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Generalized rough fuzzy c-means algorithm for brain MR image segmentation

机译:大脑MR图像分割的广义粗糙模糊c均值算法。

摘要

Fuzzy sets and rough sets have been widely used in many clustering algorithms for medical image segmentation, and have recently been combined together to better deal with the uncertainty implied in observed image data. Despite of their wide spread applications, traditional hybrid approaches are sensitive to the empirical weighting parameters and random initialization, and hence may produce less accurate results. In this paper, a novel hybrid clustering approach, namely the generalized rough fuzzy c-means (GRFCM) algorithm is proposed for brain MR image segmentation. In this algorithm, each cluster is characterized by three automatically determined rough-fuzzy regions, and accordingly the membership of each pixel is estimated with respect to the region it locates. The importance of each region is balanced by a weighting parameter, and the bias field in MR images is modeled by a linear combination of orthogonal polynomials. The weighting parameter estimation and bias field correction have been incorporated into the iterative clustering process. Our algorithm has been compared to the existing rough c-means and hybrid clustering algorithms in both synthetic and clinical brain MR images. Experimental results demonstrate that the proposed algorithm is more robust to the initialization, noise, and bias field, and can produce more accurate and reliable segmentations.
机译:模糊集和粗糙集已被广泛用于医学图像分割的许多聚类算法中,并且最近被组合在一起以更好地处理所观察图像数据中隐含的不确定性。尽管它们具有广泛的应用,但是传统的混合方法对经验加权参数和随机初始化敏感,因此可能会产生不太准确的结果。本文提出了一种新的混合聚类方法,即广义粗糙模糊c均值(GRFCM)算法,用于脑部MR图像分割。在该算法中,每个聚类的特征在于三个自动确定的粗模糊区域,因此,相对于其所在区域估计了每个像素的隶属度。每个区域的重要性由权重参数平衡,而MR图像中的偏置场则由正交多项式的线性组合建模。加权参数估计和偏置字段校正已合并到迭代聚类过程中。我们的算法已在合成和临床脑部MR图像中与现有的粗糙c均值和混合聚类算法进行了比较。实验结果表明,该算法对初始化,噪声和偏置场具有更强的鲁棒性,并且可以产生更加准确和可靠的分割。

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