Adaptive image segmentation method based on the fuzzy c‐means with spatial information. Issue 5 (1st May 2018)

• Record Type: Journal Article
• Authors: Zheng, Jia; Zhang, Dinghua; Huang, Kuidong; Sun, Yuanxi
• Is Part Of: IET image processing. Volume 12:Issue 5(2018)
• Page Start: 785
• Page End: 792
• Publication Date: 2018-05-01
• Subjects: image segmentation -- image classification -- fuzzy set theory -- pattern clustering; adaptive image segmentation method -- fuzzy c‐means with spatial information -- global FCM_S -- local FCM_S -- pixel classification -- local window size -- maximal grey level -- object pixels -- Weizmann database -- synthetic images -- area overlap measure; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0760
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : This study presents an adaptive image segmentation method based on the fuzzy c‐means with spatial information (FCM_S). First, the advantages of the local FCM_S over the global FCM_S and its segmentation characteristics are introduced, on the basis of which, the accumulated local FCM_S is proposed to classify each pixel in an image by using information from different local windows that contain them. The local window size is calculated automatically, and the classification results of all pixels are stored together in the accumulated result. The grey levels of the background and the object pixels in the accumulated image, which is converted from the accumulated result, are distributed around 0 and the maximal grey level. Thus, it can be segmented by the grey level where the change rate of the count of object pixels reaches the minimum. Experiments are performed on 16 images from the Weizmann's database, as well as two real‐world and four synthetic images. The results validated that the proposed method can segment images with inhomogeneity well and can gain better area overlap measure when compared with some new segmentation methods. Moreover, the proposed method is parameterless.