[1]马冬梅,武永娟,火元莲,等.一种改进的非局部极值FCM图像分割算法[J].计算机技术与发展,2018,28(09):20-24.[doi:10.3969/j.issn.1673-629X.2018.09.005]
　MA Dong-mei,WU Yong-juan,HUO Yuan-lian,et al.An Improved Non-local Extreme FCM Image Segmentation Algorithm[J].,2018,28(09):20-24.[doi:10.3969/j.issn.1673-629X.2018.09.005]
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一种改进的非局部极值FCM图像分割算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年09期
页码:
20-24
栏目:
智能、算法、系统工程
出版日期:
2018-09-10

文章信息/Info

Title:
An Improved Non-local Extreme FCM Image Segmentation Algorithm
文章编号:
1673-629X(2018)09-0020-05
作者:
马冬梅;  武永娟;  火元莲;  邹鑫
西北师范大学 物理与电子工程学院,甘肃 兰州,730070
Author(s):
MA Dong-mei; WU Yong-juan; HUO Yuan-lian; ZOU Xin
School of Physics and Electronic Engineering,Northwest Normal University,Lanzhou 730070,China
关键词:
模糊C均值聚类;  非局部滤波;  图像分割;  直方图斜率;  空间信息
Keywords:
fuzzy C-means clustering; non-local filter; image segmentation; histogram slope; spatial information
分类号:
TP301.6
DOI:
10.3969/j.issn.1673-629X.2018.09.005
文献标志码:
A
摘要:
传统模糊C均值聚类算法在图像分割中对初始值敏感,并且需要手动输入聚类数和初始聚类中心,手动输入错误的初始值会导致图像分割结果差;图像中的每个像素点都是相互独立的,未利用其空间信息,导致算法对噪声敏感,分割出来的区域不连续,使得分割结果差.针对上述两个问题,提出了一种改进的非局部极值模糊C均值聚类算法.首先通过计算直方图中每个点的斜率,根据其规则来确定聚类中心和聚类数,解决了对初始值敏感、易陷入局部极值的问题;然后引入非局部滤波计算加权图像,结合了灰度信息和空间信息,抑制了每个像素在非局部空间信息图像中的噪声,提高了分割精度;最后根据最大隶属度原则,把图像像素点归类,完成分割.在医学图像上进行了实验验证,计算JS指标来定量分析分割精度.结果表明,该算法既能有效去除噪声,也能很好地保留图像细节,增强了分割的鲁棒性,提高了分割精度.
Abstract:
The traditional fuzzy C-means clustering algorithm is sensitive to the initial value in image segmentation,and it needs to manu- ally input the number of clusters and the initial cluster center. Manual input of the wrong initial value will cause a poor image segmenta- tion results. Each pixel in the image is independent of each other and does not utilize their spatial information,which results in the algo- rithm being sensitive to noise and poor segmentation due to not continuous segmented regions. In view of above problems,we propose an improved non-local extreme fuzzy C-means clustering algorithm. It first finds the clustering center and the number of clusters by calcu- lating the slope of each point of the histogram,according to its rules to determine the cluster center and cluster number for the problem that the initial value is sensitive and easy to fall into the local optimal solution. Secondly,the non-local filter is introduced to compute the weighted image,and the gray information and spatial information are combined to suppress the noise of each pixel in the non-local spatial information image and improve the segmentation accuracy. Finally,according to the principle of the maximum degree of membership,the image pixels are classified to complete the segmentation. Through the experimental verification of medical images,we calculate JS index to quantitatively analyze the segmentation accuracy. The experiment shows that the algorithm can effectively remove the noise and keep the details of the image well,enhance the robustness of the segmentation and improve the segmentation precision.

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更新日期/Last Update: 2018-09-10