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首页> 外文期刊>Applied Soft Computing >A novel fuzzy clustering algorithm by minimizing global and spatially constrained likelihood-based local entropies for noisy 3D brain MR image segmentation
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A novel fuzzy clustering algorithm by minimizing global and spatially constrained likelihood-based local entropies for noisy 3D brain MR image segmentation

机译：一种新的模糊聚类算法，最大限度地减少噪声3D脑MR图像分割的全局和空间约束似乎基于似乎的基于似乎的局部熵

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摘要

In this paper, we propose a novel fuzzy clustering algorithm by minimizing global and spatially constrained likelihood-based local entropies (FCMGsLE) for segmenting noisy 3D brain magnetic resonance (MR) image volumes. For each voxel, in order to measure uncertainties that arise while identifying its class, two different entropies are defined. In particular, they measure the amount of uncertainties in terms of global entropy using fuzzifier weighted global membership function and spatially constrained likelihood-based local entropy using fuzzifier weighted local membership function. To mitigate the effect of noise and intensity inhomogeneity (IIH) or radio frequency (RF) inhomogeneity, the local membership function is induced by spatially constrained likelihood measure. These entropies are minimized through a fuzzy objective function to obtain the cluster prototypes and membership functions. The final membership function is obtained by integrating these global and local membership functions using weighted parameters. The algorithm is assessed both qualitatively and quantitatively on ten 3D volumes of simulated and clinical brain MR image data having high levels of noise and intensity inhomogeneity and a synthetic 3D image volume with Rician noise. The simulation results reveal that the proposed algorithm outperforms several state-of-the-art algorithms devised in recent past when evaluated in terms of segmentation accuracy, Dice similarity coefficient, partition coefficient, and partition entropy (C) 2020 Elsevier B.V. All rights reserved.
机译：在本文中，我们通过最小化基于空间约束的基于似的局部熵（FCMGSLE）来提出一种新的模糊聚类算法，用于分割噪声3D脑磁共振（MR）图像体积。对于每个体素，为了测量识别其类时出现的不确定性，定义了两种不同的熵。特别是，它们在全球熵使用模糊加权全球会员函数和使用模糊加权局部隶属函数的空间约束基于似然的局部熵的不确定性量。为了减轻噪声和强度不均匀性（IIH）或射频（RF）不均匀性的影响，通过空间约束的似然测量引起局部隶属函数。这些熵通过模糊目标函数最小化，以获得群集原型和隶属函数。通过使用加权参数集成这些全局和本地成员函数来获得最终的成员函数。该算法在定性和定量上评估了具有高水平噪声和强度不均匀性的模拟和临床脑MR图像数据的10卷的模拟和临床脑MR图像数据和具有瑞典噪声的合成3D图像体积。仿真结果表明，当在分割精度，骰子相似度系数，分区系数和分区熵（C）2020 Elsevier B.v方面，近过去，所提出的算法在最近过去的近似设计了几种最先进的算法。

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