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Journal articles on the topic 'MRI IMAGE'
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Zhang, Huixian, Hailong Li, Jonathan R. Dillman, Nehal A. Parikh, and Lili He. "Multi-Contrast MRI Image Synthesis Using Switchable Cycle-Consistent Generative Adversarial Networks." Diagnostics 12, no. 4 (March 26, 2022): 816. http://dx.doi.org/10.3390/diagnostics12040816.
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Multi-contrast MRI images use different echo and repetition times to highlight different tissues. However, not all desired image contrasts may be available due to scan-time limitations, suboptimal signal-to-noise ratio, and/or image artifacts. Deep learning approaches have brought revolutionary advances in medical image synthesis, enabling the generation of unacquired image contrasts (e.g., T1-weighted MRI images) from available image contrasts (e.g., T2-weighted images). Particularly, CycleGAN is an advanced technique for image synthesis using unpaired images. However, it requires two separate image generators, demanding more training resources and computations. Recently, a switchable CycleGAN has been proposed to address this limitation and successfully implemented using CT images. However, it remains unclear if switchable CycleGAN can be applied to cross-contrast MRI synthesis. In addition, whether switchable CycleGAN is able to outperform original CycleGAN on cross-contrast MRI image synthesis is still an open question. In this paper, we developed a switchable CycleGAN model for image synthesis between multi-contrast brain MRI images using a large set of publicly accessible pediatric structural brain MRI images. We conducted extensive experiments to compare switchable CycleGAN with original CycleGAN both quantitatively and qualitatively. Experimental results demonstrate that switchable CycleGAN is able to outperform CycleGAN model on pediatric MRI brain image synthesis.
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Yang, Huan, Pengjiang Qian, and Chao Fan. "An Indirect Multimodal Image Registration and Completion Method Guided by Image Synthesis." Computational and Mathematical Methods in Medicine 2020 (June 30, 2020): 1–10. http://dx.doi.org/10.1155/2020/2684851.
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Multimodal registration is a challenging task due to the significant variations exhibited from images of different modalities. CT and MRI are two of the most commonly used medical images in clinical diagnosis, since MRI with multicontrast images, together with CT, can provide complementary auxiliary information. The deformable image registration between MRI and CT is essential to analyze the relationships among different modality images. Here, we proposed an indirect multimodal image registration method, i.e., sCT-guided multimodal image registration and problematic image completion method. In addition, we also designed a deep learning-based generative network, Conditional Auto-Encoder Generative Adversarial Network, called CAE-GAN, combining the idea of VAE and GAN under a conditional process to tackle the problem of synthetic CT (sCT) synthesis. Our main contributions in this work can be summarized into three aspects: (1) We designed a new generative network called CAE-GAN, which incorporates the advantages of two popular image synthesis methods, i.e., VAE and GAN, and produced high-quality synthetic images with limited training data. (2) We utilized the sCT generated from multicontrast MRI as an intermediary to transform multimodal MRI-CT registration into monomodal sCT-CT registration, which greatly reduces the registration difficulty. (3) Using normal CT as guidance and reference, we repaired the abnormal MRI while registering the MRI to the normal CT.
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Destyningtias, Budiani, Andi Kurniawan Nugroho, and Sri Heranurweni. "Analisa Citra Medis Pada Pasien Stroke dengan Metoda Peregangan Kontras Berbasis ImageJ." eLEKTRIKA 10, no. 1 (June 19, 2019): 15. http://dx.doi.org/10.26623/elektrika.v10i1.1105.
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<p>This study aims to develop medical image processing technology, especially medical images of CT scans of stroke patients. Doctors in determining the severity of stroke patients usually use medical images of CT scans and have difficulty interpreting the extent of bleeding. Solutions are used with contrast stretching which will distinguish cell tissue, skull bone and type of bleeding. This study uses contrast stretching from the results of CT Scan images produced by first turning the DICOM Image into a JPEG image using the help of the ImageJ program. The results showed that the histogram equalization method and statistical texture analysis could be used to distinguish normal MRI and abnormal MRI detected by stroke.</p><p><strong>Keywords : </strong>Stroke, MRI, Dicom, JPEG, ImageJ, Contrast Stretching<strong></strong></p><p> </p>
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Bellam, Kiranmai, N. Krishnaraj, T. Jayasankar, N. B. Prakash, and G. R. Hemalakshmi. "Adaptive Multimodal Image Fusion with a Deep Pyramidal Residual Learning Network." Journal of Medical Imaging and Health Informatics 11, no. 8 (August 1, 2021): 2135–43. http://dx.doi.org/10.1166/jmihi.2021.3763.
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Multimodal medical imaging is an indispensable requirement in the treatment of various pathologies to accelerate care. Rather than discrete images, a composite image combining complementary features from multimodal images is highly informative for clinical examinations, surgical planning, and progress monitoring. In this paper, a deep learning fusion model is proposed for the fusion of medical multimodal images. Based on pyramidal and residual learning units, the proposed model, strengthened with adaptive fusion rules, is tested on image pairs from a standard dataset. The potential of the proposed model for enhanced image exams is shown by fusion studies with deep network images and quantitative output metrics of magnetic resonance imaging and positron emission tomography (MRI/PET) and magnetic resonance imaging and single-photon emission computed tomography (MRI/SPECT). The proposed fusion model achieves the Structural Similarity Index Measure (SSIM) values of 0.9502 and 0.8103 for the MRI/SPECT and MRI/PET MRI/SPECT image sets, signifying the perceptual visual consistency of the fused images. Testing is performed on 20 pairs of MRI/SPECT and MRI/PET images. Similarly, the Mutual Information (MI) values of 2.7455 and 2.7776 obtained for the MRI/SPECT and MRI/PET image sets, indicating the model’s ability to capture the information content from the source images to the composite image. Further, the proposed model allows deploying its variants, introducing refinements on the basic model suitable for the fusion of low and high-resolution medical images of diverse modalities.
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Schramm, Georg, and Claes Nøhr Ladefoged. "Metal artifact correction strategies in MRI-based attenuation correction in PET/MRI." BJR|Open 1, no. 1 (November 2019): 20190033. http://dx.doi.org/10.1259/bjro.20190033.
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In hybrid positron emission tomography (PET) and MRI systems, attenuation correction for PET image reconstruction is commonly based on processing of dedicated MR images. The image quality of the latter is strongly affected by metallic objects inside the body, such as e.g. dental implants, endoprostheses, or surgical clips which all lead to substantial artifacts that propagate into MRI-based attenuation images. In this work, we review publications about metal artifact correction strategies in MRI-based attenuation correction in PET/MRI. Moreover, we also give an overview about publications investigating the impact of MRI-based attenuation correction metal artifacts on the reconstructed PET image quality and quantification.
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., Swapnali Matkar. "IMAGE SEGMENTATION METHODS FOR BRAIN MRI IMAGES." International Journal of Research in Engineering and Technology 04, no. 03 (March 25, 2015): 263–66. http://dx.doi.org/10.15623/ijret.2015.0403045.
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Singh, Ram, and Lakhwinder Kaur. "Noise-residue learning convolutional network model for magnetic resonance image enhancement." Journal of Physics: Conference Series 2089, no. 1 (November 1, 2021): 012029. http://dx.doi.org/10.1088/1742-6596/2089/1/012029.
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Abstract Magnetic Resonance Image (MRI) is an important medical image acquisition technique used to acquire high contrast images of human body anatomical structures and soft tissue organs. MRI system does not use any harmful radioactive ionized material like x-rays and computerized tomography (CT) imaging techniques. High-resolution MRI is desirable in many clinical applications such as tumor segmentation, image registration, edges & boundary detection, and image classification. During MRI acquisition, many practical constraints limit the MRI quality by introducing random Gaussian noise and some other artifacts by the thermal energy of the patient body, random scanner voltage fluctuations, body motion artifacts, electronics circuits impulse noise, etc. High-resolution MRI can be acquired by increasing scan time, but considering patient comfort, it is not preferred in practice. Hence, postacquisition image processing techniques are used to filter noise contents and enhance the MRI quality to make it fit for further image analysis tasks. The main motive of MRI enhancement is to reconstruct a high-quality MRI while improving and retaining its important features. The new deep learning image denoising and artifacts removal methods have shown tremendous potential for high-quality image reconstruction from noise degraded MRI while preserving useful image information. This paper presents a noise-residue learning convolution neural network (CNN) model to denoise and enhance the quality of noise-corrupted low-resolution MR images. The proposed technique shows better performance in comparison with other conventional MRI enhancement methods. The reconstructed image quality is evaluated by the peak-signal-to-noise ratio (PSNR) and structural similarity index (SSIM) metrics by optimizing information loss in reconstructed MRI measured in mean squared error (MSE) metric.
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Yan, Rong. "The Value of Convolutional-Neural-Network-Algorithm-Based Magnetic Resonance Imaging in the Diagnosis of Sports Knee Osteoarthropathy." Scientific Programming 2021 (July 2, 2021): 1–11. http://dx.doi.org/10.1155/2021/2803857.
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The application value of the convolutional neural network (CNN) algorithm in the diagnosis of sports knee osteoarthropathy was investigated in this study. A network model was constructed in this experiment for image analysis of magnetic resonance imaging (MRI) technology. Then, 100 cases of sports knee osteoarthropathy patients and 50 healthy volunteers were selected. Digital radiography (DR) images and MRI images of all the research objects were collected after the inclusion of the two groups. Besides, the important physiological representations were extracted from their image data graphs, and the hidden complex relationships were learned. The state without input results was judged through convolutional network calculation, and the result prediction was given. On this basis, there was an analysis of the diagnostic efficiency of traditional DR images and MRI images based on CNN for patients with sports knee osteoarthropathy. The results showed that the MRI images analyzed by the CNN model showed a more obvious display rate than DR images for some nonbone changes of osteoarthritis. The correlation coefficient between MRI image rating and visual analog scale (VAS) was 0.865, which was higher than 0.713 of DR image rating, with a statistical meaning ( P < 0.01 ). For cases with mild lesions, the number of cases detected by MRI based on CNN algorithm in 0–4 image rating was 15, 18, 10, 6, and 7, respectively, which was markedly better than that of DR images. In short, the MRI examination based on the CNN image analysis model could extract important physiological representations from the image data and learn the hidden complex relationships. The convolutional network was calculated to determine the state of the uninput results and give the result predictions. Moreover, MRI examination based on the CNN image analysis model had high overall diagnostic efficiency and grading diagnostic efficiency for patients with motor knee osteoarthropathy, which was of great significance in clinical practice.
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Odusami, Modupe, Rytis Maskeliūnas, and Robertas Damaševičius. "Pareto Optimized Adaptive Learning with Transposed Convolution for Image Fusion Alzheimer’s Disease Classification." Brain Sciences 13, no. 7 (July 8, 2023): 1045. http://dx.doi.org/10.3390/brainsci13071045.
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Alzheimer’s disease (AD) is a neurological condition that gradually weakens the brain and impairs cognition and memory. Multimodal imaging techniques have become increasingly important in the diagnosis of AD because they can help monitor disease progression over time by providing a more complete picture of the changes in the brain that occur over time in AD. Medical image fusion is crucial in that it combines data from various image modalities into a single, better-understood output. The present study explores the feasibility of employing Pareto optimized deep learning methodologies to integrate Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images through the utilization of pre-existing models, namely the Visual Geometry Group (VGG) 11, VGG16, and VGG19 architectures. Morphological operations are carried out on MRI and PET images using Analyze 14.0 software and after which PET images are manipulated for the desired angle of alignment with MRI image using GNU Image Manipulation Program (GIMP). To enhance the network’s performance, transposed convolution layer is incorporated into the previously extracted feature maps before image fusion. This process generates feature maps and fusion weights that facilitate the fusion process. This investigation concerns the assessment of the efficacy of three VGG models in capturing significant features from the MRI and PET data. The hyperparameters of the models are tuned using Pareto optimization. The models’ performance is evaluated on the ADNI dataset utilizing the Structure Similarity Index Method (SSIM), Peak Signal-to-Noise Ratio (PSNR), Mean-Square Error (MSE), and Entropy (E). Experimental results show that VGG19 outperforms VGG16 and VGG11 with an average of 0.668, 0.802, and 0.664 SSIM for CN, AD, and MCI stages from ADNI (MRI modality) respectively. Likewise, an average of 0.669, 0.815, and 0.660 SSIM for CN, AD, and MCI stages from ADNI (PET modality) respectively.
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Wu, Hongliang, Guocheng Chen, Guibao Zhang, and Minghua Dai. "Application of Multimodal Fusion Technology in Image Analysis of Pretreatment Examination of Patients with Spinal Injury." Journal of Healthcare Engineering 2022 (April 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/4326638.
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As one of the most common imaging screening techniques for spinal injuries, MRI is of great significance for the pretreatment examination of patients with spinal injuries. With rapid iterative update of imaging technology, imaging techniques such as diffusion weighted magnetic resonance imaging (DWI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and magnetic resonance spectroscopy are frequently used in the clinical diagnosis of spinal injuries. Multimodal medical image fusion technology can obtain richer lesion information by combining medical images in multiple modalities. Aiming at the two modalities of DCE-MRI and DWI images under MRI images of spinal injuries, by fusing the image data under the two modalities, more abundant lesion information can be obtained to diagnose spinal injuries. The research content includes the following: (1) A registration study based on DCE-MRI and DWI image data. To improve registration accuracy, a registration method is used, and VGG-16 network structure is selected as the basic registration network structure. An iterative VGG-16 network framework is proposed to realize the registration of DWI and DCE-MRI images. The experimental results show that the iterative VGG-16 network structure is more suitable for the registration of DWI and DCE-MRI image data. (2) Based on the fusion research of DCE-MRI and DWI image data. For the registered DCE-MRI and DWI images, this paper uses a fusion method combining feature level and decision level to classify spine images. The simple classifier decision tree, SVM, and KNN were used to predict the damage diagnosis classification of DCE-MRI and DWI images, respectively. By comparing and analyzing the classification results of the experiments, the performance of multimodal image fusion in the auxiliary diagnosis of spinal injuries was evaluated.
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Liu, Huanyu, Jiaqi Liu, Junbao Li, Jeng-Shyang Pan, and Xiaqiong Yu. "DL-MRI: A Unified Framework of Deep Learning-Based MRI Super Resolution." Journal of Healthcare Engineering 2021 (April 9, 2021): 1–9. http://dx.doi.org/10.1155/2021/5594649.
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Magnetic resonance imaging (MRI) is widely used in the detection and diagnosis of diseases. High-resolution MR images will help doctors to locate lesions and diagnose diseases. However, the acquisition of high-resolution MR images requires high magnetic field intensity and long scanning time, which will bring discomfort to patients and easily introduce motion artifacts, resulting in image quality degradation. Therefore, the resolution of hardware imaging has reached its limit. Based on this situation, a unified framework based on deep learning super resolution is proposed to transfer state-of-the-art deep learning methods of natural images to MRI super resolution. Compared with the traditional image super-resolution method, the deep learning super-resolution method has stronger feature extraction and characterization ability, can learn prior knowledge from a large number of sample data, and has a more stable and excellent image reconstruction effect. We propose a unified framework of deep learning -based MRI super resolution, which has five current deep learning methods with the best super-resolution effect. In addition, a high-low resolution MR image dataset with the scales of ×2, ×3, and ×4 was constructed, covering 4 parts of the skull, knee, breast, and head and neck. Experimental results show that the proposed unified framework of deep learning super resolution has a better reconstruction effect on the data than traditional methods and provides a standard dataset and experimental benchmark for the application of deep learning super resolution in MR images.
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Rajalakshmi, N., K. Narayanan, and P. Amudhavalli. "Wavelet-Based Weighted Median Filter For Image Denoising Of MRI Brain Images." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 1 (April 1, 2018): 201. http://dx.doi.org/10.11591/ijeecs.v10.i1.pp201-206.
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<p>Preliminary diagnosing of MRI images from the hospital cannot be relied on because of the chances of occurrence of artifacts resulting in degraded quality of image, while others may be confused with pathology. Obtained MRI image usually contains limited artifacts. It becomes complex one for doctors in analyzing them. By increasing the contrast of an image, it will be easy to analyze. In order to find the tumor part efficiently MRI brain image should be enhanced properly. The image enhancement methods mainly improve the visual appearance of MRI images. The goal of denoising is to remove the noise, which may corrupt an image during its acquisition or transmission, while retaining its quality. In this paper effectiveness of seven denoising algorithms viz. median filter, wiener filter, wavelet filter, wavelet based wiener, NLM, wavelet based NLM, proposed wavelet based weighted median filter(WMF) using MRI images in the presence of additive white Gaussian noise is compared. The experimental results are analyzed in terms of various image quality metrics.</p>
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Xie, Xiaoxiao, Zhen Li, Lu Bai, Ri Zhou, Canfeng Li, Xiaocheng Jiang, Jianwei Zuo, and Yulong Qi. "Deep Learning-Based MRI in Diagnosis of Fracture of Tibial Plateau Combined with Meniscus Injury." Scientific Programming 2021 (December 20, 2021): 1–8. http://dx.doi.org/10.1155/2021/9935910.
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This study aimed to explore the application value of magnetic resonance imaging (MRI) images based on deep learning algorithms in the diagnosis of tibial plateau fractures combined with meniscus injuries. The original MRI image was input into the deep learning convolutional neural network (CNN), and the knee joint undersampled and fully sampled MRI image data were used for training to obtain a neural network model that can effectively remove the noise and blur of the undersampled image. Then, the image was reconstructed by the Regridding model to obtain an image with less noise and clearer structure. At the same time, all subjects underwent knee MRI examinations, and algorithms were used to analyze the sensitivity, specificity, and accuracy of their images. It was found that of 160 menisci from 80 cases of tibial plateau fractures, 64 were normal meniscus and 88 were injured menisci. The sensitivity, specificity, and accuracy of optimized MRI in diagnosing fracture of tibial plateau combined with meniscus injury were 96.9%, 93.2%, and 95.3%, respectively. In conclusion, the restored MRI images have high sensitivity in the diagnosis of meniscus injury and high consistency with the intraoperative results. It suggests that the optimized MRI image is effective in the diagnosis of meniscus injury.
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Liang, Yingbo, and Jian Fu. "Watershed Algorithm for Medical Image Segmentation Based on Morphology and Total Variation Model." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 05 (April 8, 2019): 1954019. http://dx.doi.org/10.1142/s0218001419540193.
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The traditional watershed algorithm has the limitation of false mark in medical image segmentation, which causes over-segmentation and images to be contaminated by noise possibly during acquisition. In this study, we proposed an improved watershed segmentation algorithm based on morphological processing and total variation model (TV) for medical image segmentation. First of all, morphological gradient preprocessing is performed on MRI images of brain lesions. Secondly, the gradient images are denoised by the all-variational model. While retaining the edge information of MRI images of brain lesions, the image noise is reduced. And then, the internal and external markers are obtained by forced minimum technique, and the gradient amplitude images are corrected by using these markers. Finally, the modified gradient image is subjected to watershed transformation. The experiment of segmentation and simulation of brain lesion MRI image is carried out on MATLAB. And the segmentation results are compared with other watershed algrothims. The experimental results demonstrate that our method obtains the least number of regions, which can extract MRI images of brain lesions effectively. In addition, this method can inhibit over-segmentation, improving the segmentation results of lesions in MRI images of brain lesions.
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., Gautam. "Super Resolution MRI Using Generative Adversarial Networks." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3896–905. http://dx.doi.org/10.22214/ijraset.2021.37237.
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This paper proposes a new frame for MRI Image Enhancement from a low-resolution (LR) image obtain from an early used MRI machine to generate a high-resolution (HR) MRI image. For this we use Generative Adversarial Networks, which have proven well in image recovery task. Here we simultaneously train two models which is Generative model that captures the data distribution in the LR MRI images, and a discriminative model that estimates the probability that a sample came from the training data rather than generator. For training generator, we have to maximize the probability of discriminator of making a mistake in comparing the fake image. For discriminator the adversarial loss uses least squares in order to stabilize the training and for generator the function is a combination of a least square adversarial loss and a content term based on mean square error and image gradient to improve the quality of generated images of MRI.
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Dong, Jie, Yueying Zhang, Yun Meng, Tingxiao Yang, Wei Ma, and Huixin Wu. "Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional Networks." Stem Cells International 2022 (October 17, 2022): 1–9. http://dx.doi.org/10.1155/2022/8619690.
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This work focused on the application value of magnetic resonance imaging (MRI) image segmentation algorithm based on fully convolutional DenseNet neural network (FCDNN) in glioma diagnosis. In this work, based on the fully convolutional DenseNet algorithm, a new MRI image automatic semantic segmentation method cerebral gliomas semantic segmentation network (CGSSNet) was established and was applied to glioma MRI image segmentation by using the BraTS public dataset as research data. Under the same conditions, compare the differences of dice similarity coefficient (DSC), sensitivity, and Hausdroff distance (HD) between this algorithm and other algorithms in MRI image processing. The results showed that the CGSSNet network segmentation algorithm significantly improved the segmentation accuracy of glioma MRI images. In addition, its DSC, sensitivity, and HD values for glioma MRI images were 0.937, 0.811, and 1.201, respectively. Under different iteration times, the DSC, sensitivity, and HD values of the CGSSNet network segmentation algorithm are significantly better than other algorithms. It showed that the CGSSNet model based on the DenseNet can improve the segmentation accuracy of glioma MRI images, and has potential application value in clinical practice.
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Sun, Lifang, Xi Hu, Yutao Liu, and Hengyu Cai. "Image Features of Magnetic Resonance Imaging under the Deep Learning Algorithm in the Diagnosis and Nursing of Malignant Tumors." Contrast Media & Molecular Imaging 2021 (August 30, 2021): 1–8. http://dx.doi.org/10.1155/2021/1104611.
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In order to explore the effect of convolutional neural network (CNN) algorithm based on deep learning on magnetic resonance imaging (MRI) images of brain tumor patients and evaluate the practical value of MRI image features based on deep learning algorithm in the clinical diagnosis and nursing of malignant tumors, in this study, a brain tumor MRI image model based on the CNN algorithm was constructed, and 80 patients with brain tumors were selected as the research objects. They were divided into an experimental group (CNN algorithm) and a control group (traditional algorithm). The patients were nursed in the whole process. The macroscopic characteristics and imaging index of the MRI image and anxiety of patients in two groups were compared and analyzed. In addition, the image quality after nursing was checked. The results of the study revealed that the MRI characteristics of brain tumors based on CNN algorithm were clearer and more accurate in the fluid-attenuated inversion recovery (FLAIR), MRI T1, T1c, and T2; in terms of accuracy, sensitivity, and specificity, the mean value was 0.83, 0.84, and 0.83, which had obvious advantages compared with the traditional algorithm ( P < 0.05 ). The patients in the nursing group showed lower depression scores and better MRI images in contrast to the control group ( P < 0.05 ). Therefore, the deep learning algorithm can further accurately analyze the MRI image characteristics of brain tumor patients on the basis of conventional algorithms, showing high sensitivity and specificity, which improved the application value of MRI image characteristics in the diagnosis of malignant tumors. In addition, effective nursing for patients undergoing analysis and diagnosis on brain tumor MRI image characteristics can alleviate the patient’s anxiety and ensure that high-quality MRI images were obtained after the examination.
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Min, Liang, Yi Gu, Rui Xue, Yi Ren, and Bo Gao. "Composite MRI Task Construction from CT Images based on Deep Convolution Neural Network." Journal of Imaging Science and Technology 65, no. 3 (May 1, 2021): 30404–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2021.65.3.030404.
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Abstract In traditional CBCT guided radiotherapy, the conventional process is to scan a planned CT image of the patient before treatment, and use the CT image to prepare a treatment plan for the patient, and calculate the radiation dose with the electronic density information of the CT image to obtain the radiation dose that the patient needs to receive. Because CT images cannot be directly used to calculate the amount of data, in order to solve the problem of CT image attenuation corresponding to MRI image synthesis, the deep convolution network model is used to map the CT image to the MRI image, input the CT image, and synthesize the corresponding MRI image with the convolution network model in this article. The synthetic MRI image can be used for the same mode registration with the patient’s positioning MRI image, so as to solve the problem of inaccurate cross-membrane registration. The multi-mode synthesis and transformation of CT/MRI images have been realized. Experiments have proved that the method presented in this article is beneficial to reducing the radiation dose of patients, enabling patients to receive more accurate radiotherapy, so that the tumor part can be irradiated as much as possible and the normal tissues around the tumor can be irradiated less, so as to improve the therapeutic effect of tumor patients.
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Lee, Giljae, Hwunjae Lee, and Gyehwan Jin. "Analysis of Fitting Degree of MRI and PET Images in Simultaneous MRPET Images by Machine Learning Neural Networks." ScholarGen Publishers 3, no. 1 (December 28, 2020): 43–61. http://dx.doi.org/10.31916/sjmi2020-01-05.
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Simultaneous MR-PET imaging is a fusion of MRI using various parameters and PET images using various nuclides. In this paper, we performed analysis on the fitting degree between MRI and simultaneous MR-PET images and between PET and simultaneous MR-PET images. For the fitness analysis by neural network learning, feature parameters of experimental images were extracted by discrete wavelet transform (DWT), and the extracted parameters were used as input data to the neural network. In comparing the feature values extracted by DWT for each image, the horizontal and vertical low frequencies showed similar patterns, but the patterns were different in the horizontal and vertical high frequency and diagonal high frequency regions. In particular, the signal value was large in the T1 and T2 weighted images of MRI. Neural network learning results for fitting degree analysis were as follows. 1. T1-weighted MRI and simultaneous MR-PET image fitting degree: Regression (R) values were found to be Training 0.984, Validation 0.844, and Testing 0.886. 2. Dementia-PET image and Simultaneous MR-PET Image fitting degree: R values were found to be Training 0.970, Validation 0.803, and Testing 0.828. 3. T2-weighted MRI and concurrent MR-PET image fitting degree: R values were found to be Training 0.999, Validation 0.908, and Testing 0.766. 4. Brain tumor-PET image and Simultaneous MR-PET image fitting degree: R values were found to be Training 0.999, Validation 0.983, and Testing 0.876. An R value closer to 1 indicates more similarity. Therefore, each image fused in the simultaneous MR-PET images verified in this study was found to be similar. Ongoing study of images acquired with pulse sequences other than the weighted images in the MRI is needed. These studies may establish a useful protocol for the acquisition of simultaneous MR-PET images.
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Veress, Alexander I., Gregory Klein, and Grant T. Gullberg. "A Comparison of Hyperelastic Warping of PET Images with Tagged MRI for the Analysis of Cardiac Deformation." International Journal of Biomedical Imaging 2013 (2013): 1–14. http://dx.doi.org/10.1155/2013/728624.
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The objectives of the following research were to evaluate the utility of a deformable image registration technique known as hyperelastic warping for the measurement of local strains in the left ventricle through the analysis of clinical, gated PET image datasets. Two normal human male subjects were sequentially imaged with PET and tagged MRI imaging. Strain predictions were made for systolic contraction using warping analyses of the PET images and HARP based strain analyses of the MRI images. Coefficient of determinationR2values were computed for the comparison of circumferential and radial strain predictions produced by each methodology. There was good correspondence between the methodologies, withR2values of 0.78 for the radial strains of both hearts and from anR2=0.81andR2=0.83for the circumferential strains. The strain predictions were not statistically different(P≤0.01). A series of sensitivity results indicated that the methodology was relatively insensitive to alterations in image intensity, random image noise, and alterations in fiber structure. This study demonstrated that warping was able to provide strain predictions of systolic contraction of the LV consistent with those provided by tagged MRI Warping.
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Zhang, Weilan, Jingyi Zhu, Xiaohan Xu, and Guoguang Fan. "Synthetic MRI of the lumbar spine at 3.0 T: feasibility and image quality comparison with conventional MRI." Acta Radiologica 61, no. 4 (September 14, 2019): 461–70. http://dx.doi.org/10.1177/0284185119871670.
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Background Synthetic magnetic resonance imaging (MRI), which can generate multiple morphologic MR images as well as quantitative maps from a single sequence, is not widely used in the spine at 3.0 T. Purpose To investigate the feasibility of synthetic MRI of the lumbar spine in clinical practice at 3.0 T. Material and Methods Eighty-four patients with lumbar diseases underwent conventional T1-weighted images, T2-weighted images, short-tau inversion recovery (STIR) images, and synthetic MRI of the lumbar spine at 3.0 T. The quantitative and qualitative image quality and agreement for detection of spinal lesions between conventional and synthetic MRI were compared by two radiologists. Results The signal-to-noise ratios of synthetic MRI showed an inferior image quality in the vertebrae and disc, whereas were higher for spinal canal and fat on the synthetic T1-weighted, T2-weighted, and STIR images. The contrast-to-noise ratios of the synthetic MRI was superior to conventional sequences, except for the vertebrae–disc contrast-to-noise ratio on T1-weighted imaging ( P = 0.005). Image quality assessments showed that synthetic MRI had greater STIR fat suppression ( P < 0.001) and fluid brightness ( P = 0.014), as well as higher degree of artifacts ( P < 0.001) and worse spatial resolution ( P = 0.002). The inter-method agreements for detection of spinal lesions were substantial to perfect (kappa, 0.614–0.925). Conclusion Synthetic MRI is a feasible method for lumbar spine imaging in a clinical setting at 3.0-T MR. It provides morphologic sequences with acceptable image quality, good agreement with conventional MRI for detection of spinal lesions and quantitative image maps with a slightly shorter acquisition time compared with conventional MRI.
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Wei, Hui, Baolong Lv, Feng Liu, Haojun Tang, Fangfang Gou, and Jia Wu. "A Tumor MRI Image Segmentation Framework Based on Class-Correlation Pattern Aggregation in Medical Decision-Making System." Mathematics 11, no. 5 (February 28, 2023): 1187. http://dx.doi.org/10.3390/math11051187.
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Medical image analysis methods have been applied to clinical scenarios of tumor diagnosis and treatment. Many studies have attempted to optimize the effectiveness of tumor MRI image segmentation by deep learning, but they do not consider the optimization of local details and the interaction of global semantic information. Second, although medical image pattern recognition can learn representative semantic features, it is challenging to ignore useless features in order to learn generalizable embeddings. Thus, a tumor-assisted segmentation method is proposed to detect tumor lesion regions and boundaries with complex shapes. Specifically, we introduce a denoising convolutional autoencoder (DCAE) for MRI image noise reduction. Furthermore, we design a novel tumor MRI image segmentation framework (NFSR-U-Net) based on class-correlation pattern aggregation, which first aggregates class-correlation patterns in MRI images to form a class-correlational representation. Then the relationship of similar class features is identified to closely correlate the dense representations of local features for classification, which is conducive to identifying image data with high heterogeneity. Meanwhile, the model uses a spatial attention mechanism and residual structure to extract effective information of the spatial dimension and enhance statistical information in MRI images, which bridges the semantic gap in skip connections. In the study, over 4000 MRI images from the Monash University Research Center for Artificial Intelligence are analyzed. The results show that the method achieves segmentation accuracy of up to 96% for tumor MRI images with low resource consumption.
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Djan, Igor, Borislava Petrovic, Marko Erak, Ivan Nikolic, and Silvija Lucic. "Radiotherapy treatment planning: Benefits of CT-MR image registration and fusion in tumor volume delineation." Vojnosanitetski pregled 70, no. 8 (2013): 735–39. http://dx.doi.org/10.2298/vsp110404001d.
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Background/Aim. Development of imaging techniques, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), made great impact on radiotherapy treatment planning by improving the localization of target volumes. Improved localization allows better local control of tumor volumes, but also minimizes geographical misses. Mutual information is obtained by registration and fusion of images achieved manually or automatically. The aim of this study was to validate the CT-MRI image fusion method and compare delineation obtained by CT versus CT-MRI image fusion. Methods. The image fusion software (XIO CMS 4.50.0) was applied to delineate 16 patients. The patients were scanned on CT and MRI in the treatment position within an immobilization device before the initial treatment. The gross tumor volume (GTV) and clinical target volume (CTV) were delineated on CT alone and on CT+MRI images consecutively and image fusion was obtained. Results. Image fusion showed that CTV delineated on a CT image study set is mainly inadequate for treatment planning, in comparison with CTV delineated on CT-MRI fused image study set. Fusion of different modalities enables the most accurate target volume delineation. Conclusion. This study shows that registration and image fusion allows precise target localization in terms of GTV and CTV and local disease control.
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David S, Alex, Almas Begum, and Ravikumar S. "Content clustering for MRI Image compression using PPAM." International Journal of Engineering & Technology 7, no. 1.7 (February 5, 2018): 126. http://dx.doi.org/10.14419/ijet.v7i1.7.10631.
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Image compression helps to save the utilization of memory, data while transferring the images between nodes. Compression is one of the key technique in medical image. Both lossy and lossless compressions where used based on the application. In case of medical imaging each and every components of pixel is very important hence its nature to chose lossless compression medical images. MRI images are compressed after processing. Here in this paper we have used PPMA method to compress the MRI image. For retrieval of the compressed image content clustering method used.
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Shwetha, V., C. H. Renu Madhavi, and Kumar M. Nagendra. "Classification of Brain Tumors Using Hybridized Convolutional Neural Network in Brain MRI images." International Journal of Circuits, Systems and Signal Processing 16 (January 14, 2022): 561–70. http://dx.doi.org/10.46300/9106.2022.16.70.
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In this research article, we have proposed a novel technique to operate on the Magnetic Resonance Imaging (MRI) data images which can be classified as image classification, segmentation and image denoising. With the efficient utilization of MRI images the medical experts are able to identify the medical disorders such as tumors which are correspondent to the brain. The prime agenda of the study is to organize brain into healthy and brain with tumor in brain with the test MRI data as considered. The MRI based technique is an methodology to study brain tumor based information for the better detailing of the internal body images when compared to other technique such as Computed Tomography (CT).Initially the MRI image is denoised using Anisotropic diffusion filter, then MRI image is segmented using Morphological operations, to classify the images for the disorder CNN based hybrid technique is incorporated, which is associated with five different set of layers with the pairing of pooling and convolution layers for the comparatively improved performance than other existing technique. The considered data base for the designed model is a publicly available and tested KAGGLE database for the brain MRI images which has resulted in the accuracy of 88.1%.
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Kumar, L. Ravi, K. G. S. Venkatesan, and S.Ravichandran. "Cloud-enabled Internet of Things Medical Image Processing Compressed Sensing Reconstruction." International Journal of Scientific Methods in Intelligence Engineering Networks 01, no. 04 (2023): 11–21. http://dx.doi.org/10.58599/ijsmien.2023.1402.
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Deep learning compresses medical image processing in IoMT. CS-MRI acquires quickly. It has various medicinal uses due to its advantages. This lowers motion artifacts and contrast washout. Reduces patient pressure and scanning costs. CS-MRI avoids the Nyquist-Shannon sampling barrier. Parallel imagingbased fast MRI uses many coils to reconstruct MRI images with less raw data. Parallel imaging enables rapid MRI. This research developed a deep learning-based method for reconstructing CS-MRI images that bridges the gap between typical non-learning algorithms that employ data from a single image and enormous training datasets. Conventional approaches only reconstruct CS-MRI data from one picture. Reconstructing CS-MRI images. CS-GAN is recommended for CS-MRI reconstruction. For success. Refinement learning stabilizes our C-GAN-based generator, which eliminates aliasing artifacts. This improved newly produced data. Product quality increased. Adversarial and information loss recreated the picture. We should protect the image’s texture and edges. Picture and frequency domain data establish consistency. We want frequency and picture domain information to match. It offers visual domain data. Traditional CS-MRI reconstruction and deep learning were used in our broad comparison research. C-GAN enhances reconstruction while conserving perceptual visual information. MRI image reconstruction takes 5 milliseconds, allowing real-time analysis.
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Taime, Abderazzak, Aziz Khamjane, Jamal Riffi, and Hamid Tairi. "Improving the accuracy of the PET/MRI tridimensional multimodal rigid image registration based on the FATEMD." Radioelectronic and Computer Systems, no. 1 (March 7, 2023): 122–33. http://dx.doi.org/10.32620/reks.2023.1.10.
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The subject matter of the article is the improvement in the accuracy of multimodal image registration between PET and MRI images in the medical field. The focus of the article pertains to the importance of these images in diagnosis, interpretation, and surgical intervention. This study increased the accuracy of PET/MRI multimodal image registration achieved through a new approach based on the multi-resolution image decomposition. The tasks to be solved are: The study proposes a new method, the fast and adaptive three-dimensional mode decomposition (FATEMD), to generate multi-resolution components for accurate registration. The method used: The study uses the FATEMD approach, which estimates the transformation parameters of the registration from the PET image and the residue of the second level of the MRI image that is obtained after the extraction of the first two tridimensional intrinsic mode functions (TIMFs). The following results were obtained: The proposed method of multimodal registration between PET and MRI images involves the use of the fast and adaptive three-dimensional mode decomposition (FATEMD) approach. This approach was tested on 25 pairs of images from the Vanderbilt database and was found to have improved accuracy compared to the usual method, as shown through comparative studies using measures of mutual information, normalized mutual information, and entropy correlation coefficient. Conclusion. The main objective achieved in the study was to enhance the accuracy of PET/MRI multimodal image registration through the application of the FATEMD decomposition method. This approach is novel compared to traditional methods as it involves estimating the transformation parameters from the PET image and the second level residue of the MRI image, resulting in more precise outcomes as opposed to using just the PET and MRI images alone. The integration of multiple imaging techniques, such as PET and MRI, provides healthcare professionals with a more comprehensive view of a patient's anatomy and physiology, leading to enhanced diagnosis and treatment planning.
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Ding, Wei Li, Feng Jiang, and Jia Qing Yan. "Automatic Segmentation of the Skull in MRI Sequences Using Level Set Method." Applied Mechanics and Materials 58-60 (June 2011): 2370–75. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2370.
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Magnetic Resonance Imaging (MRI) has been widely used in clinical diagnose. Segmentation of these images obtained by MRI is a necessary procedure in medical image processing. In this paper, an improved level set algorithm was proposed to optimize the segmentation of MRI image sequences based on article [1]. Firstly, we add an area term and the edge indicator function to the total energy function for single image segmentation. Secondly, we presented a new method which uses the circumscribed polygon of the previous segmentation result as the initial contour of the next image to achieve automatic segmentation of image sequences. The algorithm was tested on MRI image sequences provided by Chuiyanliu Hospital, Chaoyang District of Beijing; the results have indicated that the proposed algorithm can effectively enhance the segmentation speed and quality of MRI sequences.
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Kortepeter, Mark G. "MRI: My Resonant Image." Annals of Internal Medicine 115, no. 9 (November 1, 1991): 749. http://dx.doi.org/10.7326/0003-4819-115-9-749.
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Vitone, Louis, Abraham Joel, Andrew Masters, and Simon Lea. "Obturator Hernia – MRI Image." Indian Journal of Surgery 75, no. 4 (September 18, 2012): 322. http://dx.doi.org/10.1007/s12262-012-0735-x.
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Hartshorne, M. F., L. K. Arata, B. B. Roberts, P. W. Wiest, and J. A. Sanders. "MRI/SPECT IMAGE FUSION." CLINICAL NUCLEAR MEDICINE 22, no. 3 (March 1997): 199. http://dx.doi.org/10.1097/00003072-199703000-00022.
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Taie, Shereen A., and Wafaa Ghonaim. "A new model for early diagnosis of alzheimer's disease based on BAT-SVM classifier." Bulletin of Electrical Engineering and Informatics 10, no. 2 (April 1, 2021): 759–66. http://dx.doi.org/10.11591/eei.v10i2.2714.
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Magnetic Resonance Images (MRI) of the Brain is a significant tool to diagnosis Alzheimer's disease due to its ability to measure regional changes in the brain that reflect disease progression to detect early stages of the disease. In this paper, we propose a new model that adopts Bat for parameter optimization problem of Support vector machine (SVM) to diagnose Alzheimer’s disease via MRI biomedical image. The proposed model uses MRI for biomedical image classification to diagnose three classes; normal controls (NC), mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The proposed model based on segmentation for the most involved areas in the disease hippocampus, the features of MRI brain images are extracted to build feature vector of the brain, then extracting the most significant features in neuroimaging to reduce the high dimensional space of MRI images to lower dimensional subspace, and submitted to machine learning classification technique. Moreover, the model is applied on different datasets to validate the efficiency which show that the new Bat-SVM model can yield promising acceptable level of accuracy reached to 95.36 % using maximum number of bats equal to 50 and number of generation equal to 10.
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Ahmed, Ahmed Shihab, and Hussein Ali Salah. "The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 1 (January 1, 2022): 273. http://dx.doi.org/10.11591/ijeecs.v25.i1.pp273-280.
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The technology <span>of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% </span>accuracy.
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Murugachandravel, J., and S. Anand. "Enhancing MRI Brain Images Using Contourlet Transform and Adaptive Histogram Equalization." Journal of Medical Imaging and Health Informatics 11, no. 12 (December 1, 2021): 3024–27. http://dx.doi.org/10.1166/jmihi.2021.3906.
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Human brain can be viewed using MRI images. These images will be useful for physicians, only if their quality is good. We propose a new method called, Contourlet Based Two Stage Adaptive Histogram Equalization (CBTSA), that uses Nonsubsampled Contourlet Transform (NSCT) for smoothing images and adaptive histogram equalization (AHE), under two occasions, called stages, for enhancement of the low contrast MRI images. The given MRI image is fragmented into equal sized sub-images and NSCT is applied to each of the sub-images. AHE is imposed on each resultant sub-image. All processed images are merged and AHE is applied again to the merged image. The clarity of the output image obtained by our method has outperformed the output image produced by traditional methods. The quality was measured and compared using criteria like, Entropy, Absolute Mean Brightness Error (AMBE) and Peak Signal to Noise Ratio (PSNR).
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YOUSIF, AHMED, Zaid Bin Omar, and Usman Ullah Sheikh. "A Survey on Multi-Scale Medical images Fusion Techniques: Brain Diseases." Journal of Biomedical Engineering and Medical Imaging 7, no. 1 (February 28, 2020): 18–38. http://dx.doi.org/10.14738/jbemi.71.7415.
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Brain diseases such as degenerative (alzheimer's disease), neoplastic disease (brain tumor like sarcoma, glioma) are considered an interesting topic areas in the medical image fusion diagnosis. Pixel-level image fusion techniques are designed to combine multiple/multi-scale input images into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. Since they are difficult to be summarized ; survey paper are characterized by (1) medical image definition , brain diseases challenges , analysis a various techniques for multi-scale image fusion with its own modalities, fusion rule, fusion strategy and dis-advantage ,Whilst used a database of medical images for medical Harvard School (brain diseases) which contains various groups of co-registered multi-modal images including MRI/CT, MRI/PET and PET/SPECT and MRI (T1/T2) images.
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Nandhagopal, N., C. Jaichander, and R. Ponniwalavan. "Image Classification using MRI Images in Brain Tumor." Asian Journal of Research in Social Sciences and Humanities 6, cs1 (2016): 422. http://dx.doi.org/10.5958/2249-7315.2016.00974.6.
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Hata, Junichi. "3. Introduction to MRI Image Analysis Using ImageJ." Japanese Journal of Radiological Technology 75, no. 1 (2019): 89–94. http://dx.doi.org/10.6009/jjrt.2019_jsrt_75.1.89.
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Jai Shankar, B., K. Murugan, A. Obulesu, S. Finney Daniel Shadrach, and R. Anitha. "MRI Image Segmentation Using Bat Optimization Algorithm with Fuzzy C Means (BOA-FCM) Clustering." Journal of Medical Imaging and Health Informatics 11, no. 3 (March 1, 2021): 661–66. http://dx.doi.org/10.1166/jmihi.2021.3365.
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Functional and anatomical information extraction from Magnetic Resonance Images (MRI) is important in medical image applications. The information extraction is highly influenced by the artifacts in the MRI images. The feature extraction involves the segmentation of MRI images. We present a MRI image segmentation using Bat Optimization Algorithm (BOA) with Fuzzy C Means (FCM) clustering. Echolocation of bats is utilized in Bat Optimization Algorithm. The proposed segmentation technique is evaluated with existing segmentation techniques. Results of experimentation shows that proposed segmentation technique outperforms existing methods and produces 98.5% better results.
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Fu, Qimao, Chuizhi Huang, Yan Chen, Nailong Jia, Jinghui Huang, and Changkun Lin. "Magnetic Resonance Imaging Image under Low-Rank Matrix Denoising Algorithm in the Diagnosis and Evaluation of Tibial Plateau Fracture Combined with Meniscus Injury." Scientific Programming 2021 (November 24, 2021): 1–9. http://dx.doi.org/10.1155/2021/6329020.
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This study was carried out to explore the diagnostic effect of magnetic resonance imaging (MRI) based on the low-rank matrix (LRM) denoising algorithm under gradient sparse prior for the tibial plateau fracture (TPF) combined with meniscus injury (TPF + MI). In this study, the prior information of the noise-free MRI image block was combined with the self-phase prior, the gradient prior of MRI was introduced to eliminate the ringing effect, and a new MRI image denoising algorithm was constructed, which was compared with the anisotropic diffusion fusion (ADF) algorithm. After that, the LRM denoising algorithm based on gradient sparse prior was applied to the diagnosis of 112 patients with TPF + MI admitted to hospital, and the results were compared with those of the undenoised MRI image. Then, the incidence of patients with all kinds of different meniscus injury parting was observed. A total of 66 cases (58.93%) of meniscus tears (MT) were found, including 56 cases (50.00%) of lateral meniscus (LM), 10 cases (8.93%) of medial meniscus (MM), 16 cases (14.29%) of meniscus contusion (MC), and 18 cases (16.07%) of meniscus degenerative injury (MDI). The incidences of MI in Schatzker subtypes were 0%, 53.33% (24/45), 41.67% (5/12), 76.47% (13/17), 78.94% (15/19), and 23.53% (4/17), showing no statistically significant difference ( P > 0.05 ), but the incidence of MT was 54.46% (61/112), which was greatly higher than that of MC (15.18% (17/112)), and the difference was statistically obvious ( P < 0.05 ). The diagnostic specificity (93.83%) and accuracy (95.33%) of denoised MRI images were dramatically higher than those of undenoised MRI images, which were 78.34% and 71.23%, respectively, showing statistically observable differences ( P < 0.05 ). In short, the algorithm in this study showed better denoising performance with the most retained image information. In addition, denoising MRI images based on the algorithm constructed in this study can improve the diagnostic accuracy of MI.
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Peni Agustin Tjahyaningtijas, Hapsari. "Brain Tumor Image Segmentation in MRI Image." IOP Conference Series: Materials Science and Engineering 336 (April 2018): 012012. http://dx.doi.org/10.1088/1757-899x/336/1/012012.
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Basir, Otman, and Kalifa Shantta. "Automatic MRI Brain Tumor Segmentation Techniques: A Survey." IRA-International Journal of Applied Sciences (ISSN 2455-4499) 16, no. 2 (April 20, 2021): 25. http://dx.doi.org/10.21013/jas.v16.n2.p2.
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Image segmentation plays a crucial role in recognizing image signification for checking and mining medical image records. Brain tumor segmentation is a complicated assignment in medical image analysis. It is challenging to identify precisely and extract that a portion of the image has abnormal tissues for further diagnosis and analysis. The method of segmenting a tumor from a brain MRI image is a highly concentrated medical science community field, as MRI is non-invasive. In this survey, brain MRI images' latest brain tumor segmentation techniques are addressed a thoroughgoing literature review. Besides, surveys the several approved techniques regularly applied for brain tumor MRI segmentation. Also, highlighting variances among them and reviews their abilities, pros, and weaknesses. Various approaches to image segmentation are described and explicated with the modern participation of several investigators.
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Jacobson, Geraldine, Gideon Zamba, Vicki Betts, M. Muruganandham, and Joni Buechler-Price. "Image-Based Treatment Planning of the Post-Lumpectomy Breast Utilizing CT and 3TMRI." International Journal of Breast Cancer 2011 (2011): 1–5. http://dx.doi.org/10.4061/2011/246265.
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Accurate lumpectomy cavity definition is critical in breast treatment planning. We compared contouring lumpectomy cavity volume and cavity visualization score (CVS) with CT versus 3T MRI. 29 patients were imaged with CT and 3T MRI. Seven additional boost planning sets were obtained for 36 image sets total. Three observers contoured the lumpectomy cavity on all images, assigning a cavity visualization score (CVS ) of 1 to 5. Measures of consistency and agreement for CT volumes were 98.84% and 98.62%, for T1 MRI were 95.65% and 95.55%, and for T2 MRI were 97.63% and 97.71%. The mean CT, T1 MRI, and T2 MRI CVS scores were 3.28, 3.38, and 4.32, respectively. There was a highly significant difference between CT and T2 scores () and between T1 and T2 scores (). Interobserver consistency and agreement regarding volumes were high for all three modalities with T2 MRI CVS the highest. MRI may contribute to target definition in selected patients.
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Mishra, Susmita, M. Prakash, A. Hafsa, and G. Anchana. "Anfis to Detect Brain Tumor Using MRI." International Journal of Engineering & Technology 7, no. 3.27 (August 15, 2018): 209. http://dx.doi.org/10.14419/ijet.v7i3.27.17763.
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Processing of Magnetic Resonance Imaging(MRI) is one of the widely known best techniques to diagnose brain tumor since it gives better results than ultrasound or X-Ray images. The main objective is to diagnose the presence and extraction of brain tumor using MRI images. Image preprocessing includes contrast stretching, noise filtering and Adaptive Histogram Equalization(AHE). AHE gives a graphical representation of digital image without enhancing above the desired level. The next stage involves transferring the redundant information in input image to reduced set of features is called feature selection and is done by color, shape or texture of an image. Image is segmented using incorporation of Artificial Neural Networks(ANN) and Fuzzy logic called Adaptive Neuro-Fuzzy Inference System(ANFIS) wherein we get the desired output to differentiate tumor affected and normal image with its severity level. Since we deal with uncertainty much more, fuzzy logic serves as a vibrant tool in representing human knowledge as IF-THEN rules. MATLAB has been implemented in detection and extraction of tumor at an early stage.
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Liu, Hujun, Hui Gao, and Fei Jia. "The Value of Convolutional Neural Network-Based Magnetic Resonance Imaging Image Segmentation Algorithm to Guide Targeted Controlled Release of Doxorubicin Nanopreparation." Contrast Media & Molecular Imaging 2021 (July 26, 2021): 1–10. http://dx.doi.org/10.1155/2021/9032017.
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There was an investigation of the auxiliary role of convolutional neural network- (CNN-) based magnetic resonance imaging (MRI) image segmentation algorithm in MRI image-guided targeted drug therapy of doxorubicin nanomaterials so that the value of drug-controlled release in liver cancer patients was evaluated. In this study, 80 patients with liver cancer were selected as the research objects. It was hoped that the CNN-based MRI image segmentation algorithm could be applied to the guided analysis of MRI images of the targeted controlled release of doxorubicin nanopreparation to analyze the imaging analysis effect of this algorithm on the targeted treatment of liver cancer with doxorubicin nanopreparation. The results of this study showed that the upgraded three-dimensional (3D) CNN-based MRI image segmentation had a better effect compared with the traditional CNN-based MRI image segmentation, with significant improvement in indicators such as accuracy, precision, sensitivity, and specificity, and the differences were all statistically marked ( p < 0.05 ). In the monitoring of the targeted drug therapy of doxorubicin nanopreparation for liver cancer patients, it was found that the MRI images of liver cancer patients processed by 3D CNN-based MRI image segmentation neural algorithm could be observed more intuitively and guided to accurately reach the target of liver cancer. The accuracy of targeted release determination of nanopreparation reached 80 ± 6.25%, which was higher markedly than that of the control group (66.6 ± 5.32%) ( p < 0.05 ). In a word, the MRI image segmentation algorithm based on CNN had good application potential in guiding patients with liver cancer for targeted therapy with doxorubicin nanopreparation, which was worth promoting in the adjuvant treatment of targeted drugs for cancer.
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Hoffmann, Nico, Florian Weidner, Peter Urban, Tobias Meyer, Christian Schnabel, Yordan Radev, Gabriele Schackert, et al. "Framework for 2D-3D image fusion of infrared thermography with preoperative MRI." Biomedical Engineering / Biomedizinische Technik 62, no. 6 (November 27, 2017): 599–607. http://dx.doi.org/10.1515/bmt-2016-0075.
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AbstractMultimodal medical image fusion combines information of one or more images in order to improve the diagnostic value. While previous applications mainly focus on merging images from computed tomography, magnetic resonance imaging (MRI), ultrasonic and single-photon emission computed tomography, we propose a novel approach for the registration and fusion of preoperative 3D MRI with intraoperative 2D infrared thermography. Image-guided neurosurgeries are based on neuronavigation systems, which further allow us track the position and orientation of arbitrary cameras. Hereby, we are able to relate the 2D coordinate system of the infrared camera with the 3D MRI coordinate system. The registered image data are now combined by calibration-based image fusion in order to map our intraoperative 2D thermographic images onto the respective brain surface recovered from preoperative MRI. In extensive accuracy measurements, we found that the proposed framework achieves a mean accuracy of 2.46 mm.
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Andotra, Abhinav Singh, and Sandeep Sharma. "MRI Image Enhancement: Optimized Filtering Mechanism for Achieving High Accuracy in Diagnose Process." Asian Journal of Computer Science and Technology 7, no. 1 (May 5, 2018): 66–70. http://dx.doi.org/10.51983/ajcst-2018.7.1.1827.
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Segmentation plays an important role in separating data from medicinal images and also helps in clinical findings. Segmentation is the way toward apportioning the image into different regions. MRI is utilized to extract images of delicate tissues of human body. It is utilized in analyzing the human organs without the requirement of surgery. For the most part MRI images contain a lot of noise caused by operator performance, equipment and the environment, which prompts genuine errors. MRI is a productive way in giving data in regards to the area of tumors and even the volume. The noise present in the MRI image can be evacuated by utilizing different de-noising procedures whichever is most appropriate method depending on the type of image obtained and afterward can be handled by any of the segmentation techniques. The noise in MRI images might be because of field strength, RF pulses, RF coil, voxel volume, or receiver bandwidth. In our proposed paper a review of different noise handling and filtering mechanism is conducted in order to enhance the quality of image. In this paper we modify the adaptive median filter by applying redundancy handling mechanism and enhance the contrast of image by applying histogram equivalence method.
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Theocharis, Stefanos, Eleftherios P. Pappas, Ioannis Seimenis, Panagiotis Kouris, Dimitrios Dellios, Georgios Kollias, and Pantelis Karaiskos. "Geometric distortion assessment in 3T MR images used for treatment planning in cranial Stereotactic Radiosurgery and Radiotherapy." PLOS ONE 17, no. 5 (May 23, 2022): e0268925. http://dx.doi.org/10.1371/journal.pone.0268925.
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Magnetic Resonance images (MRIs) are employed in brain Stereotactic Radiosurgery and Radiotherapy (SRS/SRT) for target and/or critical organ localization and delineation. However, MRIs are inherently distorted, which also impacts the accuracy of the Magnetic Resonance Imaging/Computed Tomography (MRI/CT) co-registration process. In this phantom-based study, geometric distortion is assessed in 3T T2-weighted images (T2WIs), while the efficacy of an MRI distortion correction technique is also evaluated. A homogeneous polymer gel-filled phantom was CT-imaged before being irradiated with 26 4-mm Gamma Knife shots at predefined locations (reference control points). The irradiated phantom was MRI-scanned at 3T, implementing a T2-weighted protocol suitable for SRS/SRT treatment planning. The centers of mass of all shots were identified in the 3D image space by implementing an iterative localization algorithm and served as the evaluated control points for MRI distortion detection. MRIs and CT images were spatially co-registered using a mutual information algorithm. The inverse transformation matrix was applied to the reference control points and compared with the corresponding MRI-identified ones to evaluate the overall spatial accuracy of the MRI/CT dataset. The mean image distortion correction technique was implemented, and resulting MRI-corrected control points were compared against the corresponding reference ones. For the scanning parameters used, increased MRI distortion (>1mm) was detected at areas distant from the MRI isocenter (>5cm), while median radial distortion was 0.76mm. Detected offsets were slightly higher for the MRI/CT dataset (0.92mm median distortion). The mean image distortion correction improves geometric accuracy, but residual distortion cannot be considered negligible (0.51mm median distortion). For all three datasets studied, a statistically significant positive correlation between detected spatial offsets and their distance from the MRI isocenter was revealed. This work contributes towards the wider adoption of 3T imaging in SRS/SRT treatment planning. The presented methodology can be employed in commissioning and quality assurance programmes of corresponding treatment workflows.
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Liu, Yawen, Haijun Niu, Pengling Ren, Jialiang Ren, Xuan Wei, Wenjuan Liu, Heyu Ding, et al. "Generation of quantification maps and weighted images from synthetic magnetic resonance imaging using deep learning network." Physics in Medicine & Biology 67, no. 2 (January 17, 2022): 025002. http://dx.doi.org/10.1088/1361-6560/ac46dd.
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Abstract Objective. The generation of quantification maps and weighted images in synthetic MRI techniques is based on complex fitting equations. This process requires longer image generation times. The objective of this study is to evaluate the feasibility of deep learning method for fast reconstruction of synthetic MRI. Approach. A total of 44 healthy subjects were recruited and random divided into a training set (30 subjects) and a testing set (14 subjects). A multiple-dynamic, multiple-echo (MDME) sequence was used to acquire synthetic MRI images. Quantification maps (T1, T2, and proton density (PD) maps) and weighted (T1W, T2W, and T2W FLAIR) images were created with MAGiC software and then used as the ground truth images in the deep learning (DL) model. An improved multichannel U-Net structure network was trained to generate quantification maps and weighted images from raw synthetic MRI imaging data (8 module images). Quantitative evaluation was performed on quantification maps. Quantitative evaluation metrics, as well as qualitative evaluation were used in weighted image evaluation. Nonparametric Wilcoxon signed-rank tests were performed in this study. Main results. The results of quantitative evaluation show that the error between the generated quantification images and the reference images is small. For weighted images, no significant difference in overall image quality or signal-to-noise ratio was identified between DL images and synthetic images. Notably, the DL images achieved improved image contrast with T2W images, and fewer artifacts were present on DL images than synthetic images acquired by T2W FLAIR. Significance. The DL algorithm provides a promising method for image generation in synthetic MRI techniques, in which every step of the calculation can be optimized and faster, thereby simplifying the workflow of synthetic MRI techniques.
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De, Arunava, Anup Kumar Bhattacharjee, Chandan Kumar Chanda, and Bansibadan Maji. "Entropy Maximization, Stationary Wavelet and DCT Based Segmentation, De-Noising and Progressive Transmission Technique for Diseased MRI Images." Applied Mechanics and Materials 197 (September 2012): 229–34. http://dx.doi.org/10.4028/www.scientific.net/amm.197.229.
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We have devised a way of segmentation and progressive transmission of diseased MRI images. We use Particle Swarm Optimization (PSO) to get the region of interest (ROI) of the diseased MRI image. We use the concept of Multi-resolution Wavelet analysis to de-noise the ROI. We use Stationary Wavelet Transform together with Soft Thresholding Technique for de-noising purpose. A variable mask is used to get the segmented image. Varying percentages of DCT coefficients are used for progressive transmission of the diseased MRI image. Clustering of the images using K-Means algorithm result in predominantly two cluster namely that of diseased cells and background. Test on various MRI images show that the small diseased objects are successfully extracted irrespective of the complexity of the background and difference in intensity levels and class sizes. The proposed method only transmits the diseased MRI for further diagnosis of the disease and treatment.
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Pitkänen, Johanna, Juha Koikkalainen, Tuomas Nieminen, Ivan Marinkovic, Sami Curtze, Gerli Sibolt, Hanna Jokinen, et al. "Evaluating severity of white matter lesions from computed tomography images with convolutional neural network." Neuroradiology 62, no. 10 (April 13, 2020): 1257–63. http://dx.doi.org/10.1007/s00234-020-02410-2.
Full textAbstract:
Abstract Purpose Severity of white matter lesion (WML) is typically evaluated on magnetic resonance images (MRI), yet the more accessible, faster, and less expensive method is computed tomography (CT). Our objective was to study whether WML can be automatically segmented from CT images using a convolutional neural network (CNN). The second aim was to compare CT segmentation with MRI segmentation. Methods The brain images from the Helsinki University Hospital clinical image archive were systematically screened to make CT-MRI image pairs. Selection criteria for the study were that both CT and MRI images were acquired within 6 weeks. In total, 147 image pairs were included. We used CNN to segment WML from CT images. Training and testing of CNN for CT was performed using 10-fold cross-validation, and the segmentation results were compared with the corresponding segmentations from MRI. Results A Pearson correlation of 0.94 was obtained between the automatic WML volumes of MRI and CT segmentations. The average Dice similarity index validating the overlap between CT and FLAIR segmentations was 0.68 for the Fazekas 3 group. Conclusion CNN-based segmentation of CT images may provide a means to evaluate the severity of WML and establish a link between CT WML patterns and the current standard MRI-based visual rating scale.