Effective noise reduction fuzzy C-means algorithm for image segmentation

Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (29): 211-213.

• 图形、图像、模式识别 • Previous Articles Next Articles

Effective noise reduction fuzzy C-means algorithm for image segmentation

HE Yue，SHEN Xuanjing，ZENG Zheng

College of Computer Science and Technology，Jilin University，Changchun 130012，China

Received:1900-01-01 Revised:1900-01-01 Online:2011-10-11 Published:2011-10-11

一种有效抑制噪音的模糊C均值图像分割算法

何月，申铉京，曾铮

吉林大学计算机科学与技术学院，长春 130012

Abstract

Abstract: An effective noise reduction fuzzy C-means clustering algorithm is proposed.It reduces the impact of noise on cluster centers by constructing gray-median-based spatial information and pyramid structure.The pyramid structure is introduced to speed up the approach and the adaptive threshold selection of membership grade avoids the inflexibility of human threshold.Median images instead of original images are used to eliminate noise.Simulation results show that this method has a strong robustness for noise image and the segmentation results are more accurate.

Key words: noise reduction, fuzzy C-means clustering algorithm, pyramid structure, image segmentation

摘要： 提出一种能够有效抑制噪音的模糊C均值聚类算法，通过构造基于灰度-中值的空间信息和塔形结构减少噪音对聚类中心的影响，塔形结构的引入缩短了运算时间，通过自适应地选取隶属度阈值避免人为设定阈值的不灵活性，在图像分割时用中值图像代替源图像消除噪声点。仿真实验表明，该方法更加适合处理受噪音污染的图像，分割结果更加精确。

关键词: 抑制噪音, 模糊C均值聚类算法, 塔形结构, 图像分割

HE Yue，SHEN Xuanjing，ZENG Zheng. Effective noise reduction fuzzy C-means algorithm for image segmentation[J]. Computer Engineering and Applications, 2011, 47(29): 211-213.

何月，申铉京，曾铮. 一种有效抑制噪音的模糊C均值图像分割算法[J]. 计算机工程与应用, 2011, 47(29): 211-213.

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Effective noise reduction fuzzy C-means algorithm for image segmentation

一种有效抑制噪音的模糊C均值图像分割算法

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