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Journal articles on the topic 'MRI IMAGE'

Author: Grafiati

Published: 11 September 2023

Last updated: 31 July 2025

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1

Angadi, Sanjeevkumar, Mukesh Kumar Tripathi, Chudaman Devidasrao Sukte, and Shivendra Shivendra. "Medical image registration and classification using smell agent rat swarm optimization based deep Maxout network." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 3 (2025): 1908. https://doi.org/10.11591/ijeecs.v37.i3.pp1908-1917.

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Medical image registration (MIR) is a crucial task in clinical image processing, involving the alignment of images from different modalities, such as magnetic resonance imaging (MRI) and computed tomography (CT), across various time points and subjects. Despite numerous advancements, no universal method caters to all MIR applications. This paper introduces the smell agent rat swarm optimization based deep Maxout network (SARSO-DMN) for MIR and classification. This work aims to enhance the accuracy and efficiency of medical image alignment and classification, addressing the challenges posed by

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Sanjeevkumar, Angadi Mukesh Kumar Tripathi Chudaman Devidasrao Sukte Shivendra Shivendra. "Medical image registration and classification using smell agent rat swarm optimization based deep Maxout network." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 3 (2025): 1908–17. https://doi.org/10.11591/ijeecs.v37.i3.pp1908-1917.

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Medical image registration (MIR) is a crucial task in clinical image processing, involving the alignment of images from different modalities, such as magnetic resonance imaging (MRI) and computed tomography (CT), across various time points and subjects. Despite numerous advancements, no universal method caters to all MIR applications. This paper introduces the smell agent rat swarm optimization based deep Maxout network (SARSO-DMN) for MIR and classification. This work aims to enhance the accuracy and efficiency of medical image alignment and classification, addressing the challenges posed by

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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 (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 separat

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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 (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 histogra

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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

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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 (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 prop

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N., Rajalakshmi, Narayanan K., and Amudhavalli P. "Wavelet-Based Weighted Median Filter for Image Denoising of MRI Brain Images." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 1 (2018): 201–6. https://doi.org/10.11591/ijeecs.v10.i1.pp201-206.

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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 noi

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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 (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 an

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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 (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 inve

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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, an

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Sujata Tukaram Bhairnallykar, Et al. "T1- Weighted MRI Image Segmentation." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 2078–84. http://dx.doi.org/10.17762/ijritcc.v11i9.9208.

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Growing evidence in recent years indicates that interest in the development of automated image analysis techniques for medical imaging, especially with regard to the discipline of magnetic resonance imaging. T1-weighted MRI scans are often used for both diagnosis and monitoring various neurological disorders, making accurate segmentation of these images crucial for effective treatment planning. In this work, we offer a new method for T1-weighted MRI image segmentation using patch densenet, an image segmentation-specific deep learning architecture. Our method aims to improve the accuracy and ef

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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 (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 integra

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R., Deepa, and Narendran P. "Brain Tumor Detection Segmentation Techniques." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 207–12. https://doi.org/10.31142/ijtsrd9634.

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Computerized brain tumor detection from MRI images is one of the most challenging task in today's contemporary Medical imaging research. Magnetic Resonance Images are used to produce images of soft tissue of human body. It is used to analyze the human organs without the need for surgery. Automatic detection requires brain image most important and challenging aspect of computer aided clinical diagnostic tools. Noises present in the Brain MRI images are multiplicative noises and reductions of these noises are difficult task. The minute anatomical details should not be destroyed by the proces

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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.

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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 (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 remov

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Xie, Xiaoxiao, Zhen Li, Lu Bai, et al. "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 str

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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 (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 les

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Naresh, Ghorpade, and R. Bhapkar H. "Enhanced N-Cut and Watershed based Model for Brain MRI Segmentation." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 346–51. https://doi.org/10.35940/ijeat.E9535.069520.

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Segmentation of an image is most important and essential task in medical image processing, specifically while analyzing magnetic resonance (MR) image of brain clinically. during the clinical investigation of brain MRI images. Lot of research has been carried out for MRI segmentation but still it is challenging task. Hybrid approach which uses enhanced normalized cut and watershed transform to segment brain MRI images is developed in this paper. Watershed transform is used for the initial partitioning of the MRI, which creates primitive regions. In the next stage these primitive regions resembl

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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 wer

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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 Hausdrof

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., Swapnali Matkar. "IMAGE SEGMENTATION METHODS FOR BRAIN MRI IMAGES." International Journal of Research in Engineering and Technology 04, no. 03 (2015): 263–66. http://dx.doi.org/10.15623/ijret.2015.0403045.

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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 (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 ma

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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-

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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 (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 im

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Noor, Elaiza Abd Khalid, Firdaus Ismail Muhammad, Azri AB Manaf Muhammad, Firdaus Ahmad Fadzil Ahmad, and Ibrahim Shafaf. "MRI brain tumor segmentation: A forthright image processing approach." Bulletin of Electrical Engineering and Informatics 9, no. 3 (2020): 1024–31. https://doi.org/10.11591/eei.v9i3.2063.

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Brain tumor is a collection of cells that grow in an abnormal and uncontrollable way. It may affect the regular function of the brain since it grows inside the skull region. As a brain tumor can be possibly led to cancer, early detection in computed tomography (CT) or magnetic resonance imaging (MRI) scanned images are crucial. Thus, this paper proposed a forthright image processing approach towards detection and localization of brain tumor region The approach consists of a few stages such as pre-processing, edge detection and segmentation. The pre-processing stage converts the original image

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Tajuddin, Nur Wahyu, Bambang Satoto, Rini Indrati, Donny Kristanto Mulyantoro, Darmini Darmini, and Gatot Murti Wibowo. "Application of Fusion Technique with ImageJ Stacks Feature for Brain Tumor MRI Image Optimization." Asian Journal of Social and Humanities 2, no. 11 (2024): 2768–80. http://dx.doi.org/10.59888/ajosh.v2i11.359.

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Fusion techniques on MRI for brain tumors can provide comprehensive visualization by combining Axial T2-Flair and Axial T1-GD (T1-weighted post-contrast) sequence images. Fusion MRI in brain tumors is able to clearly display the location, size and characteristics of the tumor. However, not all institutions can install such additional fusion software due to significant additional costs. Therefore, this study aims to prove that the Stacks feature on ImageJ as an alternative can be optimal in visualizing brain tumor image information through MRI fusion techniques. This study used 17 image samples

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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 (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

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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 (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

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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 (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 bo

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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 st

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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.

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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 deta

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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

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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 compone

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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 (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 a

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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 (2022): 273–80. https://doi.org/10.11591/ijeecs.v25.i1.pp273-280.

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The technology 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 t

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Jiang, Mingfeng, Peihang Jia, Xin Huang, et al. "Frequency-Aware Diffusion Model for Multi-Modal MRI Image Synthesis." Journal of Imaging 11, no. 5 (2025): 152. https://doi.org/10.3390/jimaging11050152.

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Magnetic Resonance Imaging (MRI) is a widely used, non-invasive imaging technology that plays a critical role in clinical diagnostics. Multi-modal MRI, which combines images from different modalities, enhances diagnostic accuracy by offering comprehensive tissue characterization. Meanwhile, multi-modal MRI enhances downstream tasks, like brain tumor segmentation and image reconstruction, by providing richer features. While recent advances in diffusion models (DMs) show potential for high-quality image translation, existing methods still struggle to preserve fine structural details and ensure a

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Setyabudi, Bintang Kukuh Iman, Hernastiti Sedya Utami, Fani Susanto, and Kusnanto Mukti Wibowo. "Difference in Image Information Between DWI Sequence and DWI Blade for Optimization of Axial Brain." Proceedings Series on Health & Medical Sciences 5 (March 20, 2024): 140–44. http://dx.doi.org/10.30595/pshms.v5i.976.

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Background: Diffusion Weighted Image (DWI) sequence that utilizes the movement of molecules due to random thermal motion. The aim of this research is to determine the difference in image information between DWI sequence and DWI BLADE on axial brain MRI images for optimization and to find the most optimal sequence between DWI and DWI BLADE on axial brain MRI images. Method: This study used a quantitative experimental research that aims to determine image information and optimize brain MRI examinations between DWI sequence and DWI BLADE using the MRI Siemens Magnetom Amira 1.5 T at Dr. Oen Kanda

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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 (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-sc

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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 (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 mer

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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 (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 m

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Shereen, A. Taie, and Ghonaim Wafaa. "A new model for early diagnosis of Alzheimer's disease based on BAT-SVM classifier." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 759~766. https://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, for this reason we use it 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 dis

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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 an

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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 (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 segmentatio

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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 denoi

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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 (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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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 (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 revie

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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 ima

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Hoffmann, Nico, Florian Weidner, Peter Urban, et al. "Framework for 2D-3D image fusion of infrared thermography with preoperative MRI." Biomedical Engineering / Biomedizinische Technik 62, no. 6 (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

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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 (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 i

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