抑制式非局部空间直觉模糊C-均值图像分割算法

兰蓉; 林洋

doi:10.11999/JEIT180651

抑制式非局部空间直觉模糊C-均值图像分割算法

doi: 10.11999/JEIT180651

兰蓉,
林洋^,

1.
西安邮电大学通信与信息工程学院西安 710121
2.
电子信息现场勘验应用技术公安部重点实验室西安 710121
3.
陕西省无线通信与信息处理技术国际合作研究中心西安 710121

基金项目: 国家自然科学基金(61571361, 61671377)，陕西省教育厅科学研究计划(16JK1709)，西安邮电大学西邮新星团队计划(xyt2016-01)

详细信息

作者简介:
兰蓉：女，1977年生，博士，副教授，研究方向为模式识别和图像处理

林洋：男，1993年生，硕士生，研究方向为图像处理

通讯作者:
林洋　784046805@qq.com

中图分类号: TP391
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- 被引次数: 0
出版历程
- 收稿日期: 2018-07-03
- 修回日期: 2018-12-29
- 网络出版日期: 2019-01-07
- 刊出日期: 2019-06-01

Suppressed Non-local Spatial Intuitionistic Fuzzy C-means Image Segmentation Algorithm

Rong LAN,
Yang LIN^,

1.
School of Telecommunications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
Key Laboratory of Electronic Information Application Technology for Scene Investigation, Ministry of Public Security, Xi’an 710121, China
3.
International Joint Research Center for Wireless Communication and Information Processing, Shannxi Province Xi’an 710121, China

Funds: The National Natural Science Foundation of China (61571361, 61671377), Shaanxi Provincial Department of Education Scientific Research Plan (16JK1709), New Star Team of Xi’an University of Posts and Telecommunications (xyt2016-01)

摘要

摘要: 针对传统的模糊C-均值(FCM)算法没有考虑图像像素的空间邻域信息，对噪声敏感，算法收敛较慢等问题，该文提出一种抑制式非局部空间直觉模糊C-均值图像分割算法。首先，通过计算像素的非局部空间信息提高抗噪能力，克服传统的FCM算法只考虑图像单个像素的灰度特征信息的缺陷，提高分割精度。其次，根据直觉模糊集理论，通过“投票模型”自适应生成犹豫度作为抑制因子修正隶属度，提高算法的运行效率。实验结果表明，该算法对噪声鲁棒性较强并且有较好的分割性能。
- 图像分割 /
- 模糊C-均值 /
- 直觉模糊集 /
- 非局部空间信息 /
- 犹豫度
Abstract: In order to deal with these issues of the traditional Fuzzy C-Means (FCM) algorithm, such as without consideration of the spatial neighborhood information of pixels, noise sensitivity and low convergence speed, a suppressed non-local spatial intuitionistic fuzzy c-means image segmentation algorithm is proposed. Firstly, in order to improve the accuracy of segmentation image, the non-local spatial information of pixel is used to improve anti-noise ability, and to overcome the shortcomings of the traditional FCM algorithm, which only considers the gray characteristic information of single pixel. Secondly, by using the ‘voting model’ based on the intuitionistic fuzzy set theory, the hesitation degrees are adaptively generated as inhibitory factors to modify the membership degrees, and then the operating efficiency is increased. Experimental results show that the new algorithm is robust to noise and has better segmentation performance.
- Image segmentation /
- Fuzzy C-Means (FCM) /
- Intuitionistic fuzzy set /
- Non-local spatial information /
- Hesitation degree

HTML全文

图 1 4种算法对synthetic的分割结果

下载: 全尺寸图片幻灯片

图 2 4种算法对#15088的分割结果

下载: 全尺寸图片幻灯片

图 3 4种算法对#42049的分割结果

下载: 全尺寸图片幻灯片

图 4 4种算法对#24063的分割结果

下载: 全尺寸图片幻灯片

图 5 4种算法对#118035的分割结果

下载: 全尺寸图片幻灯片

图 6 #15088, #24063随噪声水平变化的运行时间对比

下载: 全尺寸图片幻灯片

表 1 4种算法对人工图像的分割结果指标

分割算法	V_pc	V_pe
FCM	0.8449	0.3065
FCM-IFS	0.8431	0.3109
FCM_NLS	0.9223	0.1777
本文算法	0.9229	0.1765

下载: 导出CSV

表 2 4种算法的分割结果指标

测试图像	分割算法	V_pc	V_pe	PSNR	DC
#15088	FCM	0.8484	0.2535	3.2419	0.4046
	FCM-IFS	0.8581	0.2397	3.2490	0.4316
	FCM_NLS	0.9229	0.1502	8.0774	0.6364
	本文算法	0.9245	0.1477	8.0832	0.6400
#42049	FCM	0.8992	0.1790	2.2577	0.8517
	FCM-IFS	0.9010	0.1765	2.2609	0.8541
	FCM_NLS	0.9267	0.1371	10.5479	0.8947
	本文算法	0.9270	0.1369	10.5636	0.8937
#24063	FCM	0.7928	0.3778	4.0303	0.9239
	FCM-IFS	0.7956	0.3740	3.9493	0.9259
	FCM_NLS	0.8331	0.3262	7.1709	0.9707
	本文算法	0.8371	0.3208	7.9568	0.9741
#118035	FCM	0.8382	0.2927	3.0429	0.9438
	FCM-IFS	0.8377	0.2948	3.0053	0.9433
	FCM_NLS	0.8410	0.2936	11.0120	0.9519
	本文算法	0.8594	0.2693	11.7764	0.9487

下载: 导出CSV

表 3 强度0.02高斯噪声下两种算法运行时间对比

图像编号	FCM_NLS算法		本文算法
图像编号	运行时间(s)	迭代次数	运行时间(s)	迭代次数
#15088	439.63	24	423.17	13
#42049	421.56	23	420.66	14
#24063	436.69	37	418.97	20
#118035	404.66	64	405.08	19
#3096	427.91	94	413.07	36
#135069	433.81	38	426.97	26
#8068	442.48	19	408.73	14
#113044	436.53	28	405.51	18
#27	420.28	37	409.27	23
#101027	427.13	27	408.06	25

下载: 导出CSV

表 4 强度0.02高斯噪声下两种算法平均运行时间对比

算法	平均运行时间(s)	平均迭代次数
FCM_NLS	427.54	41
本算法	414.77	24

下载: 导出CSV

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