|
|
|
篇目详细内容 |
|
【篇名】 |
Fuzzy c-means clustering with non local spatial information for noisy image segmentation |
|
【刊名】 |
Frontiers of Computer Science in China |
|
【刊名缩写】 |
Front. Comput. Sci. China |
|
【ISSN】 |
1673-7350 |
|
【EISSN】 |
1673-7466 |
|
【DOI】 |
10.1007/s11704-010-0393-8 |
|
【出版社】 |
Higher Education Press and Springer-Verlag Berlin
Heidelberg |
|
【出版年】 |
2011 |
|
【卷期】 |
5
卷1期 |
|
【页码】 |
45-56
页,共
12
页 |
|
【作者】 |
Feng ZHAO;
Licheng JIAO;
Hanqiang LIU;
|
|
【关键词】 |
image segmentation; fuzzy clustering algorithm; non local spatial information; magnetic resonance (MR) image |
【摘要】 |
|
As an effective image segmentation method, the standard fuzzy c-means (FCM) clustering algorithm is very sensitive to noise in images. Several modified FCM algorithms, using local spatial information, can overcome this problem to some degree. However, when the noise level in the image is high, these algorithms still cannot obtain satisfactory segmentation performance. In this paper, we introduce a non local spatial constraint term into the objective function of FCM and propose a fuzzy c-means clustering algorithm with non local spatial information (FCM_NLS). FCM_NLS can deal more effectively with the image noise and preserve geometrical edges in the image. Performance evaluation experiments on synthetic and real images, especially magnetic resonance (MR) images, show that FCM_NLS is more robust than both the standard FCM and the modified FCM algorithms using local spatial information for noisy image segmentation. |
|
