Open AccessSpatially Constrained Fuzzy c-Means Clustering Algorithm for Image Segmentation
Author(s) -
Xiaohe Li,
Zhan Qu,
Xiu Yang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1237/3/032024
Subject(s) - cluster analysis , pattern recognition (psychology) , artificial intelligence , fuzzy clustering , image segmentation , fuzzy logic , pixel , robustness (evolution) , flame clustering , segmentation based object categorization , canopy clustering algorithm , region growing , computer science , correlation clustering , cure data clustering algorithm , segmentation , mathematics , algorithm , scale space segmentation , biochemistry , chemistry , gene
The fuzzy c-means (FCM) clustering is an unsupervised clustering method, which has been widely used in image segmentation. In this paper, a spatially constrained fuzzy c-means clustering algorithm for image segmentation is proposed to overcome the sensitivity of the FCM clustering algorithm to noises and other imaging artifacts. Firstly, the local prior probabilities of pixel classification are defined according to the fuzzy membership function values of neighbouring pixels, and then those local prior probabilities are incorporated into the objective function of the standard FCM. Thus, the local spatial information embedded in the image is incorporated into the FCM algorithm. Experimental results on the synthetic and real images are given to demonstrate the robustness and validity of the proposed algorithm.