MRI Medical Images Research Articles

Dual stage MRI image restoration based on blind spot denoising and hybrid attention

BackgroundMagnetic Resonance Imaging (MRI) is extensively utilized in clinical diagnostics and medical research, yet the imaging process is often compromised by noise interference. This noise arises from various sources, leading to a reduction in image quality and subsequently hindering the accurate interpretation of image details by clinicians. Traditional denoising methods typically assume that noise follows a Gaussian distribution, thereby neglecting the more complex noise types present in MRI images, such as Rician noise. As a result, denoising remains a challenging and practical task.MethodThe main research work of this paper focuses on modifying mask information based on a global mask mapper. The mask mapper samples all blind spot pixels on the denoised image and maps them to the same channel. By incorporating perceptual loss, it utilizes all available information to improve performance while avoiding identity mapping. During the denoising process, the model may mistakenly remove some useful information as noise, resulting in a loss of detail in the denoised image. To address this issue, we train a generative adversarial network (GAN) with adaptive hybrid attention to restore the detailed information in the denoised MRI images.ResultThe two-stage model NRAE shows an improvement of nearly 1.4 dB in PSNR and approximately 0.1 in SSIM on clinical datasets compared to other classic models. Specifically, compared to the baseline model, PSNR is increased by about 0.6 dB, and SSIM is only 0.015 lower. From a visual perspective, NRAE more effectively restores the details in the images, resulting in richer and clearer representation of image details.ConclusionWe have developed a deep learning-based two-stage model to address noise issues in medical MRI images. This method not only successfully reduces noise signals but also effectively restores anatomical details. The current results indicate that this is a promising approach. In future work, we plan to replace the current denoising network with more advanced models to further enhance performance.

Brain CT to MRI medical image transformation based on U-Net

Brain CT to MRI medical image transformation based on U-Net

Continuum topological derivative - a novel application tool for denoising CT and MRI medical images

BackgroundCT and MRI modalities are important diagnostics tools for exploring the anatomical and tissue properties, respectively of the human beings. Several advancements like HRCT, FLAIR and Propeller have advantages in diagnosing the diseases very accurately, but still have enough space for improvements due to the presence of inherent and instrument noises. In the case of CT and MRI, the quantum mottle and the Gaussian and Rayleigh noises, respectively are still present in their advanced modalities of imaging. This paper addresses the denoising problem with continuum topological derivative technique and proved its trustworthiness based on the comparative study with other traditional filtration methods such as spatial, adaptive, frequency and transformation techniques using measures like visual inspection and performance metrics.MethodsThis research study focuses on identifying a novel method for denoising by testing different filters on HRCT (High-Resolution Computed Tomography) and MR (Magnetic Resonance) images. The images were acquired from the Image Art Radiological Scan Centre using the SOMATOM CT and SIGNA Explorer (operating at 1.5 Tesla) machines. To compare the performance of the proposed CTD (Continuum Topological Derivative) method, various filters were tested on both HRCT and MR images. The filters tested for comparison were Gaussian (2D convolution operator), Wiener (deconvolution operator), Laplacian and Laplacian diagonal (2nd order partial differential operator), Average, Minimum, and Median (ordinary spatial operators), PMAD (Anisotropic diffusion operator), Kuan (statistical operator), Frost (exponential convolution operator), and HAAR Wavelet (time–frequency operator). The purpose of the study was to evaluate the effectiveness of the CTD method in removing noise compared to the other filters. The performance metrics were analyzed to assess the diligence of noise removal achieved by the CTD method. The primary outcome of the study was the removal of quantum mottle noise in HRCT images, while the secondary outcome focused on removing Gaussian (foreground) and Rayleigh (background) noise in MR images. The study aimed to observe the dynamics of noise removal by examining the values of the performance metrics.In summary, this study aimed to assess the denoising ability of various filters in HRCT and MR images, with the CTD method being the proposed approach. The study evaluated the performance of each filter using specific metrics and compared the results to determine the effectiveness of the CTD method in removing noise from the images.ResultsBased on the calculated performance metric values, it has been observed that the CTD method successfully removed quantum mottle noise in HRCT images and Gaussian as well as Rayleigh noise in MRI. This can be evidenced by the PSNR (Peak Signal-to-Noise Ratio) metric, which consistently exhibited values ranging from 50 to 65 for all the tested images. Additionally, the CTD method demonstrated remarkably low residual values, typically on the order of e−09, which is a distinctive characteristic across all the images. Furthermore, the performance metrics of the CTD method consistently outperformed those of the other tested methods. Consequently, the results of this study have significant implications for the quality, structural similarity, and contrast of HRCT and MR images, enabling clinicians to obtain finer details for diagnostic purposes.ConclusionContinuum topological derivative algorithm is found to be constructive in removing prominent noises in both CT and MRI images and can serve as a potential tool for recognition of anatomical details in case of diseased and normal ones. The results obtained from this research work are highly inspiring and offer great promise in obtaining accurate diagnostic information for critical cases such as Thoracic Cavity Carina, Brain SPI Globe Lens 4th Ventricle, Brain-Middle Cerebral Artery, Brain-Middle Cerebral Artery and neoplastic lesions. These findings lay the foundation for implementing the proposed CTD technique in routine clinical diagnosis.

K-Mean Based Hyper-Metaheuristic Grey Wolf and Cuckoo Search Optimizers for Automatic MRI Medical Image Clustering

In this paper a new clustering algorithm is proposed for optimal clustering of MRI medical image. In our proposed algorithm, the clustering process implemented by K-means clustering algorithm, due to its simplicity and speed. The optimization process was done by a well-known metaheuristic algorithms Grey Wolf Optimizer (GWO) and Cuckoo Search Optimizer. GWO is a metaheuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. It mimics the leadership hierarchy and hunting strategies of wolves to explore the search space efficiently. GWO has shown promising performance in finding high-quality solutions compared to other well-established optimizers. It explores the solution space to find better cluster assignments that minimize the overall intra-cluster variance. By leveraging the exploration potential of GWO, the proposed algorithm aims to improve the quality of the clustering results. Furthermore, the Cuckoo Search Optimizer (CS) is combined with GWO to enhance the algorithm's ability to find a global solution. Cuckoo Search is a metaheuristic algorithm inspired by the breeding behavior of cuckoo birds. It employs random search and Levy flights to diversify the search process and avoid getting trapped in local optima. By combining CS with GWO, the proposed algorithm aims to increase the likelihood of finding the optimal clustering solution.

Enhancing medical image classification with generative AI using latent denoising diffusion probabilistic model and wiener filtering approach

In the evolving landscape of medical diagnostics, a paradigm shift is being catalysed by the advent of generative artificial intelligence. Medical X-ray, CT and MRI images are essential diagnostic tools used by healthcare professionals to assess various musculoskeletal conditions. However, obtaining a sufficient number of medical images for training deep learning models can be challenging due to limited access to labelled data. Hence, the authors propose a novel Latent diffusion process for synthesizing medical images that closely resemble real patient images, aiming to address the challenge of limited access to labelled data in medical diagnostics. Leveraging deep learning and generative modelling techniques, this method synthesizes high-fidelity images that closely mimic real patient scans. By introducing noise and subsequently training the model to denoise, the approach captures intricate patterns inherent in authentic medical images. Among the four datasets utilized, the Diabetes Retinopathy dataset demonstrates superior performance, achieving the highest Mean Structural Similarity Index (MSSIM) score of 0.57 (compared to the dataset baseline of 0.62) and an accuracy of 93.75% when passed through the proposed pipeline. The Cataract dataset, registered a MSSIM score of 0.51 (versus the dataset baseline of 0.53) and an accuracy score of 97.52%, while the Knee OA dataset follows closely with MSSIM and accuracy scores of 0.65 (in contrast to the dataset baseline of 0.63) and 68.66% respectively. The results obtained are then compared with the results generated by the other state of the art models.

Universal adversarial perturbations for multiple classification tasks with quantum classifiers

Abstract Quantum adversarial machine learning is an emerging field that studies the vulnerability of quantum learning systems against adversarial perturbations and develops possible defense strategies. Quantum universal adversarial perturbations are small perturbations, which can make different input samples into adversarial examples that may deceive a given quantum classifier. This is a field that was rarely looked into but worthwhile investigating because universal perturbations might simplify malicious attacks to a large extent, causing unexpected devastation to quantum machine learning models. In this paper, we take a step forward and explore the quantum universal perturbations in the context of heterogeneous classification tasks. In particular, we find that quantum classifiers that achieve almost state-of-the-art accuracy on two different classification tasks can be both conclusively deceived by one carefully-crafted universal perturbation. This result is explicitly demonstrated with well-designed quantum continual learning models with elastic weight consolidation method to avoid catastrophic forgetting, as well as real-life heterogeneous datasets from hand-written digits and medical MRI images. Our results provide a simple and efficient way to generate universal perturbations on heterogeneous classification tasks and thus would provide valuable guidance for future quantum learning technologies.

CNN-Res: deep learning framework for segmentation of acute ischemic stroke lesions on multimodal MRI images

Background Accurate segmentation of stroke lesions on MRI images is very important for neurologists in the planning of post-stroke care. Segmentation helps clinicians to better diagnose and evaluation of any treatment risks. However, manual segmentation of brain lesions relies on the experience of neurologists and is also a very tedious and time-consuming process. So, in this study, we proposed a novel deep convolutional neural network (CNN-Res) that automatically performs the segmentation of ischemic stroke lesions from multimodal MRIs.MethodsCNN-Res used a U-shaped structure, so the network has encryption and decryption paths. The residual units are embedded in the encoder path. In this model, to reduce gradient descent, the residual units were used, and to extract more complex information in images, multimodal MRI data were applied. In the link between the encryption and decryption subnets, the bottleneck strategy was used, which reduced the number of parameters and training time compared to similar research.ResultsCNN-Res was evaluated on two distinct datasets. First, it was examined on a dataset collected from the Neuroscience Center of Tabriz University of Medical Sciences, where the average Dice coefficient was equal to 85.43%. Then, to compare the efficiency and performance of the model with other similar works, CNN-Res was evaluated on the popular SPES 2015 competition dataset where the average Dice coefficient was 79.23%.ConclusionThis study presented a new and accurate method for the segmentation of MRI medical images using a deep convolutional neural network called CNN-Res, which directly predicts segment maps from raw input pixels.

Multimodality medical image fusion analysis with multi-plane features of PET and MRI images using ONSCT

ABSTRACT The Multimodal Medical Image Fusion (MMIF) is affected by poor image quality, which leads to the extraction of inefficient features. The main intent of this work is to fuse various planes in the PET and MRI medical images efficiently using the MMIF approach. Initially, the sample images containing the axial plane of PET and MRI images are aggregated from standard datasets. Then, the collected images are employed for the decomposition process, which is accomplished via Optimal Non-Subsampled Contourlet Transform (ONSCT). The parameters in the NSCT are optimized using the Modified Water Strider Algorithm (MWSA. Once the images are decomposed, it is segmented into two sub-bands as high frequency and low-frequency sub-bands. Consequently, the high-frequency sub-bands of both PET and MRI images are fused by using the optimal weighted average fusion, in which the weight factor is obtained optimally by the MWSA algorithm. Similarly, the low-frequency sub-bands of both medical images are combined by sparse fusion technique. Finally, both the resultant fused images are subjected to Inverse Non-Subsampled Contourlet Transform (INSCT) to get desired fused images. The experimental findings suggest that the proposed model has effectively fused the images, and it also enhances the similarity score with axial planes.

New Proposed Mixed Transforms: CAW and FAW and Their Application in Medical Image Classification

The transformation model plays a vital role in medical image processing. This paper proposed new two Mixed Transforms models that are the hybrid combination of linear and nonlinear Transformations techniques. The first mixed transform is computed in three steps: calculate 2D discrete cosine transform (DCT) of the image, and applying Arnold Transform (AT) on the DCT coefficients, and applying the discrete Wavelet Transform (DWT) on the result to get which was abbreviated as (CAW). The second mixed transform consists of firstly computing the discrete Fourier transform (DFT), net applying the Arnold Transform (AT), and finally, the computation of discrete Wavelet Transform (DWT) which was abbreviated as (FAW). These transforms have superior directional representations as compared to other multiresolution representations such as DWT or DCT and work as non-adaptive mixed transformations for multi-scale object analysis. Due to their relationship to the wavelet idea, they are finding increasing use in areas like image processing and scientific computing. These transforms are tested in medical image classification task and their performances are compared with that of the traditional transforms. CAW and FAW transforms are used in the feature extraction stage of a classification VGG16 deep learning (DNN) task of Tumor MRI medical image. The numerical findings favor CAW and FAW over the wavelet transform for estimating and classifying pictures. From the results obtained it was shown that the CAW and FAW transform gave e much higher classification rate than that achieved with the traditional transforms, namely DCT, DFT and DWT. Furthermore, this combination leads to a family of directional and multi-transformation bases for image processing.

Segmentation of Brain MRI Images using Multi-Kernel FCM EHO Method.

In image processing, image segmentation is a more challenging task due to different shapes, locations, image intensities, etc. Brain tumors are one of the most common diseases in the world. So, the detection and segmentation of brain tumors are important in the medical field. The primary goal of this work is to use the proposed methodology to segment brain MRI images into tumor and non-tumor segments or pixels. In this work, we first selected the MRI medical images from the BraTS2020 database and transferred them to the contrast enhancement phase. Then, we applied thresholding for contrast enhancement to enhance the visibility of structures like blood arteries, tumors, or abnormalities. After the contrast enhancement process, the images were transformed into the image denoising phase. In this phase, a fourth-order partial differential equation was used for image denoising. After the image denoising process, these images were passed on to the segmentation phase. In this segmentation phase, we used an elephant herding algorithm for centroid optimization and then applied the multi-kernel fuzzy c-means clustering for image segmentation. Peak signal-to-noise ratio, mean square error, sensitivity, specificity, and accuracy were used to assess the performance of the proposed methods. According to the findings, the proposed strategy produced better outcomes than the conventional methods. Our proposed methodology was reported to be a more effective technique than existing techniques.

AEAU-Net: an unsupervised end-to-end registration network by combining affine transformation and deformable medical image registration.

Deformable medical image registration plays an essential role in clinical diagnosis and treatment. However, due to the large difference in image deformation, unsupervised convolutional neural network (CNN)-based methods cannot extract global features and local features simultaneously and cannot capture long-distance dependencies to solve the problem of excessive deformation. In this paper, an unsupervised end-to-end registration network is proposed for 3D MRI medical image registration, named AEAU-Net, which includes two-stage operations, i.e., an affine transformation and a deformable registration. These two operations are implemented by an affine transformation subnetwork and a deformable registration subnetwork, respectively. In the deformable registration subnetwork, termed as EAU-Net, we designed an efficient attention mechanism (EAM) module and a recursive residual path (RSP) module. The EAM module is embedded in the bottom layer of the EAU-Net to capture long-distance dependencies. The RSP model is used to obtain effective features by fusing deep and shallow features. Extensive experiments on two datasets, LPBA40 and Mindboggle101, were conducted to verify the effectiveness of the proposed method. Compared with baseline methods, this proposed method could obtain better registration performance. The ablation study further demonstrated the reasonability and validity of the designed architecture of the proposed method.

Image Segmentation-A Novel Doping of Feature Extraction and Pixel Level Abstraction of MRI Medical Images

Image Segmentation-A Novel Doping of Feature Extraction and Pixel Level Abstraction of MRI Medical Images

Brain tumor diagnosis based on convolutional neural network improved by a new version of political optimizer

New strategies for the prevention, diagnosis, and treatment of brain tumors have emerged as a result of advances in medical science. A brain tumor is recently one of the best cases for the focus of researchers in a variety of scientific domains, including medicine, medical engineering, and software engineering, as a result of its aggressive nature. Accurate tumor area recognition in MRI scans has a multitude of purposes, including determining the kind of illness, analyzing treatment and disease progression, and creating therapeutic measures in radiation approaches. Currently, this is made by humans. Considerable study has been done to introduce computer processing in medical sciences, particularly in the processing of medical MRI images, as science has progressed. MRIs of the intracranial cavity offers a comprehensive view of the brain in most cases. A doctor examines this visual representation in order to identify a brain tumor. This method of diagnostics, on the other hand, is incompatible with pinpointing the specific position and size of the tumor. To get a more accurate diagnosis, in this study, we designed an automatic methodology based on deep learning and optimization for diagnosing brain tumors. The method uses an improved version of the political optimizer for the optimal arrangement of the convolutional neural network (CNN) which is done by optimal selection of the CNN hyperparameters. The designed model has been then implemented on the Figshare dataset and its outcomes are verified and validated with some other processes to indicate its efficiency.

Enhancing Privacy of information with Data Embedding in Medical MRI Images Based on Segmentation and HVS Model

The development of communications technology and the arrival of Medicine modern equipment in the health domain has caused the diagnosis and treatment methods to be considered from a distance and medical centers are equipped with telemedicine systems. Forensic medicine organizations with daily clients and outpatient examinations such as accident clients, Conflict returns as well as spousal abuse. Doctors and employees of the organization also serve as one of the powerful arms of the judiciary, following up on important cases in the medical, laboratory, and psychiatric commissions So that they can take steps to realize the rights of the people. Patient data security has become a serious concern for professionals and one of the methods is using data embedding to the protection against these risks. In this method, medical informatics, telemedicine, and forensic medicine organizations has played a pivotal role and any mistake in the reporting can be catastrophic. The main purpose of this research is to present data on EPRs with Enhancing data embedding Based on SSIM and HVS with the help of medical image segmentation and focus on brain MRI images. In this study, innovations include the addition of the HVS block based on the SSIM criterion to meet transparency and robustness conditions. Selection of the embedding coefficient K is considered adaptively depending on the degree of uniformity of the N - ROI region with the image quality factor. The coordinates of ROI areas in one of the DCT and DWT conversion blocks have been demonstrated to have better performance at Concealment EPRs. The choice of coefficients af,k which consists of the optimization frequency sensitivity function and spatial property is comparatively done to match the visual perception of the visual system. The present study aims to improve the effectiveness of the proposed method, improve the security level, and the confidentiality of patient information, and integrate the storage of patient information and image. The simulation results of the proposed method, considering the parameters of embedding and transparency in comparison with other methods, have been done using evaluation criteria including MSE PSNR, NC, SSIM, and BER.

A novel diffusivity function-based image denoising for MRI medical images

A novel diffusivity function-based image denoising for MRI medical images

Application of wavelet transform for processing and analysis of medical data of patients with epilepsy

The study of the wavelet characteristics of the signal helps to identify characteristic patterns in the patterns of physiological rhythms of a healthy person and to determine changes in the dynamic complexity of the patterns in the event of various pathological conditions. The paper shows the possibility of using the wavelet characteristics of EEG (electroencephalogram) patterns for automatic detection of epileptiform discharges. An algorithm for compressing MRI medical images associated with brain pathologies based on the use of Haar wavelets is proposed.

Colored MRI biomedical image tumor classification and segmentation based on transfer learning of modified Y-Net

By incorporating the colored MRI identification synthesis into the MRI segmentation model with transfer learning AI Y-Net, this study clearly shows the high potential of a multidisciplinary system-level study for diagnoses. This way, such a system can provide integrity of the goal without compromising the quality of each one and saving time consumption. Another alternative to such integration is to be used for enhancement and segmentation that is accurate and robust to the variabilities in scanner and acquisition protocols. System Level Simulator is the deep learning based on Kearse AI deep learning network specified to Y-VGG16 net results of outstanding performance in medical image segmentation. Based on the literature, there are different AI models for the diagnosis system, which are different of what is proposed in this paper. A partial-frozen network is applied to the U-net to compare results between different fine-tuning FT strategies. The network operation is also evaluated depending on the dataset size, showing the importance of the combination of dataset, TL and data augmentation (DA). Transfer learning (TL) helps us for MRI medical image segmentation deep learning with more accurate performances of the TL technique. The system hybrid the Y-Net architecture with Transfer learning to reduce the domain-shift effect in brain MRI segmentation results of the automated deep learning segmentation approach.

Compression of mri images with brain pathology based on Haaar wavelet

Currently wavelets are widely used in various fields related to the processing and analysis of signals and images. One application is the task of image compression. The advantage of compression using wavelet algorithms is that they work with the entire image and not with its individual blocks. At high compression ratios this avoids a block structure. This paper analyzes the effectiveness of the compression algorithm for medical MRI images with brain pathology which is based on the Haar wavelet transform. Implementation effective compression methods will allow storing a larger number of patient tomograms on a central server without increasing storage volumes. Experiments were performed and the dependences of indicators for assessing the quality of compressed MRI images on the compression ratio were constructed. The algorithm with zeroing wavelet coefficients by a threshold showed a better result compared to zeroing by levels based on assessing the quality of a compressed MRI image using a set of corresponding metrics.

Image Fusion using Waveatom Transform for Medical Applications

Image fusion is the process of gathering all the important information of multiple images into a single fused image. It is used in many fields like medical, military, remote sensing etc... Image fusion in a medical field is extensively used by physicians for analysis and treatment, as the fused image contains complementary features present in different medical images obtained from imaging devices. The wave atom transform based medical image fusion is proposed as the potential capabilities of wave atoms have been exploded in many applications like image denoising, fingerprint identification, compression etc. Medical image techniques became important tool for medical diagnosis and treatment. Medical images of different modalities such as MRI, CT, PET, SPECT provide information in a limited domain. In medical resources, MRI and CT images contain structural information whereas PET and SPECT images contain functional Information. Thus, single modality does not provide complete and accurate information, both structural as well as functional information is required. So far, many medical image fusion methods have been developed. The easiest way to perform image fusion is to perform weighted average of input images pixel by pixel. But this method produces side effects such as artifacts & contrast reduction. The proposed method is experimented on various medical images and compared with recent state of fusion methods. Results prove that images obtained from proposed method have better clarity and enhanced information and are practically more helpful for better treatment and quick analysis.

IMAGING OF RHINOCEREBRAL MUCORMYCOSIS

Background: Rhinocerebral mucormycosis is a dangerous fungal infection seen predominantly in immune-compromised patients. Many cases were detected and treated during the Second wave of the COVID-19 pandemic. In this case series, we are describing the imaging ndings of the Rhinocerebral mucormycosis. A retrospective descriptive study of 46 cases done in Materials And Methods: Sree Siddhartha medical college, Tumkur, Karnataka, India, CT and MRI images are reviewed, analyzed and documented. CT Results: and MRI images of 46post COVID -19 patients showed involvement of the sinuses with extension to orbit, face and central nervous system. Two cases showed intracranial extension. The contrast images delineated the extent of the disease. Signicant bony erosions were seen in most of the patients. Contrast CT and Conclusion: MRI scans showed the spectrum of ndings in Rhinocerebral mucormycosis to describe the extent of the disease and its complications.