An Improved Suppressed FCM Algorithm for Image Segmentation

2013 ◽  
Vol 712-715 ◽  
pp. 2349-2353
Author(s):  
Hong Lan ◽  
Shao Bin Jin
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Experimental Results ◽  
Two Dimensional ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Neighborhood Information ◽  
Noisy Images ◽  
Fuzzy C Means Clustering ◽  
Improved Algorithm

Fuzzy C-Means clustering(FCM) algorithm plays an important role in image segmentation, but it is sensitive to noise because of not taking into account the spatial information. Addressing this problem, this paper presents an improved suppressed FCM algorithm based on the pixels and the spatial neighborhood information of the image. The algorithm combines the two-dimentional histogram and suppressed FCM algorithm together. First, construct a two-dimentional histogram instead of one-dimentional histogram, which can better distinguish the distribution of the object and background for noisy images. Then determine the initial clustering based on two-dimensional histogram. Last, provide a new way to determine the suppressed factor and use the improved FCM algorithm to realize the image segmentation. Experimental results show that the improved algorithm is effective to improve the clustering speed, and can achieve better segmentation results.

AN UNSUPERVISED KERNEL BASED FUZZY C-MEANS CLUSTERING ALGORITHM WITH KERNEL NORMALISATION

2004 ◽  
Vol 04 (04) ◽  
pp. 355-373 ◽  
Author(s):  
SHANG-MING ZHOU ◽  
JOHN Q. GAN
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Experimental Results ◽  
New Method ◽  
Fcm Algorithm ◽  
Input Space ◽  
Fuzzy C Means ◽  
Benchmark Datasets ◽  
Fuzzy C Means Clustering ◽  
Mercer Kernel

In this paper, a novel procedure for normalising Mercer kernel is suggested firstly. Then, the normalised Mercer kernel techniques are applied to the fuzzy c-means (FCM) algorithm, which leads to a normalised kernel based FCM (NKFCM) clustering algorithm. In the NKFCM algorithm, implicit assumptions about the shapes of clusters in the FCM algorithm is removed so that the new algorithm possesses strong adaptability to cluster structures within data samples. Moreover, a new method for calculating the prototypes of clusters in input space is also proposed, which is essential for data clustering applications. Experimental results on several benchmark datasets have demonstrated the promising performance of the NKFCM algorithm in different scenarios.

scholarly journals Fast FCM with Spatial Neighborhood Information for Brain Mr Image Segmentation

2013 ◽  
Vol 3 (1) ◽  
pp. 15-25 ◽  
Author(s):  
Abbas Biniaz ◽  
Ataollah Abbasi
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Medical Image Segmentation ◽  
Convergence Time ◽  
Noise Interference ◽  
Neighborhood Information ◽  
Elapsed Time ◽  
Fuzzy C Means Clustering ◽  
Conventional Validity ◽  
Mr Image Segmentation

Abstract Among different segmentation approaches Fuzzy c-Means clustering (FCM) is a welldeveloped algorithm for medical image segmentation. In emergency medical applications quick convergence of FCM is necessary. On the other hand spatial information is seldom exploited in standard FCM; therefore nuisance factors can simply affect it and cause misclassification. This paper aims to introduce a Fast FCM (FFCM) technique by incorporation of spatial neighborhood information which is exploited by a linear function on fuzzy membership. Applying proposed spatial Fast FCM (sFFCM), elapsed time is decreased and neighborhood spatial information is exploited in FFCM. Moreover, iteration numbers by proposed FFCM/sFFCM techniques are decreased efficiently. The FCM/FFCM techniques are examined on both simulated and real MR images. Furthermore, to considerably decrease of convergence time and iterations number, cluster centroids are initialized by an algorithm. Accuracy of the new approach is same as standard FCM. The quantitative assessments of presented FCM/FFCM techniques are evaluated by conventional validity functions. Experimental results demonstrate that sFFCM techniques efficiently handle noise interference and significantly decrease elapsed time.

FUZZY C-MEANS CLUSTERING ALGORITHM WITH TWO LAYERS AND ITS APPLICATION TO IMAGE SEGMENTATION BASED ON TWO-DIMENSIONAL HISTOGRAM

1994 ◽  
Vol 02 (03) ◽  
pp. 343-350 ◽  
Author(s):  
WEIXIN XIE ◽  
JIANZHUANG LIU
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Two Dimensional ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Hard Clustering

This paper presents a fast fuzzy c-means (FCM) clustering algorithm with two layers, which is a mergence of hard clustering and fuzzy clustering. The result of hard clustering is used to initialize the c cluster centers in fuzzy clustering, and then the number of iteration steps is reduced. The application of the proposed algorithm to image segmentation based on the two dimensional histogram is provided to show its computational efficience.

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