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Novel Framework of Segmentation 3D MRI of Brain Tumors

Computers, Materials & Continua 2023年第2期74卷 3489-3502页

作者： Ibrahim Mahmoud El-Henawy Mostafa Elbaz Zainab H.Ali Noha Sakr Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt Department of Computer Science Faculty of Computers and InformaticsKafrelsheikh UniversityKafrelsheikhEgypt Department of Embedded Network Sys.and Tec. Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Medical image segmentation is a crucial process for computer-aided diagnosis and *** image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis and *** and speckle noise are different types of noise in magnetic resonance imaging(MRI)that affect the accuracy of the segmentation process ***,image enhancement has a significant role in MRI *** paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove rician and speckle noise using the best possible noise-free *** proposed techniques consider the values of Peak Signal to noise Ratio(PSNR)and the level of noise as inputs to the attention-U-Net model for segmentation of the *** framework has been divided into three stages:removing speckle and rician noise,the segmentation stage,and the feature extraction *** framework presents solutions for each problem at a different stage of the *** the first stage,the framework uses Vibrational Mode Decomposition(VMD)along with Block-matching and 3D filtering(Bm3D)algorithms to remove the ***,the most significant rician noise-free images are passed to the three different methods:Deep Residual Network(DeRNet),Dilated Convolution Auto-encoder Denoising Network(Di-Conv-AE-Net),andDenoising Generative Adversarial Network(DGAN-Net)for removing the speckle *** Bm3D have achieved PSNR values for levels of noise(0,0.25,0.5,0.75)for reducing the rician noise by(35.243,32.135,28.214,24.124)and(36.11,31.212,26.215,24.123)*** framework also achieved PSNR values for removing the speckle noise process for each level as follows:(34.146,30.313,28.125,24.001),(33.112,29.103,27.110,24.194),and(32.113,28.017,26.193,23.121)forDeRNet,Di-Conv-AE-Net,and DGAN-Net,*** experiments that have been conducted have proved the efficiency of the proposed framework a

关键词： MRI rician noise speckle noise segmentation deep learning

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Adaptive neighbor constrained deviation sparse variant fuzzy c-means clustering for brain MRI of AD subject

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Visual Informatics 2021年第4期5卷 67-80页

作者： Sukanta Ghosh Amlan Pratim Hazarika Abhijit Chandra Rajani K.Mudi Department of Instrumentation&Electronics Engineering Jadavpur UniversityKolkata700106India

Progression of Alzheimer’s disease(AD)bears close proximity with the tissue loss in the medial temporal lobe(MTL)and enlargement of lateral ventricle(LV).The early stage of AD,mild cognitive impairment(MCI),can be traced by diagnosing brain MRI scans with advanced fuzzy c-means clustering algorithm that helps to take an appropriate *** this paper,firstly the sparsity is initiated in clustering method that too rician noise is also incorporated for brain MR scans of AD ***,a novel neighbor pixel constrained fuzzy c-means clustering algorithm is designed where topoloty-based selection of parsimonious neighbor pixels is *** adaptability in choice of neighbor pixel class outliers more justified object edge boundary which outperforms a dynamic cluster *** proposed adaptive neighbor constrained deviation sparse variant fuzzy c-means clustering(AN_DsFCM)can withhold imposed sparsity and withstands rician noise at imposed sparse *** novel algorithm is applied for MRI of AD subjects and normative data is acquired to analyse clustering *** data processing pipeline of theoretically plausible proposition is elaborated in *** experimental results are compared with state-of-the-art fuzzy clustering methods for test MRI *** evaluation and statistical measures are studied to meet both image processing and clinical neurophysiology *** the performance of proposed AN_DsFCM is significantly better than other methods.

关键词： MRI Fuzzy c-means Neighbor information modeling Sparse rician noise AD

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Filter Bank Based Nonlocal Means for Denoising Magnetic Resonance Images

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Journal of Shanghai Jiaotong university(Science) 2014年第1期19卷 72-78页

作者：郭甜莉刘且根骆建华 School of Aeronautics and Astronautics Shanghai Jiaotong University

Image denoising is a classical problem in image processing. Its essential goal is to preserve the image features and to reduce noise effiectively. The nonlocal means(NL-means) filter is a successful approach proposed in recent years due to its patch similarity comparison. However, the accuracy of similarities in this algorithm degrades when it suffiers from heavy noise. In this paper, we introduce feature similarities based on a multichannel filter into NL-means filter. The multi-bank based feature vectors of each pixel in the image are computed by convolving from various orientations and scales to Leung-Malik set(edge, bar and spot filters), and then the similarities based on this information are computed instead of pixel intensity. Experiments are carried out with rician noise. The results demonstrate the superior performance of the proposed method. The wavelet-based method and traditional NL-means in term of both mean square error(MSE) and perceptual quality are compared with the proposed method, and structural similarity(SSIM) and quality index based on local variance(QILV) are given.

关键词： Leung-Malik(LM) filter nonlocal means similarity image denoising rician noise

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