Developing Modified Fuzzy C-Means Clustering Algorithm for Image Segmentation

Effective algorithm for segmenting image is important for pattern recognition, images analysis and computer vision. Fuzzy c-means (FCM) is the mostly used methodology in image clustering. However, the results of the standard and the modified version FCM are not always satisfactory. This paper introd...

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Bibliographic Details
Main Authors: Aljebory, Karim M, Mohammed, Thabit Sultan
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
fuzzy c-means
image clustering
image processing
Image segmentation
images segmentation
Kernel
Linear programming
Microsoft Windows
Noise measurement
Thresholding (Imaging)
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Summary:Effective algorithm for segmenting image is important for pattern recognition, images analysis and computer vision. Fuzzy c-means (FCM) is the mostly used methodology in image clustering. However, the results of the standard and the modified version FCM are not always satisfactory. This paper introduces a modification on spatial FCM considering the weighted fuzzy effect of neighboring pixels on the center of the cluster. So, the objective function in FCM algorithm is modified to minimize the intensity inhomogeneities by implicating the spatial information and the modified membership weighting. The advantages of the new FCM algorithm are: (a) produces homogeneous regions, (b) handles noisy spots, and (c) relatively less sensitive to noise. Experimental results on real images show that the algorithm is effective, efficient, and is relatively independent of the type of noise. Especially, it can process non-noisy and noisy images without knowing the type of the noise.
ISSN:2474-0446
DOI:10.1109/SSD.2018.8570440