Novel Framework of Segmentation 3D MRI of Brain Tumors

Medical image segmentation is a crucial process for computer-aided diagnosis and surgery.Medical image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis and examination.Rician and speckle noise are different types of noise in magnetic resonance imaging(MRI)that affect the accuracy of the segmentation process negatively.Therefore,image enhancement has a significant role in MRI segmentation.This 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 image.The 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 tumor.The framework has been divided into three stages:removing speckle and Rician noise,the segmentation stage,and the feature extraction stage.The framework presents solutions for each problem at a different stage of the segmentation.In the first stage,the framework uses Vibrational Mode Decomposition(VMD)along with Block-matching and 3D filtering(Bm3D)algorithms to remove the Rician.Afterwards,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 noise.VMDand 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)respectively.The 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,respectively.The experiments that have been conducted have proved the efficiency of the proposed framework against classical filters such as Bilateral,Frost,Kuan,and Lee according to different levels of noise.The attention gate U-Net achieved 94.66 and 95.03 in the segmentation of free noise images in dice and accuracy,respectively.

Filter Bank Based Nonlocal Means for Denoising Magnetic Resonance Images

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.

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

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 intervention.In this paper,firstly the sparsity is initiated in clustering method that too rician noise is also incorporated for brain MR scans of AD subject.Secondly,a novel neighbor pixel constrained fuzzy c-means clustering algorithm is designed where topoloty-based selection of parsimonious neighbor pixels is automated.The adaptability in choice of neighbor pixel class outliers more justified object edge boundary which outperforms a dynamic cluster output.The proposed adaptive neighbor constrained deviation sparse variant fuzzy c-means clustering(AN_DsFCM)can withhold imposed sparsity and withstands rician noise at imposed sparse environment.This novel algorithm is applied for MRI of AD subjects and normative data is acquired to analyse clustering accuracy.The data processing pipeline of theoretically plausible proposition is elaborated in detail.The experimental results are compared with state-of-the-art fuzzy clustering methods for test MRI scans.Visual evaluation and statistical measures are studied to meet both image processing and clinical neurophysiology standards.Overall the performance of proposed AN_DsFCM is significantly better than other methods.