earticle

영어

The segmentation results of the traditional FCM based image segmentation algorithms are only determined by the distribution of pixel intensity in the feature space, and they does not take the spatial distribution of pixels into consideration, which make the segmentation results discrete in the spatial distribution. To solve this problem, a global spatial similarity metric and a global intensity similarity metric are proposed, and introduced to a new distance metric which is used to calculate the difference between pixels and cluster centers. In addition, a maximal similarity based class merging mechanism is employed to achieve more accurate image segmentation. The experiments demonstrate that, comparison with the FCM and KFCM based image segmentation algorithms, the proposed method produces more accurate and applicable segmentation results.