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Alzheimer’s disease has become a major concern in the healthcare domain as it is growing rapidly. Much research has been conducted to detect it from MRI images through various deep learning approaches.However, the problems of the availability of medical data and preserving the privacy of patients still exists. To mitigate this issue in Alzheimer’s disease detection, we implement the federated approach, which is found to be more efficient, robust, and consistent compared with the conventional approach. For this, we need deep excavation on various orientations of MRI images and transfer learning architectures. Then, we utilize two publicly available datasets (OASIS and ADNI) and design various cases to evaluate the performance of the federated approach. The federated approach achieves better accuracy and sensitivity compared with the conventional approaches in most of the cases. Moreover, the robustness of the proposed approach is also found to be better than the conventional approach. In our federated approach, MobileNet, a low-cost transfer learning architecture, achieves the highest 95.24%, 81.94%, and 83.97% accuracy in the OASIS, ADNI, and merged (ADNI + OASIS) test sets, which is much higher than the achieved performance in the conventional approach. Furthermore, in the proposed approach, only the weights of the model are shared, which keeps the original MRI images in their respective hospital or institutions, preserving privacy in the healthcare domain.

Brain cancer ranks tenth on the list of leading causes of death in both men and women. Biopsy is one of the most used methods for diagnosing cancer. However, the biopsy process is quite dangerous and take a long time to reach a decision. Furthermore, as the tumor size is rising quickly, non-invasive, automatic diagnostic equipment is required which can automatically detect the tumor and its stage precisely in a few seconds. In recent years, techniques based on Machine Learning and Deep Learning (DL) for detecting and classifying cancers has gained remarkable success in recent years. This paper suggested an ensemble method for detecting and classifying brain tumor and its stages using brain Magnetic Resonance Imaging (MRI). A modified InceptionResNetV2 pre-trained model is used for tumor detection from MRI image. After tumor detection, a combination of InceptionResNetV2 and Random Forest Tree (RFT) is used to determine the cancer stage, which includes glioma, meningioma, and pituitary cancer. The size of the dataset is small, so C-GAN (Cyclic Generative Adversarial Networks) is used to increase the dataset size. The experiment results demonstrate that the suggested tumor detection and tumor classification models achieve the accuracy of 99% and 98%, respectively. [ABSTRACT FROM AUTHOR]

Osteosarcoma is a malignant bone tumor that is extremely dangerous to human health. Not only does it require a large amount of work, it is also a complicated task to outline the lesion area in an image manually, using traditional methods. With the development of computer-aided diagnostic techniques, more and more researchers are focusing on automatic segmentation techniques for osteosarcoma analysis. However, existing methods ignore the size of osteosarcomas, making it difficult to identify and segment smaller tumors. This is very detrimental to the early diagnosis of osteosarcoma. Therefore, this paper proposes a Contextual Axial-Preserving Attention Network (CaPaN)-based MRI image-assisted segmentation method for osteosarcoma detection. Based on the use of Res2Net, a parallel decoder is added to aggregate high-level features which effectively combines the local and global features of osteosarcoma. In addition, channel feature pyramid (CFP) and axial attention (A-RA) mechanisms are used. A lightweight CFP can extract feature mapping and contextual information of different sizes. A-RA uses axial attention to distinguish tumor tissues by mining, which reduces computational costs and thus improves the generalization performance of the model. We conducted experiments using a real dataset provided by the Second Xiangya Affiliated Hospital and the results showed that our proposed method achieves better segmentation results than alternative models. In particular, our method shows significant advantages with respect to small target segmentation. Its precision is about 2% higher than the average values of other models. For the segmentation of small objects, the DSC value of CaPaN is 0.021 higher than that of the commonly used U-Net method. [ABSTRACT FROM AUTHOR]

Alzheimer’s Disease (AD) is the most common cause of dementia globally. It steadily worsens from mild to severe, impairing one’s ability to complete any work without assistance. It begins to outstrip due to the population ages and diagnosis timeline. For classifying cases, existing approaches incorporate medical history, neuropsychological testing, and Magnetic Resonance Imaging (MRI), but efficient procedures remain inconsistent due to lack of sensitivity and precision. The Convolutional Neural Network (CNN) is utilized to create a framework that can be used to detect specific Alzheimer’s disease characteristics from MRI images. By considering four stages of dementia and conducting a particular diagnosis, the proposed model generates high-resolution disease probability maps from the local brain structure to a multilayer perceptron and provides accurate, intuitive visualizations of individual Alzheimer’s disease risk. To avoid the problem of class imbalance, the samples should be evenly distributed among the classes. The obtained MRI image dataset from Kaggle has a major class imbalance problem. A DEMentia NETwork (DEMNET) is proposed to detect the dementia stages from MRI. The DEMNET achieves an accuracy of 95.23%, Area Under Curve (AUC) of 97% and Cohen’s Kappa value of 0.93 from the Kaggle dataset, which is superior to existing methods. We also used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to predict AD classes in order to assess the efficacy of the proposed model.

Image classification is a significant way in the field of image processing to automatically categorize large numbers of images. Brain tumor classification is mainly a helpful and widely desired process in the medical system. Brain tumor classification is a significant way to automatically categorize brain tumors images. Many methods have been introduced for solving the classification task with leverage low-level features. However, it has few limitations for achieving the higher accuracy of image tumor classification with minimum time. To overcome the issue, this study has proposed a novel technique called Regularized Anisotropic Filtered Tanimoto Indexive Deep Multilayer Perceptive Connectionist Network (RAFTIDMPCN), consisting of many layers of nodes for deeply analyzing the input and providing better classification results. The proposed architecture helps in improving the accuracy and reducing the time. The input layer receives the number of MRI images and natural image datasets collected from the dataset. Then the images are sent to the first hidden layer where the preprocessing is carried out to improve the image quality by removing the noise pixels using Regularized Anisotropic diffusion filtering technique. Followed by, shape, color, texture, and size features of input images are extracted in the second hidden layer. The classification is performed at the third hidden layer based on the Tanimoto similarity measure. Finally, the Heaviside step activation function is applied to obtain the classification results with higher accuracy. Experimental evaluation is carried out with different qualitative and quantitative results discussion by using brain tumor MRI dataset and natural image datasets. The obtained results indicate that the proposed technique provides better results in terms of Peak signal to noise ratio, accuracy, false-positive rate, time complexity, and space complexity. The analyzed results show the superior performance of our proposed RAFTIDMPCN technique accuracy by 6%, minimizes the false-positive rate by 40%, and time complexity by 11% in brain tumor detection when compared with the two state-of-the-art methods. This paper also presents several discoveries that could be helpful to the neurological community.

This study was to evaluate the performance of magnetic resonance imaging (MRI) reconstruction algorithm based on convolutional neural network (CNN) in the diagnosis of ankle injury of football player. An MRI image reconstruction algorithm based on CNN (CNN-MRI) was constructed, and it was compared with the generalized autocalibrating partially parallel acquisitions (GRAPPA), iterative self-consistent parallel imaging reconstruction from arbitrary k-space (SPIRiT), and simultaneous autocalibrating and k-space estimation (SAKE) in MRI image of patients with ankle injury. They were compared regarding MRI image reconstruction quality (image score, peak signal-to-noise ratio (PSNR), structural similarity (SSIM), root mean square error (RMSE), and running time) and the assessment accuracy for each lesion. The detection rate of ankle joint injury and evaluation accuracy of the bone contusion, ligament injury, tendon injury focus, and different stages of osteochondral injury of the patients were recorded. The reconstructed image score, PSNR, SSIM, RMSE, and running time were calculated accordingly. The results showed that the detection rates of osteochondral injury (97.58%), joint effusion (91.21%), and soft tissue swelling (93.47%) in the CNN-MRI group were higher than those in GRAPPA (81.14%, 74.24%, and 76.11%), SPIRiT (80.66%, 73.66%, and 74.87%), and SAKE (86.96%, 82.61%, and 80.5%) groups (P < 0.05). The number of patients with 5 scores of reconstructed images in the CNN-MRI group (19 cases) was remarkably higher than that in GRAPPA (12 cases), SPIRiT (11 cases), and SAKE groups (14 cases) (P < 0.05). PSNR and SSIM of reconstructed image of patients in CNN-MRI group were much higher in contrast to those in GRAPPA, SPIRiT, and SAKE groups (P < 0.05), while the RMSE and running time were much lower than other algorithms (P < 0.05). In addition, the accuracy of evaluating the osteochondral injury in stage I and stage II for the patients in the CNN-MRI group was obviously higher than that in GRAPPA, SPIRiT, and SAKE groups (P < 0.05). In short, CNN-MRI algorithm could greatly improve the quality and PSNR of MRI reconstructed images, shorten the running time, and had excellent performance in showing the details of tendon injury, ligament injury, and bone contusion lesions. Moreover, it had a good effect on image processing of football players, could improve the accuracy of the diagnosis of ankle joint injuries, and could assist clinicians in the imaging diagnosis of ankle joint injuries of the football players. [ABSTRACT FROM AUTHOR]

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Accurate clinical cancer T-stage diagnosis is crucial for effective treatment. However, it is difficult, time-consuming, and laborious for physicians to recognize T-stage manually using rectal Magnetic Resonance Imaging (MRI) images. Machine learning methods have played important roles in medical image processing. With the goal of automatic rectal cancer T-stage prediction, we train the proposed Feature Extraction based Support Vector Machine (FE-SVM) model with the newly acquired dataset consisting of 147 patients' MRI images with primary rectal cancer. Our method adapts SVM as the training framework as SVM is effective enough for dealing with small datasets as opposed to state-of-the-art deep learning models. FE-SVM firstly extracts image similarity as an initial feature because the feature of image similarity can better reflect the differences among various types of MRI images, and the final 10-dimensional features are obtained by a 5-layers Autoencoder. To evaluate the performance of FE-SVM, we compared it with six benchmark models: CNN, Alexnet, Resnet18, Resnet50, Capsule Network, and Random Forest. FE-SVM outperforms these state-of-the-art models with significant evaluation scores. [ABSTRACT FROM AUTHOR]

Osteosarcoma is a malignant bone tumor that is extremely dangerous to human health. Not only does it require a large amount of work, it is also a complicated task to outline the lesion area in an image manually, using traditional methods. With the development of computer-aided diagnostic techniques, more and more researchers are focusing on automatic segmentation techniques for osteosarcoma analysis. However, existing methods ignore the size of osteosarcomas, making it difficult to identify and segment smaller tumors. This is very detrimental to the early diagnosis of osteosarcoma. Therefore, this paper proposes a Contextual Axial-Preserving Attention Network (CaPaN)-based MRI image-assisted segmentation method for osteosarcoma detection. Based on the use of Res2Net, a parallel decoder is added to aggregate high-level features which effectively combines the local and global features of osteosarcoma. In addition, channel feature pyramid (CFP) and axial attention (A-RA) mechanisms are used. A lightweight CFP can extract feature mapping and contextual information of different sizes. A-RA uses axial attention to distinguish tumor tissues by mining, which reduces computational costs and thus improves the generalization performance of the model. We conducted experiments using a real dataset provided by the Second Xiangya Affiliated Hospital and the results showed that our proposed method achieves better segmentation results than alternative models. In particular, our method shows significant advantages with respect to small target segmentation. Its precision is about 2% higher than the average values of other models. For the segmentation of small objects, the DSC value of CaPaN is 0.021 higher than that of the commonly used U-Net method.

The early detection and accurate diagnosis of liver fibrosis, a progressive and potentially serious liver condition, are crucial for effective medical intervention. Invasive methods like biopsies for diagnosis can be risky and expensive. This research presents a novel computer-aided diagnosis model for liver fibrosis using a hybrid approach of minimum redundancy maximum relevance (MRMR) feature selection, bidirectional long short-term memory (BiLSTM), and convolutional neural networks (CNN). The proposed model involves multiple stages, including image acquisition, preprocessing, feature representation, fibrous tissue identification, and classification. Notably, histogram equalization is employed to enhance image quality by addressing variations in brightness levels. Performance evaluation encompasses a range of metrics such as accuracy, precision, sensitivity, specificity, F1 score, and error rate. Comparative analyses with established methods like DCNN, ANN-FLI, LungNet22, and SDAE-GAN underscore the efficacy of the proposed model. The innovative integration of hybrid MRMR-BiLSTM-CNN architecture and the horse herd optimization algorithm significantly enhances accuracy and F1 score, even with small datasets. The model tackles the complexities of hyperparameter optimization through the IHO algorithm and reduces training time by leveraging MRMR feature selection. In practical application, the proposed hybrid MRMR-BiLSTM-CNN method demonstrates remarkable performance with a 97.8% accuracy rate in identifying liver fibrosis images. It exhibits high precision, sensitivity, specificity, and minimal error rate, showcasing its potential for accurate and non-invasive diagnosis., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

A Bayesian approach using wavelet coefficient modeling is proposed for de-noising additive white Gaussian noise in medical magnetic resonance imaging (MRI). In a parallel acquisition process, the magnetic resonance image is affected by white Gaussian noise, which is additive in nature. A normal inverse Gaussian probability distribution function is taken for modeling the wavelet coefficients. A Bayesian approach is implemented for filtering the noisy wavelet coefficients. The maximum likelihood estimator and median absolute deviation estimator are used to find the signal parameters, signal variances, and noise variances of the distribution. The minimum mean square error estimator is used for estimating the true wavelet coefficients. The proposed method is simulated on MRI. Performance and image quality parameters show that the proposed method has the capability to reduce the noise more effectively than other state-of-the-art methods. The proposed method provides 8.83%, 2.02%, 6.61%, and 30.74% improvement in peak signal-to-noise ratio, structure similarity index, Pratt’s figure of merit, and Bhattacharyya coefficient, respectively, over existing well-accepted methods. The effectiveness of the proposed method is evaluated by using the mean squared difference (MSD) parameter. MSD shows the degree of dissimilarity and is 0.000324 for the proposed method, which is less than that of the other existing methods and proves the effectiveness of the proposed method. Experimental results show that the proposed method is capable of achieving better signal-to-noise ratio performance than other tested de-noising methods.

Medical applications have a massive footprint in human's day‐to‐day life. Among that, MRI has a significant role, as it incorporates a significant impact on a brain tumour. Segmenting the tumour from MRI is substantial, but it is a time‐consuming process. Both the normal and abnormal tissues found in the brain look similar, which increases the difficulty of the tumour detection process. The digital image needs to be processed to obtain an exact tumour detection result. The tumour detection process comprises five different stages, such as pre‐processing, segmentation, feature extraction, feature selection, and classification. In this proposed work, hybrid wavelet Hadamard transform and grey‐level co‐occurrence matrix are included for feature extraction. Feature selection utilises sequential forward selection, which is an easy greedy search algorithm. This algorithm chooses only the predominant features for classification. The classification uses a hybrid support vector machine and adaptive emperor penguin optimisation. The experimental analysis shows the efficiency of the proposed work in terms of accuracy, specificity, and sensitivity values by computing the true positive, false positive, true negative, and false negative. [ABSTRACT FROM AUTHOR]

Detection of brain of tumor is a laborious task as it involves identification, segmentation followed by detection of the tumor. It is a very challenging task to envisage uncommon structures in the image of human brain [15]. An Image processing concept called MRI can be used to visualize different structures of human body. The Magnetic Resonance images (MRI) are used to detect the uncommon portions of human brain. This paper explores different noise removal methods accompanied by Balance-contrast enhancement technique (BCET) which results in increased accuracy. Segmentation followed by canny edge detection is performed on the improved images to detect the fine edges of the abnormalities present. The model attained an accuracy of at most 98% in detecting the tumor or the abnormality in a human brain which determines the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) of the brain is a safe and painless test that uses a magnetic field and radio waves to produce detailed images of the brain. FreeSurfer is a tool neuroscientists use to create models of structures in the brain. An average MRI analysis using FreeSurfer takes around 7 h on a central processing unit with 4 cores. Since execution time is so high, researchers are working on different ways to parallelize the software. Most efforts are concentrated on parallelization using multicore, specifically with OpenMP (an implementation of multithreading) reducing execution time around 20%. In this paper, we further accelerate the analysis time for FreeSurfer using the manycore processors, special multicore processors containing from dozens to thousands simpler independent cores. Specifically, we will use graphics processing unit (GPU) a manycore with thousands of simpler cores. Multicore and manycore using GPU acceleration are not mutually exclusive (we will call it GPU acceleration from now on), and we present an implementation that uses both types of accelerations (multicore and GPU). Results show that execution times using both accelerations reduce the analysis time by 70%. Manycore processors are specialist multicore processors designed for a high degree of parallel processing, containing numerous simpler, independent processor cores (from a few tens of cores to thousands or more). Manycore processors are used extensively in embedded computers and high-performance computing. [ABSTRACT FROM AUTHOR]

Through imaging and pathology studies, high-resolution MRI combined with remote sensing image homogenization algorithm was used to analyze the different components of brain demyelinated pseudotumor, and the stability of plaque was determined before demyelinating pseudotumor resection. A unified optical processing method combining image dynamic block idea and HIS transform is proposed. The image is dynamically divided into high-light sub-blocks, ordinary sub-blocks and excessive sub-sub-blocks, and then these sub-blocks are homogenized by HIS transform. Finally, the processed sub-blocks are linearly stretched to ensure the entire frame, and the MASK-based algorithm is analyzed. And several improved algorithms for remote sensing image homogenization algorithms. Experiments show that the MASK-based gradient stretching algorithm and the mean and variance based remote sensing image homogenization algorithm can achieve better uniformity, but there will be blur or color deviation. Therefore, a regional 2% linear stretching algorithm is proposed. The algorithm has great advantages in maintaining and enhancing the color of the original image. The MRI findings of demyelinating pseudotumor of the brain have certain characteristics. Dynamic observation of suspicious cases is after clinical hormonal examination, increase the rate of discovery, reduce or avoid irreversible damage to patients through surgery or radiotherapy. [ABSTRACT FROM AUTHOR]

Image fusion (IF) attracts the researchers in the areas of the medical industry as valuable information could be afforded through the fusion of images that enable the clinical decisions to remain effective. With the aim to render an effective image fusion, this paper concentrates on the Bayesian fusion approach, which is tuned optimally using the proposed Fractional Bird Swarm Optimization (Fractional-BSA). The medical image fusion is progressed using the MRI brain image taken from the BRATS database, and the source images of multimodalities are fused effectively to present an information-rich fused image. The source images are subjected to the Haar wavelet transform, and the Bayesian fusion is performed using the Bayesian parameter, which is determined optimally using the proposed Fractional-BSA optimization. The proposed optimization is the integration of the fractional theory in the standard Bird Swarm Optimization (BSA), which improves the effectiveness of image fusion. Unlike any other existing methods, the proposed Fractional-BSA-based Bayesian Fusion approach renders a good quality and complex-free fusion experience. The analysis reveals that the method outperformed the existing methods with maximal mutual information, maximal peak signal-to-noise ratio (PSNR), minimal root mean square error (RMSE) of 1.5665, 44.0857 dB, and 5.4840, respectively. [ABSTRACT FROM AUTHOR]

The MRI brain image detection and classification is very crucial for medical science. From last few years, various methods are being presented. Here in this paper, we proposed a novel approach to classify a given MRI brain image as normal or abnormal i.e. affected through tumor. The performed method firstly taken the wavelet transform for feature extraction from the images then applied principle component analysis to decrease the dimensions of features. Then the decreased features are being employed to kernel –support vector machine (k-SVM). Here the data has been taken for seven general brain diseases viz. glioma, Alzheimer's disease, Sarcoma, coma, LINN, IPOL, HPOL. We collected the data of 20 normal and 140 abnormal patients from Harvard Medical Laboratory website. Here four methods of SVM are performed such as L-Kernel, RBF, Poly, and NN. The comparative analysis is also being performed by the existing literatures which illustrates the result by DWT+PCA+KSVM by applying with GRB Kernel. Our classification gives fast and accurate results and assist doctors to distinguish between normal or abnormal to certain precision values. [ABSTRACT FROM AUTHOR]

Purpose: Gliomas are the most common primary tumor of the brain and are classified into grades I‐IV of the World Health Organization (WHO), based on their invasively histological appearance. Gliomas grading plays an important role to determine the treatment plan and prognosis prediction. In this study we propose two novel methods for automatic, non‐invasively distinguishing low‐grade (Grades II and III) glioma (LGG) and high‐grade (grade IV) glioma (HGG) on conventional MRI images by using deep convolutional neural networks (CNNs). Methods: All MRI images have been preprocessed first by rigid image registration and intensity inhomogeneity correction. Both proposed methods consist of two steps: (a) three‐dimensional (3D) brain tumor segmentation based on a modification of the popular U‐Net model; (b) tumor classification on segmented brain tumor. In the first method, the slice with largest area of tumor is determined and the state‐of‐the‐art mask R‐CNN model is employed for tumor grading. To improve the performance of the grading model, a two‐dimensional (2D) data augmentation has been implemented to increase both the amount and the diversity of the training images. In the second method, denoted as 3DConvNet, a 3D volumetric CNNs is applied directly on bounding image regions of segmented tumor for classification, which can fully leverage the 3D spatial contextual information of volumetric image data. Results: The proposed schemes were evaluated on The Cancer Imaging Archive (TCIA) low grade glioma (LGG) data, and the Multimodal Brain Tumor Image Segmentation (BraTS) Benchmark 2018 training datasets with fivefold cross validation. All data are divided into training, validation, and test sets. Based on biopsy‐proven ground truth, the performance metrics of sensitivity, specificity, and accuracy are measured on the test sets. The results are 0.935 (sensitivity), 0.972 (specificity), and 0.963 (accuracy) for the 2D Mask R‐CNN based method, and 0.947 (sensitivity), 0.968 (specificity), and 0.971 (accuracy) for the 3DConvNet method, respectively. In regard to efficiency, for 3D brain tumor segmentation, the program takes around ten and a half hours for training with 300 epochs on BraTS 2018 dataset and takes only around 50 s for testing of a typical image with a size of 160 × 216 × 176. For 2D Mask R‐CNN based tumor grading, the program takes around 4 h for training with around 60 000 iterations, and around 1 s for testing of a 2D slice image with size of 128 × 128. For 3DConvNet based tumor grading, the program takes around 2 h for training with 10 000 iterations, and 0.25 s for testing of a 3D cropped image with size of 64 × 64 × 64, using a DELL PRECISION Tower T7910, with two NVIDIA Titan Xp GPUs. Conclusions: Two effective glioma grading methods on conventional MRI images using deep convolutional neural networks have been developed. Our methods are fully automated without manual specification of region‐of‐interests and selection of slices for model training, which are common in traditional machine learning based brain tumor grading methods. This methodology may play a crucial role in selecting effective treatment options and survival predictions without the need for surgical biopsy. [ABSTRACT FROM AUTHOR]

Image segmentation has considered an important step in image processing. Fuzzy c-means (FCM) is one of the commonly used clustering algorithms because of its simplicity and effectiveness. However, FCM has the disadvantages of sensitivity to initial values, falling easily into local optimal solution and sensitivity to noise. To tackle these disadvantages, many optimization-based fuzzy clustering methods have been proposed in the literature survey. Particle swarm optimization (PSO) has good global optimization capability and a hybrid of FCM and PSO have improved accuracy over tradition FCM clustering. In this paper, a new image segmentation method based on Dynamic Particle swarm optimization (DPSO) and FCM algorithm along with the noise reduction mechanism is proposed. DPSO has the advantages to change the inertia weight and learning parameters dynamically. It adopts the inertia weight according to the fitness value and learning parameters along with time. The proposed method combines DPSO with FCM, using the advantages of global optimization searching and parallel computing of DPSO to find a superior result of the FCM algorithm. Moreover, a noise reduction mechanism based on the surrounding pixels is used for enhancing the anti-noise ability. The synthetic image and Magnetic Resonance Imaging (MRI) have been used for testing the proposed method by introducing different types of noises and the results show that the proposed algorithm has better performance and less sensitive to noise. [ABSTRACT FROM AUTHOR]

In medical image processing, brain tumor detection and segmentation is a challenging and time-consuming task. Magnetic Resonance Image (MRI) scan analysis is a powerful tool in the recent technology that makes effective detection of the abnormal tissues from the brain. In the brain image, the size of a tumor can be varied for different patients along with the minute details of the tumor. It is a difficult task to diagnose and classify the tumor from numerous images for the radiologists. This paper developed a brain tumor classification using a hybrid deep autoencoder with a Bayesian fuzzy clustering-based segmentation approach. Initially, the pre-processing stage is performed using the non-local mean filter for denoising purposes. Then the BFC (Bayesian fuzzy clustering) approach is utilized for the segmentation of brain tumors. After segmentation, robust features such as, information-theoretic measures, scattering transform (ST) and wavelet packet Tsallis entropy (WPTE) methods are used for the feature extraction process. Finally, a hybrid scheme of the DAE (deep autoencoder) based JOA (Jaya optimization algorithm) with a softmax regression technique is utilized to classify the tumor part for the brain tumor classification process. The proposed scheme is implemented in a MATLAB environment. The simulation results are conducted by the BRATS 2015 database which proved that the proposed approach obtained the high classification accuracy (98.5 %) when compared to other state-of-art methods. [ABSTRACT FROM AUTHOR]

A Bayesian approach using wavelet coefficient modeling is proposed for de-noising additive white Gaussian noise in medical magnetic resonance imaging (MRI). In a parallel acquisition process, the magnetic resonance image is affected by white Gaussian noise, which is additive in nature. A normal inverse Gaussian probability distribution function is taken for modeling the wavelet coefficients. A Bayesian approach is implemented for filtering the noisy wavelet coefficients. The maximum likelihood estimator and median absolute deviation estimator are used to find the signal parameters, signal variances, and noise variances of the distribution. The minimum mean square error estimator is used for estimating the true wavelet coefficients. The proposed method is simulated on MRI. Performance and image quality parameters show that the proposed method has the capability to reduce the noise more effectively than other state-of-the-art methods. The proposed method provides 8.83%, 2.02%, 6.61%, and 30.74% improvement in peak signal-to-noise ratio, structure similarity index, Pratt's figure of merit, and Bhattacharyya coefficient, respectively, over existing well-accepted methods. The effectiveness of the proposed method is evaluated by using the mean squared difference (MSD) parameter. MSD shows the degree of dissimilarity and is 0.000324 for the proposed method, which is less than that of the other existing methods and proves the effectiveness of the proposed method. Experimental results show that the proposed method is capable of achieving better signal-to-noise ratio performance than other tested de-noising methods. [ABSTRACT FROM AUTHOR]

Nowadays, SRR (super resolution image reconstruction) technology is a very effective method in improving spatial resolution of images and obtaining high-definition images. The SRR approach is an image late processing method that does not require any improvement in the hardware of the imaging system. In the SRR reconstruction model, it is the key point of the research to choose a proper cost function to achieve good reconstruction effect. In this paper, based on a lot of research, Lorenzian norm is employed as the error term, Tikhonov regularization is employed as the regularization term in the reconstruction model, and iteration method is employed in the process of SRR. In this way, the outliers and image edge preserving problems in SRR reconstruction process can be effectively solved and a good reconstruction effect can be achieved. A low resolution MRI brain image sequence with motion blur and several noises are used to test the SRR reconstruction algorithm in this paper and the reconstruction results of SRR reconstruction algorithm based on L2 norm are also be used for comparison and analysis. Results from experiments show that the SRR algorithm in this paper has better practicability and effectiveness.

Magnetic resonance imaging (MRI) is an essential radiology technique in clinical diagnosis, but its spatial resolution may not suffice to meet the growing need for precise diagnosis due to hardware limitations and thicker slice thickness. Therefore, it is crucial to explore suitable methods to increase the resolution of MRI images. Recently, deep learning has yielded many impressive results in MRI image super-resolution (SR) reconstruction. However, current SR networks mainly use convolutions to extract relatively single image features, which may not be optimal for further enhancing the quality of image reconstruction. In this work, we propose a multi-level feature extraction and reconstruction (MFER) method to restore the degraded high-resolution details of MRI images. Specifically, to comprehensively extract different types of features, we design the triple-mixed convolution by leveraging the strengths and uniqueness of different filter operations. For the features of each level, we then apply deconvolutions to upsample them separately at the tail of the network, followed by the feature calibration of spatial and channel attention. Besides, we also use a soft cross-scale residual operation to improve the effectiveness of parameter optimization. Experiments on lesion-free and glioma datasets indicate that our method obtains superior quantitative performance and visual effects when compared with state-of-the-art MRI image SR methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Recently computer aided diagnosis is largely used in many clinical processes to detect, predict and analyze many abnormalities. It is clear that in medical image processing, brain tumor classification and detection plays a significant task. MRI gives anatomical structure's information, and the potential abnormal tissues' information. Hence in this paper a new system is proposed for detection and classification of brain tumors. The proposed system consists of feature extraction and tumor classification. In feature extraction, Rough set theory (RST) is used and for classification task particle swam optimization neural network (PSONN) is trained and tested in order to classify the MRI brain images into normal and abnormal. [ABSTRACT FROM AUTHOR]

In this work, we come up with a novel method to merge the color channels created from multi modal images to form a RGB image and then highlight the tumor in the MRI brain images, detect and extract the tumor region from the human head scan MRI images. This extraction process is done purely based on multimedia color channels and the basic characteristics of a MRI brain image. The original multi modal grayscale images (FLAIR, T2, T1c) are used to build the Red, Green and Blue channels respectively and then combined to form a whole RGB image using the pseudo coloring process. The Pseudo colored brain images are then analyzed for the presence of specific colors in the images which clearly identifies the tumor region in the whole brain¬ image. The tumor and necrotic regions are shown in the RGB images in Golden Yellow color and White color respectively. Experimental study was done on BRATS high glioma imagesets. The main objective of this work is to detect the tumor in the human brain scans. A detailed study has to be done to detect the various regions of tumor. The results given by the proposed method reached good qualitative and quantitative level. [ABSTRACT FROM AUTHOR]

Nowadays, the medical images upsurge because of numerous major disease predictions. The medical image size needed vast volumes of memory and taking additional bandwidth for storage as well as transmission. With the aim of decreasing the size of the storage and as well for greater transmission image compression is needed. The prior research presented a completely automatic technique for skin lesion segmentation by leveraging 19-layer deep convolutional neural networks (CNNs), which is skilled end to-end and does not depend on past acquittance of the data. On the other hand it contains problem with storage for the period of the medical image transmission and transmission speed. With the aim of resolving this issue the presented system developed a compression method. In this research, magnetic resonance imaging are pre-processed by means of median filter. The preprocessed image is split into region of interest (ROI) and non region of interest by means of deep fully convolutional networks with Jaccard distance. Subsequent to the ROI segmentation, the ROI edge is taken out and encrypted with Freeman chain coding. At that point the ROI part is compressed by hybrid Lempel–Ziv–Welch and clipped histogram equalization (CHE). In CHE, ideal threshold value is chosen by means of particle swarm optimization technique for enhancing the brightness maintenance. The Non ROI part is compressed by means of enhanced zero tree wavelet (EZW). In this EZW technique, a preliminary threshold is chosen by making use of firefly algorithm. Lastly the decompression is carried out at the receiving end. The experimentation outcomes prove that the presented method attains greater performance when matched up with the previous technique in regard to compression ratio, peak signal to noise ratio and mean square error. [ABSTRACT FROM AUTHOR]

The paper focuses on how a brain image is being segmented to diagnose the brain tumor by using spatial fuzzy clustering algorithm. The segmentation process may cause error while diagonizing MR images due to the artifacts and noises exist in it. This may leads to misclassify the normal tissue as abnormal tissue. The proposed method is to segment normal tissues such as White Matter, Gray Matter, Cerebrospinal fluid and abnormal tissue like tumor part from MR images automatically. It comprises of pre-processing step, using wrapping based curvelet transform to remove noise and modified spatial fuzzy C-Means algorithm (Liu et al. in Tsinghua Sci Technol 19(6):578–595, 2014; Kwak and Choi in IEEE Trans Neural Netw 13(1):143–159, 2014) segments normal tissues by considering spatial information. The neighboring pixels are highly correlated and construct initial membership matrix randomly. The process is also intended to segment the tumor cells as well as the removal of background noises for smoothening the region, results in presenting segmented tissues and parameter evaluation to produce the algorithm efficiency. [ABSTRACT FROM AUTHOR]

Viewing the trend of beam shaping, beam steering, and metasurface holograms, a reconfigurable graphene-based metasurface is proposed and modeled using the method of moments combined with the generalized equivalent circuit (MoM-GEC) approach. The scattering state of each individual unit cell of the metasurface can be controlled by applying different biased voltages into the graphene antennas. In fact, the complex surface conductivity of graphene is introduced in our mathematical formulation in order to tune the magnitude and phase of the local reflection coefficient. Benefitting from this scalability, our graphene-based metasurface will regenerate a 3D medical hologram. The proposed approach is validated and paves the way for the generation a 3D digital holograms. There are several important areas where this study makes an original contribution such as medical imaging 3D displays. [ABSTRACT FROM AUTHOR]

One of the main tasks in MRI image reconstruction is to catch the picture characteristics such as interfaces and textures from incomplete frequency data, and the iteration methods are the most useful methods to the minimization problem. A new viewpoint of choosing the iteration stopping rules in image reconstruction problem is proposed. The reconstruction model based on compressive sensing theory consists of a data matching term and two penalty terms, wavelet sparse and total variation regularization term. Then the Bregman iteration with lagged diffiusivity fixed point iteration is used to solve the corresponding nonlinear Euler-Lagrange equation of image reconstruction model with incomplete frequency data. A real MRI image is used to test the proposed method in numerical experiments with diffierent stopping rules. The theoretical analysis illustrate that although the norm of objective functional decreases with respect to the number of iteration, it cannot ensure the reconstructed image is the desired optimization image. [ABSTRACT FROM AUTHOR]

Background/Objectives: It is to sustain the test accuracy and improve diagnostic capability of self-developed aid in Dunn view to confirm modification of pistol grip in neck of femur that causes anterior femoro acetabular impingement. Method/Statistical Analysis: The test was performed before and after the usage of the self-developed aid in the patients referred to the test of Dunn view (45 and 90-degree flexion) who were scheduled with hip arthroscopy. a-angle and femoral head-neck offset were measured by oblique transverse images in MRI test and statistical significance was analyzed with paired T-test. Findings: In the MRI images and Dunn 45° and 90° images before and after ancillary device use, as a result of measuring and analyzing a-angle, it was not statistically significant at 45° (P>0.05), but it was found there was statistical significance in 90° (P<0.05). In the MRI images and Dunn 45° and 90° images before and after ancillary device use, as a result of measuring and analyzing femoral head-neck offset, it was found there was statistical significance in both 45° and 90° (P<0.05). Improvements/Applications: Upon the comparison results of unidirectional image and MRI test before and after the use of the aid in this study, statistically significant outcomes were found in 45 and 90 degree of Dunn view after the use of the aid. Therefore, it is considered that they could be used as the foundation data in the image studies with Dunn view. [ABSTRACT FROM AUTHOR]

A support vector machine (SVM) classifier is being proposed for characterization of cerebrum tissues in attractive reverberation photographs (MRI). A wavelet based absolutely surface highlights set is determined. The most helpful surface capacities are extricated from standard just as tumor districts through the utilization of spatial dark dimension reliance strategy. The proposed strategy settles the enormous inconvenience of class procedures. These most proficient capacities are then used to classifications the cerebrum tissues into favorable and threatening tumor. The general execution of the arrangement of principles is assessed completely dependent on a succession of mind tumor pictures. [ABSTRACT FROM AUTHOR]

One of the main tasks in MRI image reconstruction is to catch the picture characteristics such as interfaces and textures from incomplete frequency data, and the iteration methods are the most useful methods to the minimization problem. A new viewpoint of choosing the iteration stopping rules in image reconstruction problem is proposed. The reconstruction model based on compressive sensing theory consists of a data matching term and two penalty terms, wavelet sparse and total variation regularization term. Then the Bregman iteration with lagged diffusivity fixed point iteration is used to solve the corresponding nonlinear Euler-Lagrange equation of image reconstruction model with incomplete frequency data. A real MRI image is used to test the proposed method in numerical experiments with different stopping rules. The theoretical analysis illustrate that although the norm of objective functional decreases with respect to the number of iteration, it cannot ensure the reconstructed image is the desired optimization image. [ABSTRACT FROM AUTHOR]