Brain tumor segmentation from magnetic resonance (MR) image is vital for both the diagnosis and treatment of brain cancers. To alleviate noise sensitivity and improve stability of segmentation, an effective hybrid clustering algorithm combined with fast guided filter is proposed for brain tumor segmentation in this paper. Preprocessing is performed using adaptive Wiener filtering combined with a fast guided filter. Then simple linear iterative clustering (SLIC) is utilized for pre-segmentation to effectively remove scatter. During the clustering, K-means[Formula: see text] and Gaussian kernel-based fuzzy C-means (K[Formula: see text]GKFCM) clustering algorithm are combined to segment, and the fast-guided filter is introduced into the clustering. The proposed algorithm not only improves the robustness of the algorithm to noise, but also improves the stability of the segmentation. In addition, the proposed algorithm is compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity and recall.

中文翻译：

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基于超像素的脑肿瘤分割和快速引导过滤器的混合聚类

从磁共振 (MR) 图像进行脑肿瘤分割对于脑癌的诊断和治疗都至关重要。为了减轻噪声敏感性，提高分割的稳定性，本文提出了一种结合快速引导滤波器的有效混合聚类算法用于脑肿瘤分割。使用结合快速引导滤波器的自适应维纳滤波来执行预处理。然后使用简单的线性迭代聚类（SLIC）进行预分割，以有效地消除分散。聚类时结合K-means[公式：见文]和基于高斯核的模糊C-均值（K[公式：见文]GKFCM）聚类算法进行分割，并在聚类中引入快速引导滤波器. 该算法不仅提高了算法对噪声的鲁棒性，同时也提高了分割的稳定性。此外，将所提出的算法与其他当前的分割算法进行了比较。结果表明，所提出的算法在准确性、敏感性、特异性和召回率方面表现更好。