation factor as evaluation indices, as they can be used to quantitatively express the decrease in the degree of contrast in imaging. We verified that the scattered dose generated by the increase in the thickness of the aluminum filter causes degradation of the quality of medical images.

Object contour based on improving region growing method and context border condition

SummaryImage diagnosis is a very significant medical specialty. Thereby, the doctor will see the form and function of the structure inside the body. The recorded images will be an important basis to help doctors find the disease and treatment more effectively. However, the quality of medical images is a vital factor. Improving the quality of medical images and object contour are the major challenges, because each tiny detail of medical images can contain much information. Object contour in good medical image quality is hard work and it is more difficult in low‐quality medical image. The organs in the image are not clear, which is difficult for the doctor to distinguish the border of the organs in the image. In this paper, we proposed a method for object contour based on improving region growing in bandelet domain in low‐quality medical images. The method includes two stages: increasing the quality of medical image in bandelet domain and object contour by improving region growing combined with context border condition in medical images in the first stage. The result of the proposed method is better than the other methods.

A total variation-undecimated wavelet approach to chest radiograph image enhancement

Most often medical images such as X-Rays have a low dynamic range and many of their targeted features are difficult to identify. Intensity transformations that improve image quality usually rely onwavelet denoising and enhancement typically use the technique of thresholding to obtain better quality medical images. A disadvantage of wavelet thresholding is that even though it adequately removes noise in an image, it introduces unwanted artifacts into the image near discontinuities. We utilize a total variation method and an undecimated wavelet image enhancing algorithm for improving the image quality of chest radiographs. Our approach achieves a high level chest radiograph image deniosing in lung nodules detection while preserving the important features. Moreover, our method results in a high image sensitivity that reduces the average number of false positives on a test set of medical data.

Computed Tomography (CT) Image Quality Enhancement via a Uniform Framework Integrating Noise Estimation and Super-Resolution Networks.

Computed tomography (CT) imaging technology has been widely used to assist medical diagnosis in recent years. However, noise during the process of imaging, and data compression during the process of storage and transmission always interrupt the image quality, resulting in unreliable performance of the post-processing steps in the computer assisted diagnosis system (CADs), such as medical image segmentation, feature extraction, and medical image classification. Since the degradation of medical images typically appears as noise and low-resolution blurring, in this paper, we propose a uniform deep convolutional neural network (DCNN) framework to handle the de-noising and super-resolution of the CT image at the same time. The framework consists of two steps: Firstly, a dense-inception network integrating an inception structure and dense skip connection is proposed to estimate the noise level. The inception structure is used to extract the noise and blurring features with respect to multiple receptive fields, while the dense skip connection can reuse those extracted features and transfer them across the network. Secondly, a modified residual-dense network combined with joint loss is proposed to reconstruct the high-resolution image with low noise. The inception block is applied on each skip connection of the dense-residual network so that the structure features of the image are transferred through the network more than the noise and blurring features. Moreover, both the perceptual loss and the mean square error (MSE) loss are used to restrain the network, leading to better performance in the reconstruction of image edges and details. Our proposed network integrates the degradation estimation, noise removal, and image super-resolution in one uniform framework to enhance medical image quality. We apply our method to the Cancer Imaging Archive (TCIA) public dataset to evaluate its ability in medical image quality enhancement. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on de-noising and super-resolution by providing higher peak signal to noise ratio (PSNR) and structure similarity index (SSIM) values.

Efficient medical image enhancement based on CNN‐FBB model

Medical image quality requirements have been increasingly stringent with the recent developments of medical technology. To meet clinical diagnosis needs, an effective medical image enhancement method based on convolutional neural networks (CNNs) and frequency band broadening (FBB) is proposed. Curvelet transform is used to deal with medical data by obtaining the curvelet coefficient in each scale and direction, and the generalised cross-validation is implemented to select the optimal threshold for performing denoising processing. Meanwhile, the cycle spinning scheme is used to wipe off the visible ringing effects along the edges of medical images. Then, FBB and a new CNN model based on the retinex model are used to improve the processed image resolution. Eventually, pixel-level fusion is made between two enhanced medical images from CNN and FBB. In the authors’ study, 50 groups of medical magnetic resonance imaging, X-ray, and computed tomography images in total have been studied. The experimental results indicate that the final enhanced image using the proposed method outperforms other methods. The resolution and the edge details of the processed image are significantly enhanced, providing a more effective and accurate basis for medical workers to diagnose diseases.

Measuring the Influence of Service Quality on Patient Satisfaction in Malaysia

The healthcare industry is increasingly becoming more competitive, with patients now demanding a higher level of service quality. This study aims to model the impact of service quality (medical care procedures, administrative practices, hospital image, trustworthiness, patient safety, infrastructure, personnel quality, and social responsibility) on patient satisfaction. Using a structured questionnaire, data were gathered from 194 patients from public and private hospitals in Melaka and Johor. The data were analyzed using a second-generation analytical software, SmartPLS. The results suggest the model can explain 58 percent of the variance in patient satisfaction. Hospital image, patient safety, personnel quality, and social responsibility were the significant predictors of patient satisfaction. Personnel quality was the most important predictor. The implication is that to keep patients satisfied and willing to return for subsequent treatments, the hospitals should invest more on training their personnel.

Singular value decomposition filter for speckle reduction in adaptive ultrasound imaging

Various adaptive beamforming methods have been developed recently for improvement of image quality in medical ultrasonic imaging. Such adaptive beamformers improve the spatial resolution and contrast, but often degrade the contrast-to-noise ratio (CNR) because speckles are well-resolved owing to improvement in the spatial resolution. For improvement of CNR, speckle reduction techniques are required. However, a typical smoothing filter, such as a Gaussian filter, blurs the boundary between different structures in an ultrasonic image. In the present study, a filter based on singular value decomposition was developed for improvement in CNR, while the sharpness of the boundary between different stucutures is preserved. The proposed method was evaluated by a basic experiment using a phantom with an anechoic cyst region. The CNR was improved from 2.93 to 11.06 dB by the proposed method.

A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop.

Imaging research laboratories are rapidly creating machine learning systems that achieve expert human performance using open-source methods and tools. These artificial intelligence systems are being developed to improve medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection, computer-aided classification, and radiogenomics. In August 2018, a meeting was held in Bethesda, Maryland, at the National Institutes of Health to discuss the current state of the art and knowledge gaps and to develop a roadmap for future research initiatives. Key research priorities include: 1, new image reconstruction methods that efficiently produce images suitable for human interpretation from source data; 2, automated image labeling and annotation methods, including information extraction from the imaging report, electronic phenotyping, and prospective structured image reporting; 3, new machine learning methods for clinical imaging data, such as tailored, pretrained model architectures, and federated machine learning methods; 4, machine learning methods that can explain the advice they provide to human users (so-called explainable artificial intelligence); and 5, validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets. This research roadmap is intended to identify and prioritize these needs for academic research laboratories, funding agencies, professional societies, and industry.

Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning.

Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) cine images. We compare two deep learning architectures to classify poor quality CMR images: 1) 3D spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent Convolutional Network (LRCN). Though in real clinical setup motion artefacts are common, high-quality imaging of UKBB, which comprises cross-sectional population data of volunteers who do not necessarily have health problems creates a highly imbalanced classification problem. Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space. We also investigate a learning approach using a predetermined curriculum based on synthetic artefact severity. We evaluate our pipeline on a subset of the UK Biobank data set consisting of 3510 CMR images. The LRCN architecture outperformed the 3D-CNN architecture and was able to detect 2D+time short axis images with motion artefacts in less than 1ms with high recall. We compare our approach to a range of state-of-the-art quality assessment methods. The novel data augmentation and curriculum learning approaches both improved classification performance achieving overall area under the ROC curve of 0.89.

Efficient compression of volumetric medical images using Legendre moments and differential evolution

Volumetric medical images are widely used in diagnosing and detecting health problems of patients. Large datasets of volumetric medical images required huge storage space and high network capabilities to transmit these medical images from one location to another especially in the applications of telemedicine and teleradiology. In addition, the quality of medical images plays an important role in successful diagnoses. Therefore, an efficient compression algorithm must achieve significant reduction in the size of these volumetric medical images by using high compression ratio and preserve the quality of these images for successful diagnosis. In this paper, a novel optimized compression algorithm for volumetric medical images is proposed. In this algorithm, the volumetric medical images are divided into two-dimensional (2D) slices where each slice is divided into a group of 8 × 8 nonoverlapped blocks. The Legendre moments are computed for each block where the differential evolution optimization algorithm is utilized to select the optimum moments according to minimization of the cost function. Volumetric medical images from different medical imaging modalities are used in testing and evaluating the proposed compression algorithm. The performance of the proposed algorithm is compared with the existing volumetric medical images compression algorithms where the comparison clearly shows that the proposed algorithm outperforms the existing compression algorithms in terms of mean square error, peak signal-to-noise ratio, normalized correlation coefficient, and structural similarity index.

Development of an aperture-type radiation regulator for shielding against secondary radiation from x-ray tubes and collimators in computed tomography

During computed tomography (CT) scans, radiation scatters in all directions, increasing radiation exposure. In this study, an aperture-type radiation regulator was developed to provide shielding against secondary radiation from the x-ray tube and collimator in CT. To evaluate the usefulness of the developed aperture-type radiation regulator, (1) spatial dose distribution within the CT room was measured, (2) dose intensity at 1 m from the isocenter was compared, (3) absorbed dose in the nearby organs was evaluated using a human equivalent phantom, and (4) noise, CNR, and SNR were compared for assessment of image quality. The results showed that the developed aperture-type radiation regulator reduced the intensity of secondary radiation by approximately 25% in front of the gantry and 15% to the rear of the gantry. The maximum dose distribution on 10 μGy was reduced by approximately 18% in front of the gantry and 12% in the rear. In addition, when the neck and head were scanned, the absorbed dose in the chest decreased by 25% and 40%, respectively, and noise was reduced by 3.3%–4.5% for different phantoms. Evaluation of abdominal CT images showed 18% noise reduction, with 27% and 28% increases in the signal-to-noise and contrast-to-noise ratios, respectively. These results confirmed that the proposed aperture-type radiation regulator can reduce radiation exposure without affecting primary radiation that creates medical images. The results also confirmed that the radiation regulator effectively improves the quality of medical images.

Medical Physics 3.0: Ensuring Quality and Safety in Medical Imaging.

Medical imaging often involves radiation and thus radiation protection. Radiation protection in medicine is only one component of the broader calling of health care professionals: fostering human health. As such, radiation risk needs to be put into the context of the larger mandate of improved outcomes in health care. Medical physicists, according to the new vision of Medical Physics 3.0, make a significant contribution to this mandate as they engage proactively and meaningfully in patient care. Facing the new realities of value-based, personalized, and evidence-based practice, Medical Physics 3.0 is an initiative to make physics inform every patient's care by fostering new skills and expanding horizons for the medical physics profession. It provides a framework by which medical physicists can maintain and improve their integral roles in, and contributions to, health care, its innovation, and its precision. One way that Medical Physics 3.0 will manifest itself in medical imaging practice is by engaging physicists to ensure the precise and optimized use of radiation. Optimization takes place through knowing the defining attributes of the technology in use, the specifics of the patient's situation, and the goals of the imaging and/or intervention. The safety as well as the quality of the procedure is ascertained quantitatively and optimized prospectively, ensuring a proper balance between quality and safety to offer maximum potential benefit to the patient. The results of procedures across the health care operation are then retrospectively analyzed to ensure that each procedure has, in actuality, delivered the targeted quality and safety objectives. Characterizing quality and safety in quantitative terms, objectively optimizing them in the practice of personalized care, and analyzing the results from clinical operations all require the unique combination of precision and innovation that physicists bring to the development and practice of medicine.

A Fast Fractal Based Compression for MRI Images

Magnetic resonance imaging (MRI), which assists doctors in determining clinical staging and expected surgical range, has high medical value. A large number of MRI images require a large amount of storage space and the transmission bandwidth of the PACS system in offline storage and remote diagnosis. Therefore, high-quality compression of MRI images is very research-oriented. Current compression methods for MRI images with high compression ratio cause loss of information on lesions, leading to misdiagnosis; compression methods for MRI images with low compression ratio does not achieve the desired effect. Therefore, a fast fractal-based compression algorithm for MRI images is proposed in this paper. First, three-dimensional (3D) MRI images are converted into a two-dimensional (2D) image sequence, which facilitates the image sequence based on the fractal compression method. Then, range and domain blocks are classified according to the inherent spatiotemporal similarity of 3D objects. By using self-similarity, the number of blocks in the matching pool is reduced to improve the matching speed of the proposed method. Finally, a residual compensation mechanism is introduced to achieve compression of MRI images with high decompression quality. The experimental results show that compression speed is improved by 2-3 times, and the PSNR is improved by nearly 10. It indicates the proposed algorithm is effective and solves the contradiction between high compression ratio and high quality of MRI medical images.

A Secure and High Visual-Quality Framework for Medical Images by Contrast-Enhancement Reversible Data Hiding and Homomorphic Encryption

Medical data security is facing great challenges in medical applications due to the open internet and the semi-trusted cloud. For the sake of privacy protection and the security of medical images, this paper proposes a secure and high visual-quality framework for medical images. In this framework, a novel reversible data hiding (RDH) based on lesion extraction embeds privacy data into medical images for privacy protection and image quality improvement, homomorphic encryption based on chaotic map encrypts images for medical image security. The experiments have shown that the proposed framework increases the security of medical data and improves the visual quality of medical images significantly. The proposed RDH in this framework outperforms the other RDH methods with contrast enhancement and the proposed encryption scheme increases image security well.

A Fast Medical Image Super Resolution Method Based on Deep Learning Network

Low-resolution medical images can hamper medical diagnosis seriously, especially in the analysis of retina images and specifically for the detection of macula fovea. Therefore, improving the quality of medical images and speeding up their reconstruction is particularly important for expert diagnosis. To deal with this engineering problem, our paper presents a fast medical image super-resolution (FMISR) method whereby the three hidden layers to complete feature extraction is as same as the super resolution convolution neural network. It is important that a well-designed deep learning network processes images in the low resolution instead of the high-resolution space and enables the super-resolution reconstruction to be more efficient. Sub-pixel convolution layer addition and mini-network substitution in the hidden layers are critical for improving the image reconstruction speed. While the hidden layers are proposed for ensuring reconstruction quality, our FMISR framework performs significantly faster and produces a higher resolution images. As such, the technique underlying this framework presents a high potential in retinal macular examination as it provides a good platform for the segmentation of retinal images.

Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm

In this paper we have segmented an image by using a k-clustering algorithm, using the Gaussian Mixture Model cluster to generate the initial centroid. Many types of research have been done in the area of image segmentation using clustering especially medical images, these techniques help medical scientists in the diagnosis of diseases thereby to cure this diseases K-means clustering algorithm one of these techniques, it is an unsupervised algorithm and it is used to segment the interest area from the background. We used also partial contrast stretching to improve the quality of the original image. And the final segmented result is comparing with the k-means clustering algorithm and we can conclude that the proposed clustering algorithm has better segmentation. Finally, MSE and PSNR are checked and discovered that they have small and large value respective, which are the condition for good image segmentation quality. And comparison for MSE and PSNR are done for the proposed method and classical K-means algorithm and it is found that the proposed method has better performance result.

Pancreas Contour Detection Based On Shearlet Domain In Low Quality Medical Images

Medical images are very useful in diagnosis and treatment. The low quality medical image will be difficult for the doctor to find abnormalities in the image. One of the difficulties for the doctor is how to clarify parts of the medical image, especially the pancreas. In this paper, we propose the new method for object contour detection based on context awareness in low quality medical images in shearlet domain. The object in medical images which detect contour here is pancreas object. The proposed method includes two periods: improving the medical image quality and detecting pancreas contour. To evaluate the results of the proposed method, we compared the result with the recent methods.

36. Optimization of the acquisition threshold of Photon Counting Detectors (PCDs) used in X-ray medical imaging

Purpose PCDs with energy discrimination led to new possibilities of improving the quality of X-ray medical images. PCDs are direct detection devices in which the charge delivered by a single interacting photon is directly collected and discriminated at single pixels level. The limit of high densely pixelated PCDs is the charge sharing which can lead to multiple counts from a single interaction degrading the spatial resolution of the system. Increasing the discrimination threshold, multiple counts can be partially or totally removed from images improving the spatial resolution at the cost of a reduction of the detection area of single pixels with a consequent loss of statistics. This work aims to show a simple method to find the optimal threshold which gives the best trade-off between spatial resolution and statistics. Methods The acquisitions have been performed at the SYRMEP beamline (Elettra Trieste) using the tomographic experimental set-up of INFN project SYnchrotron Radiation MAmmography (SYRMA-3D). SYRMA-3D uses ‘Pixirad-8’, a PCD with CdTe sensor and a pixel size of 60 μm. For this study, we used a monochromatic beam of 38 keV acquiring a set of images of an edge and a set of images of the beam with different detection thresholds. The two sets of images have been used respectively for the measurement of the Modulation transfer function (MTF) and the noise power spectrum (NPS). Combining both NPS and MTF the Number of Equivalent Quanta (NEQ) can be determined. The NEQ summarizes both informations about spatial resolution and noise and is an useful metrics for the global image quality assessment. Results The comparison between NEQ curves shows that lower thresholds give better NEQ response. Conclusions The increase of the discrimination threshold improves the spatial resolution but worsens the noise. The global image quality, measured using NEQ, is higher for lower thresholds.

Maximum accurate medical image demosaicing using WRGB based Newton Gregory interpolation method

Color image demosaicing is an important image processing technique for obtaining high quality image. This operation is complicated for digital camera, since it converts a Bayer mosaic image to a full color image, which estimates the output image quality of the camera. Nowadays, many techniques are developed to reduce the size and weight of electronic gadgets. In this way, to reduce the size of camera Color Filter Array is used which has only one sensor for three primary colors (Red, Green and Blue). So many demosaicing techniques are developed which reconstruct the full color image from the incomplete color samples in CFA. But till today there is no demosaicing algorithm for medical image re-construction is available in existing literature. In this paper a hybrid medical image demosaicing technique is developed for obtaining better image quality, higher sensitivity as well as higher resolution for high quality medical diagnosis. To improve the medical image quality, Newton Gregory Interpolation is used. This technique mainly focused on Green channel and produce interpolated Red, Green and Blue channels and for enhancing the sensitivity and resolution, updating White channel texture component with RGB channels. In post processing Cross Multilateral Filter is used which decomposes the smoothness component from the texture component. The proposed newton Gregory interpolation based medical image Demosaicing achieves 42.31 db for brain database of NIH and 39.52 db for Iris database of UPOL.