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 t...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.展开更多
Fuzzy c-means （FCM） algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the im...Fuzzy c-means （FCM） algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the image. An improved FCM algorithm is proposed to improve the antinoise performance of FCM algorithm. The new algorithm is formulated by incorporating the spatial neighborhood information into the membership function for clustering. The distribution statistics of the neighborhood pixels and the prior probability are used to form a new membership func- tion. It is not only effective to remove the noise spots but also can reduce the misclassified pixels. Experimental results indicate that the proposed algorithm is more accurate and robust to noise than the standard FCM algorithm.展开更多
This paper presents a novel approach that can quickly and effectively partition images based on fully exploiting the spatially coherent property. We propose an algorithm named iterative loopy belief propagation（iLBP...This paper presents a novel approach that can quickly and effectively partition images based on fully exploiting the spatially coherent property. We propose an algorithm named iterative loopy belief propagation（iLBP） to integrate the homogenous regions and prove its convergence. The image is first segmented by mean shift（MS） algorithm to form over-segmented regions that preserve the desirable edges and spatially coherent parts. The segmented regions are then represented by region adjacent graph（RAG） . Motivated by k-means algorithm,the iLBP algorithm is applied to perform the minimization of the cost function to integrate the over-segmented parts to get the final segmentation result. The image clustering based on the segmented regions instead of the image pixels reduces the number of basic image entities and enhances the image segmentation quality. Comparing the segmentation result with some existing algorithms,the proposed algorithm shows a better performance based on the evaluation criteria of entropy especially on complex scene images.展开更多
文摘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.
基金supported by the National Natural Science Foundation of China(6087403160740430664)
文摘Fuzzy c-means （FCM） algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the image. An improved FCM algorithm is proposed to improve the antinoise performance of FCM algorithm. The new algorithm is formulated by incorporating the spatial neighborhood information into the membership function for clustering. The distribution statistics of the neighborhood pixels and the prior probability are used to form a new membership func- tion. It is not only effective to remove the noise spots but also can reduce the misclassified pixels. Experimental results indicate that the proposed algorithm is more accurate and robust to noise than the standard FCM algorithm.
基金Supported by the National Natural Science Foundation of China (60703107, 60703108)the National High Technology Research and Development Program of China (863 Program) (2006AA01Z107)+1 种基金the National Basic Research Program of China (973 Program) (2006CB705700)the Program for Changjiang Scholars and Innovative Research Team in University (IRT0645)
文摘This paper presents a novel approach that can quickly and effectively partition images based on fully exploiting the spatially coherent property. We propose an algorithm named iterative loopy belief propagation（iLBP） to integrate the homogenous regions and prove its convergence. The image is first segmented by mean shift（MS） algorithm to form over-segmented regions that preserve the desirable edges and spatially coherent parts. The segmented regions are then represented by region adjacent graph（RAG） . Motivated by k-means algorithm,the iLBP algorithm is applied to perform the minimization of the cost function to integrate the over-segmented parts to get the final segmentation result. The image clustering based on the segmented regions instead of the image pixels reduces the number of basic image entities and enhances the image segmentation quality. Comparing the segmentation result with some existing algorithms,the proposed algorithm shows a better performance based on the evaluation criteria of entropy especially on complex scene images.
