摘要
为了提高图像分割算法的抗噪声性能,提出了一种改进的基于模糊C-均值聚类的图像分割算法.该算法首先根据邻域像素的隶属度矩阵来计算出像素和聚类中心的空间距离,然后利用空间距离和欧氏距离来重新确定像素和聚类中心的距离,最后利用新提取的距离特征和改进的FCM聚类算法对图像进行分割.实验结果表明,该算法能有效地提取目标图像,对噪声具有较强的鲁棒性,收敛速度快.
To enhance the noise immunity performance of the image segmentation algorithm, an improved algorithm for image segmentation based on fuzzy C-means clustering is proposed in this paper. The spatial distance between a pixel and the cluster center is calculated by the membership matrix of the neighboring pixels, and a new distance is determined by the spatial distance and the Euclidean distance. This new distance feature and the improved algorithm based on fuzzy C-means clustering are used in image segmentation. The experimental results show that the proposed algorithm is effective in getting the target image, more robust to the noises and faster than the conventional fuzzy C-means （FCM） algorithm.
出处
《西南大学学报（自然科学版）》
CAS
CSCD
北大核心
2009年第3期169-172,共4页
Journal of Southwest University(Natural Science Edition)
基金
西南师范大学发展基金资助项目(SWNUF2004006)
重庆市自然科学基金资助项目(2007BB2331)
关键词
空间距离
欧氏距离
模糊C-均值聚类
图像分割
spatial distance
Euclidean distance
fuzzy C-means clustering
image segmentation
