scholarly journals Robust Image Segmentation Using Fuzzy C-Means Clustering With Spatial Information Based on Total Generalized Variation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 95681-95697
Author(s):  
Xiaolei Zhang ◽  
Weijun Pan ◽  
Zhengyuan Wu ◽  
Jiayang Chen ◽  
Yifei Mao ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Total Generalized Variation ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Generalized Variation

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

Adaptive Kernel-Based Fuzzy C-Means Clustering with Spatial Constraints for Image Segmentation

2018 ◽  
Vol 33 (01) ◽  
pp. 1954003 ◽  
Author(s):  
Guang Hu ◽  
Zhenbin Du
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Spatial Constraints ◽  
Fuzzy C Means ◽  
Central Pixel ◽  
Fuzzy C Means Clustering ◽  
Adaptive Kernel ◽  
Fcm Clustering ◽  
Image Pixels

In order to resolve the disadvantages of fuzzy C-means (FCM) clustering algorithm for image segmentation, an improved Kernel-based fuzzy C-means (KFCM) clustering algorithm is proposed. First, the reason why the kernel function is introduced is researched on the basis of the classical KFCM clustering. Then, using spatial neighborhood constraint property of image pixels, an adaptive weighted coefficient is introduced into KFCM to control the influence of the neighborhood pixels to the central pixel automatically. At last, a judging rule for partition fuzzy clustering numbers is proposed that can decide the best clustering partition numbers and provide an optimization foundation for clustering algorithm. An adaptive kernel-based fuzzy C-means clustering with spatial constraints (AKFCMS) model for image segmentation approach is proposed in order to improve the efficiency of image segmentation. Various experiment results show that the proposed approach can get the spatial information features of an image accurately and is robust to realize image segmentation.

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.

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