Robust Fuzzy Clustering Algorithm Integrating Membership Degree and Pixel Alternating Guided Filtering

doi:10.19678/j.issn.1000-3428.0062286

Abstract

Abstract: Fuzzy Local Information C-Means(FLICM) clustering algorithm is a popular robust segmentation algorithms, even though it is only suitable for the processing of low noise images.Recently, the combination of FLICM algorithm and pixel guided membership degree filtering have slightly improved the noise suppression ability;however, this is still insufficient for the segmentation strong noise images.In this study, a kernel fuzzy clustering algorithm with alternate guidance of membership degree and pixel values is proposed by combining guided filtering with Kernel metric-based Weighted Fuzzy Local Information C-Means(KWFLICM) clustering algorithm.The pixel guided membership degree filtering module and membership degree guided pixel filtering module are introduced into the KWFLICM algorithm to construct a multi-objective kernel fuzzy clustering optimization model constrained by guided filtering.Thereafter, the iterative algorithm of the model is obtained by the least square method.In the iterative process, the pixel membership degree and pixel value of the input image are modified by pixel guided membership degree filtering and membership degree guided pixel filtering, respectively.This further improves the robustness of kernel fuzzy clustering algorithm for noisy image segmentation.The experimental results show, compared with existing homogeneous kernel fuzzy clustering algorithms, that the algorithm has outstanding performance in terms of evaluation indicators such as Misclassified Error(ME), Accuracy(ACC), Peak Signal to Noise Ratio(PSNR), and Jaccard Similarity(JS) coefficient under the interference of Rician noise.This demonstrates the better segmentation performance and strong robustness of the proposed algorithm.

Key words: image segmentation, guided filtering, fuzzy clustering, kernel function, local information, membership degree, least square method

摘要： 模糊局部信息C-均值（FLICM）聚类算法是目前应用较广泛的图像分割算法，然而仅适用于处理低噪声图像。FLICM算法与像素引导隶属度滤波的结合在一定程度上提高了噪声抑制能力，但仍无法满足强噪声图像的分割需求。联合引导滤波与基于核度量的加权模糊局部信息C-均值（KWFLICM）聚类算法，提出一种隶属度与像素值交替引导的核模糊聚类算法。将像素引导隶属度滤波模块和隶属度引导像素滤波模块引入KWFLICM算法，构造一种引导滤波约束的多目标核模糊聚类优化模型，采用最小二乘法对该模型进行迭代求解。在迭代过程中，通过像素引导隶属度滤波和隶属度引导像素滤波，分别修正输入图像的隶属度和像素值，进一步提高核模糊聚类算法对含噪图像的鲁棒性。实验结果表明，与同类核模糊聚类算法相比，该算法在莱斯噪声干扰下的误分率、精确度、峰值信噪比、Jaccard相似系数等评价指标上表现突出，具有更好的分割性能和更强的鲁棒性。

关键词: 图像分割, 引导滤波, 模糊聚类, 核函数, 局部信息, 隶属度, 最小二乘法

CLC Number:

TP391

QIAO Caicai, WU Chengmao, LI Changxing, WANG Jiaye. Robust Fuzzy Clustering Algorithm Integrating Membership Degree and Pixel Alternating Guided Filtering[J]. Computer Engineering, 2022, 48(8): 224-233.

乔彩彩, 吴成茂, 李昌兴, 王佳烨. 结合隶属度与像素交替引导滤波的鲁棒模糊聚类算法[J]. 计算机工程, 2022, 48(8): 224-233.

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