Quality Of Medical Images Research Articles

A Non-Conventional Review on Multi-Modality-Based Medical Image Fusion

Today, medical images play a crucial role in obtaining relevant medical information for clinical purposes. However, the quality of medical images must be analyzed and improved. Various factors affect the quality of medical images at the time of medical image reconstruction. To obtain the most clinically relevant information, multi-modality-based image fusion is beneficial. Nevertheless, numerous multi-modality-based image fusion techniques are present in the literature. Each method has its assumptions, merits, and barriers. This paper critically analyses some sizable non-conventional work within multi-modality-based image fusion. Often, researchers seek help in apprehending multi-modality-based image fusion and choosing an appropriate multi-modality-based image fusion approach; this is unique to their cause. Hence, this paper briefly introduces multi-modality-based image fusion and non-conventional methods of multi-modality-based image fusion. This paper also signifies the merits and downsides of multi-modality-based image fusion.

Semi-Supervised Medical Image Segmentation Guided by Bi-Directional Constrained Dual-Task Consistency

Medical image processing tasks represented by multi-object segmentation are of great significance for surgical planning, robot-assisted surgery, and surgical safety. However, the exceptionally low contrast among tissues and limited available annotated data makes developing an automatic segmentation algorithm for pelvic CT challenging. A bi-direction constrained dual-task consistency model named PICT is proposed to improve segmentation quality by leveraging free unlabeled data. First, to learn more unmarked data features, it encourages the model prediction of the interpolated image to be consistent with the interpolation of the model prediction at the pixel, model, and data levels. Moreover, to constrain the error prediction of interpolation interference, PICT designs an auxiliary pseudo-supervision task that focuses on the underlying information of non-interpolation data. Finally, an effective loss algorithm for both consistency tasks is designed to ensure the complementary manner and produce more reliable predictions. Quantitative experiments show that the proposed PICT achieves 87.18%, 96.42%, and 79.41% mean DSC score on ACDC, CTPelvic1k, and the individual Multi-tissue Pelvis dataset with gains of around 0.8%, 0.5%, and 1% compared to the state-of-the-art semi-supervised method. Compared to the baseline supervised method, the PICT brings over 3-9% improvements. The developed PICT model can effectively leverage unlabeled data to improve segmentation quality of low contrast medical images. The segmentation result could improve the precision of surgical path planning and provide input for robot-assisted surgery.

DTMF: Decision Based Trimmed Multimode Approach Filter for Denoising MRI Images

The brain MRI image denoising is a challenging and attracting field for young researchers because it enhances the quality of medical images. Salt and pepper noise is the most dangerous noise which reduces the accuracy of brain diagnosis, and it damages the brain medical images severely, that leads to neurologists to fix incorrect treatments or surgery. The pitfalls raised in the existing denoising methods are less Peak signal to noise ratio, high time consumption and incapable for enormous level of noise range. Hence, this research proposes a novel denoising filter which is entitled as ‘Decision based Trimmed Multimode approach oriented Filter (DTMF)’ for salt and pepper noise removal. Herein, the noise removal section is branched into six steps which efficiently reduce noises based on multimode of majority strength. The main concepts used in this research are viz. decision based approach, trimming process, majority of intensity, median, mean, dynamic windows and Square shaped Exemplar Modeled Patch Mechanism (SEMPM). The essential contributions of this approach are i) designing rule set for majority strength structured multimode denoising, ii) computation of majority property oriented parameters like majority-instance, majority strength and majority value, iii) novel SEMPM mechanism to predict noise-free data. The novel SEMPM mechanism grants a solution for the prediction of noise-free pixel in account of the noisy pixels whose surrounding window is completely packed by noisy data. The proposed decision-based approach removes the salt and pepper noise with high peak signal to noise ratio even for huge noise range, with reasonable time consumption.

Effect of Continuous Medical Education on Awareness of Clinical Imaging Guidelines Among Imaging Referrers in Sub-Saharan Africa

Rationale and objectives: In recent decades, there has been an effort to improve the quality and safety of medical imaging globally. Such, has been promoted through the application of decision aid tools. Clinical Imaging Guidelines (CIGs) are systematically developed statements to assist referrers to make appropriate patient imaging decisions for screening, diagnosis and management of conditions. Awareness of such guidelines prompts their application thus enhancing safety of imaging procedures. There is evidence of low levels of awareness elsewhere but such hasn’t been assessed in Sub-Saharan Africa. The study assessed the CIGs awareness level among imaging referrers before and after giving continuous medical education (CME) and awareness materials. Methods: A pre and post -CME mean score of 18 item questionnaire on awareness on CIGs for 109 referrers from 5 health facilities were compared. A statistical difference in the mean scores for the pre and post intervention assessment was determined using a paired T-test at P > 0.05 and Confidence interval of 95%. Results - At baseline, we found a 47% level of CIGs awareness and after the intervention we found a level of 59%. There was a significant statistical change of 12% level of CIGs awareness from pre-intervention 47% to post intervention 59% at P-value < 0.0001 and 95% confidence interval (7.8-16.4). Conclusion and Recommendation: Routine CMEs are a good to start platforms for enhancing awareness of CIGs and strengthening justification of medical exposures.

IRMIRS: Inception-ResNet-Based Network for MRI Image Super-Resolution

Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues. These challenges are increasing the interest in the quality of medical images. Recent research has proven that the rapid progress in convolutional neural networks (CNNs) has achieved superior performance in the area of medical image super-resolution. However, the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance (MR) images, adding extra noise in the models and more memory consumption. Furthermore, conventional deep CNN approaches used layers in series-wise connection to create the deeper mode, because this later end layer cannot receive complete information and work as a dead layer. In this paper, we propose Inception-ResNet-based Network for MRI Image Super-Resolution known as IRMRIS. In our proposed approach, a bicubic interpolation is replaced with a deconvolution layer to learn the upsampling filters. Furthermore, a residual skip connection with the Inception block is used to reconstruct a high-resolution output image from a low-quality input image. Quantitative and qualitative evaluations of the proposed method are supported through extensive experiments in reconstructing sharper and clean texture details as compared to the state-of-the-art methods.

Compensation for Electrical Fatigue in Piezoelectric Transducers

Malfunction of ultrasonic transducers is one of the most common factors affecting the quality of medical ultrasound images. In this work, the effect of electrical fatigue in piezoelectric transducers on the image quality is investigated and subsequently compensated. To this end, through a combination of simulation and controlled experimental and clinical evaluations, the effect of fatigue and its compensation is realized. Initially, the electrical fatigue is simulated by reducing the piezoelectric permittivity and piezoelectric constant parameters based on finite element method (FEM). Next, the transducer’s electromechanical impulse response is obtained, and the quality of reconstructed images from the fatigued transducer array elements is evaluated by applying the FEM simulation results to the Field II simulation program. Also, experimental measurements are conducted using a tissue-mimicking phantom and in vivo imaging. To evaluate the image quality, various metrics such as signal to noise ratio (SNR), side lobe level (SLL), full width at half maximum (FWHM) and contrast to noise ratio (CNR) are measured for the simulated and tissue-mimicking phantoms. To compensate for negative effects of electrical fatigue, a method based on the nonblind deconvolution of the ultrasound radio frequency (RF) signals is proposed. This method utilizes the corresponded electromechanical impulse responses to the fatigue level as the known undesired impulse responses. The results show that the image quality metrics which were significantly degraded due to electrical fatigue, are appropriately improved as a result of the proposed restoration method.

Domain and Content Adaptive Convolution Based Multi-Source Domain Generalization for Medical Image Segmentation.

The domain gap caused mainly by variable medical image quality renders a major obstacle on the path between training a segmentation model in the lab and applying the trained model to unseen clinical data. To address this issue, domain generalization methods have been proposed, which however usually use static convolutions and are less flexible. In this paper, we propose a multi-source domain generalization model based on the domain and content adaptive convolution (DCAC) for the segmentation of medical images across different modalities. Specifically, we design the domain adaptive convolution (DAC) module and content adaptive convolution (CAC) module and incorporate both into an encoder-decoder backbone. In the DAC module, a dynamic convolutional head is conditioned on the predicted domain code of the input to make our model adapt to the unseen target domain. In the CAC module, a dynamic convolutional head is conditioned on the global image features to make our model adapt to the test image. We evaluated the DCAC model against the baseline and four state-of-the-art domain generalization methods on the prostate segmentation, COVID-19 lesion segmentation, and optic cup/optic disc segmentation tasks. Our results not only indicate that the proposed DCAC model outperforms all competing methods on each segmentation task but also demonstrate the effectiveness of the DAC and CAC modules. Code is available at https://git.io/DCAC.

Label-free Medical Image Quality Evaluation by Semantics-aware Contrastive Learning in IoMT

Label-free Medical Image Quality Evaluation by Semantics-aware Contrastive Learning in IoMT

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image pre-processing to enhance medical image quality. Followed by, Inception with ResNet-v2 model is employed for feature extraction. Besides, political optimizer (PO) with twin support vector machine (TSVM) model is exploited for image classification process, shows the novelty of the work. The design of PO algorithm assists in the optimal parameter selection of the TSVM model. For ensuring the enhanced outcomes of the PODL-TCIA model, a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.

An adversarially consensus model of augmented unlabeled data for cardiac image segmentation (CAU+).

High quality medical images play an important role in intelligent medical analyses. However, the difficulty of acquiring medical images with professional annotation makes the required medical image datasets, very expensive and time-consuming. In this paper, we propose a semi-supervised method, $ {\mathrm{C}\mathrm{A}\mathrm{U}}^{+} $, which is a consensus model of augmented unlabeled data for cardiac image segmentation. First, the whole is divided into two parts: the segmentation network and the discriminator network. The segmentation network is based on the teacher student model. A labeled image is sent to the student model, while an unlabeled image is processed by CTAugment. The strongly augmented samples are sent to the student model and the weakly augmented samples are sent to the teacher model. Second, $ {\mathrm{C}\mathrm{A}\mathrm{U}}^{+} $ adopts a hybrid loss function, which mixes the supervised loss for labeled data with the unsupervised loss for unlabeled data. Third, an adversarial learning is introduced to facilitate the semi-supervised learning of unlabeled images by using the confidence map generated by the discriminator as a supervised signal. After evaluating on an automated cardiac diagnosis challenge (ACDC), our proposed method $ {\mathrm{C}\mathrm{A}\mathrm{U}}^{+} $ has good effectiveness and generality and $ {\mathrm{C}\mathrm{A}\mathrm{U}}^{+} $ is confirmed to have a improves dice coefficient (DSC) by up to 18.01, Jaccard coefficient (JC) by up to 16.72, relative absolute volume difference (RAVD) by up to 0.8, average surface distance (ASD) and 95% Hausdorff distance ($ {HD}_{95} $) reduced by over 50% than the latest semi-supervised learning methods.

Painless and accurate medical image analysis using deep reinforcement learning with task-oriented homogenized automatic pre-processing

Pre-processing is widely applied in medical image analysis to remove the interference information. However, the existing pre-processing solutions mainly encounter two problems: (i) it is heavily relied on the assistance of clinical experts, making it hard for intelligent CAD systems to deploy quickly; (ii) due to the personnel and information barriers, it is difficult for medical institutions to conduct the same pre-processing operations, making a deep model that performs well on a specific medical institution difficult to achieve similar performances on the same task in other medical institutions. To overcome these problems, we propose a deep-reinforcement-learning-based task-oriented homogenized automatic pre-processing (DRL-HAPre) framework to overcome these two problems. This framework utilizes deep reinforcement learning techniques to learn a policy network to automatically and adaptively select the optimal pre-processing operations for the input medical images according to different analysis tasks, thus helping the intelligent CAD system to achieve a rapid deployment (i.e., painless) and maintain a satisfactory performance (i.e., accurate) among different medical institutes. To verify the effectiveness and advantages of the proposed DRL-HAPre framework, we further develop a homogenized automatic pre-processing model based on the DRL-HAPre framework to realize the automatic pre-processing of key region selection (called HAPre-KRS) in the pneumonia image classification task. Extensive experimental studies are conducted on three pediatric pneumonia classification datasets with different image qualities, and the results show that: (i) There does exist a hard-to-reproduce problem in clinical practices and the fact that having different medical image qualities in different medical institutes is an important reason for the existing of hard-to-reproduce problem, so it is compelling to propose homogenized automatic pre-processing method. (ii) The proposed HAPre-KRS model and DRL-HAPre framework greatly outperform three kinds of state-of-the-art baselines (i.e., pre-processing, attention and pneumonia baseline), and the lower the medical image quality, the greater the improvements of using our HAPre-KRS model and DRL-HAPre framework. (iii) With the help of homogenized pre-processing, HAPre-KRS (and DRL-HAPre framework) can greatly avoid performance degradation in real-world cross-source applications (i.e., thus overcoming the hard-to-reproduce problem).

A Brief Analysis of Multimodal Medical Image Fusion Techniques

Recently, image fusion has become one of the most promising fields in image processing since it plays an essential role in different applications, such as medical diagnosis and clarification of medical images. Multimodal Medical Image Fusion (MMIF) enhances the quality of medical images by combining two or more medical images from different modalities to obtain an improved fused image that is clearer than the original ones. Choosing the best MMIF technique which produces the best quality is one of the important problems in the assessment of image fusion techniques. In this paper, a complete survey on MMIF techniques is presented, along with medical imaging modalities, medical image fusion steps and levels, and the assessment methodology of MMIF. There are several image modalities, such as Computed Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and Single Photon Emission Computed Tomography (SPECT). Medical image fusion techniques are categorized into six main categories: spatial domain, transform fusion, fuzzy logic, morphological methods, and sparse representation methods. The MMIF levels are pixel-level, feature-level, and decision-level. The fusion quality evaluation metrics can be categorized as subjective/qualitative and objective/quantitative assessment methods. Furthermore, a detailed comparison between obtained results for significant MMIF techniques is also presented to highlight the pros and cons of each fusion technique.

A deep journey into image enhancement: A survey of current and emerging trends

Image captured under poor-illumination conditions often display attributes of having poor contrasts, low brightness, a narrow gray range, colour distortions and considerable interference, which seriously affect the qualitative visual effects on human eyes and severely restrict the efficiency of several machine vision systems. In addition, underwater images often suffer from colour shift and contrast degradation because of an absorption and scattering of light while travelling in water. These unpleasant effects limits visibility, reduce contrast and even generate colour casts that limits the use of underwater images and videos in marine archaeology and biology. In medical imaging applications, medical images are important tools for detecting and diagnosing several medical conditions and ailments. However, the quality of medical images can often be degraded during image acquisition due to factors such as noise interference, artefacts, and poor illumination. This may lead to the misdiagnosis of medical conditions, which can further aggravate life threatening situations. Image enhancement is one of the most important technologies in the field of image processing, and its purpose is to improve the quality of images for specific applications. In general, the basic principle of image enhancement is to improve the quality and visual interpretability of an image so that it is more suitable for the specific applications and the observers. Over the last few decades, numerous image enhancement techniques have been proposed in the literature This study covers a systematic survey on existing state-of-the-art image enhancement techniques into broad classification of their algorithms. In addition, this paper summarises the datasets utilised in the literature for performing the experiments. Furthermore, an attention has been drawn towards several evaluation parameters for quantitative evaluation and compared different state-of-the-art algorithms for performance analysis on benchmark datasets. In addition, we discussed the recent areas of applications in image enhancement in detail. Lastly, we have also discussed numerous unresolved open problems and suggested possible future research directions. We believe that by putting forth all our efforts this study may presents a comprehensive resource for the future research.

Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging.

Computerized tomography (CT) is widely used for clinical screening and treatment planning. In this study, we aimed to reduce X-ray radiation and achieve high-quality CT imaging by using low-intensity X-rays because CT radiation is damaging to the human body. An innovative vision transformer for medical image super-resolution (SR) is applied to establish a high-definition image target. To achieve this, we proposed a method called swin transformer and attention network (STAN) that uses the swin transformer network, which employs an attention method to overcome the long-range dependency difficulties encountered in CNNs and RNNs to enhance and restore the quality of medical CT images. We adopted the peak signal-to-noise ratio for performance comparison with other mainstream SR reconstruction models used in medical CT imaging. Experimental results revealed that the proposed STAN model yields superior medical CT imaging results than the existing SR techniques based on CNNs. The proposed STAN model employs a self-attention mechanism to more effectively extract critical features and long-range information, hence enhancing the quality of medical CT image reconstruction.

IMPROVING MEDICAL IMAGE PIXEL QUALITY USING MICQ UNSUPERVISED MACHINE LEARNING TECHNIQUE

Biomedical image processing and decision making is a growing research demand under global pandemic situation. The quality of medical images plays a vital role in streamlining remote diagnosis and processing via telemedicine platform, in providing unambiguous results and decision supports. This paper presents an improved Medical Image Content Quality (MICQ) technique and it aims to enrich the Magnetic Resonance (MR) image content or pixels based on semi supervised clustering technique for the process of deeper analysis and investigation to identify the normal and abnormal portions. The proposed (IMICQ) system is containing three stages namely pre-processing, clustering and validation respectively. In the pre-processing stage, the MICQ divides the MR image into finite number of non-overlapping blocks or vectors with size (2*2). Next stage, the proposed MICQ system iteratively partitions the MR image dataset or vector set into optimum number of highly relative dissimilar clusters based on K-Means clustering technique. In the last stage, the proposed system measures the quality of clustering result which obtained in the previous stage based on Effective Cluster Validation Measure (ECVM). Experimental results show that the MICQ is better suitable to improve MR image content quality for telemedicine platform and to predict the normal and abnormal portions over the image with higher accuracy ratio.

REVIEW IN IMAGES ENHANCEMENT MEDICAL USING CURVELET TRANSFORM AND VECTOR INFORMATION

The use of medical imaging for the purposes of medical monitoring and diagnosis has seen tremendous growth in recent years, which has resulted in a rising need for suitable data storage and accessibility. The primary goal of image enhancement is to generate a processed image that is suitable for a specific application. This can be accomplished by acquiring the image in the highest resolution possible or by using a variety of techniques to extract the important information while ignoring the irrelevant information. Image processing can then be performed on the processed image. Image enhancement techniques are used in medical imaging to increase the quality of the visual representation of an image by reducing the amount of noise present and sharpening the features. This study examines the Curvelet transform-based approach for increasing medical image quality. The contribution of the proposed review two steps are advised for optimizing the images. The first step is the optimization step utilizing the Curvelet transform, which featured a series of image-preparation processors. Curvelet's ability to extract complete spectrum information from an image with varying edge orientations makes it valuable for assessing data about wave propagation. In light of this, the curvelet transform is considered an excellent filter for improving and denoising images, and it is intended to deal with components in several orientations and scales. The primary objective of this treatment is to boost visibility and eliminate noise, which is superior to conventional techniques of enhancement in which the enhancement step is performed by discrete curvelet transform through wrapping. The contribution of the proposed review the usual vector is the second step of this review approach. The vector image is distributed randomly to each pixel in images that are unclear and noisy. This procedure eliminates the unpredictability in the previously computed directions and makes them regular, producing more precise results and enhancing image clarity, particularly at the image's edges. By comparing the results of the proposed technique of improvement to the results of existing improvement methods, it was determined that the proposed approach yields superior performance and is more efficient. The efficiency and efficacy of the suggested technique may be determined based on the results of the trials, which demonstrated that the method performs very well and yields accurate and precise results.

Medical Image Compression Based on Variational Autoencoder

With the rapid growth of medical image data, it has become a current research hotspot that how to realize the large amounts of the real-time upload and storage of medical images with limited network bandwidth and storage space. However, currently, medical image compression technology cannot perform joint optimization of rate (the degree of compression) and distortion (reconstruction effect). Therefore, this study proposed a medical image compression algorithm based on a variational autoencoder. This algorithm takes rate and distortion as the common optimization goal and uses the residual network module to directly transmit information, which alleviates the contradiction between improving the degree of compression and optimizing the reconstruction effect. At the same time, the algorithm also reduces image loss in the medical image compression process by adding the residual network. The experimental results show that, compared with the traditional medical image compression algorithm and the deep learning compression algorithm, the algorithm in this study has smaller distortion, better reconstruction effect, and can obtain higher quality medical images at the same compression rate.

Medical Ultrasound Image Quality Enhancement and Regions Segmentation

Medical Ultrasound (US) has many features that make it widely used in the world. These features are safety, availability and low cost. However, despite these features, the ultrasound suffers from problems. These problems are speckle noise and artifacts. In this paper, a new method is proposed to improve US images by removing speckle noise and reducing artifacts to enhance the contrast of the image. The proposed method involves algorithms for image preprocessing and segmentation. A median filter is used to smooth the image in the pre-processing. Additionally, to obtain best results, applying median filter with different kernel values. We take the better output of the median filter and feed it into the Gaussian filter, which then feeds the output of the Gaussian filter into histogram equalization to improve image visualization. The segmentation is done by thresholding and region growing segmentation. The value of threshold 128 was found to be better after we tested many values of thresholding. This value of thresholding combined with region growing gave accurate result segmentation of images. This paper demonstrates how image noise, artifacts and techniques were used effectively to improve image quality, and the analysis of performance of various techniques.

An efficient hybrid approach for medical images enhancement

Medical images have various critical usages in the field of medical science and healthcare engineering. These images contain information about many severe diseases. Health professionals identify various diseases by observing the medical images. Quality of medical images directly affects the accuracy of detection and diagnosis of various diseases. Therefore, quality of images must be as good as possible. Different approaches are existing today for enhancement of medical images, but quality of images is not good. In this literature, we have proposed a novel approach that uses principal component analysis (PCA), multi-scale switching morphological operator (MSMO) and contrast limited adaptive histogram equalization (CLAHE) methods in a unique sequence for this purpose. We have conducted exhaustive experiments on large number of images of various modalities such as MRI, ultrasound, and retina. Obtained results demonstrate that quality of medical images processed by proposed approach has significantly improved and better than other existing methods of this field.

A hybrid bat-genetic algorithm for improving the visual quality of medical images

Efficient repression of noise in a medical image is a very significant issue. This paper proposed a method to denoise medical images by the use of a hybrid adaptive algorithm based on the bat algorithm (BA) and genetic algorithm (GA). Medical images can be often affected by different kinds of noise that decrease the precision of any automatic system for analysis. Therefore, the noise reduction methods are always utilized for increasing the Peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) of images to optimize the originality. Gaussian noise and salt and pepper noise corrupted the used medical data, separately. The noise level to medical images was added noise variance from 0.1 to 0.5 to compare the performance of the de-noising techniques. In the analytical study, we apply different kinds of noise like Gaussian noise and salt-and-pepper noise to medical images for making these images noisy. The hybrid BA-GA model was applied on medical noisy images to eliminate noise and the performances have been determined by the statistical analyses such as PSNR, values are gotten 63.04 dB and 59.75 dB for CT and MRI images.