NDLI: Adaptive image segmentation method based on the fuzzy c-means with spatial information

Content Provider	IET Digital Library
Author	Zheng, Jia Zhang, Dinghua Huang, Kuidong Sun, Yuanxi
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.
Starting Page	785
Ending Page	792
Page Count	8
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 5, May (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0760
Journal	IET Image Processing
Publisher Date	2017-12-19
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Image Segmentation Method Area Overlap Measure Combinatorial Mathematics Computer Vision And Image Processing Technique Fuzzy C-means with Spatial Information Fuzzy Set Theory Global FCM_S Image Classification Image Recognition Image Segmentation Local FCM_S Local Window Size Maximal Grey Level Object Pixel Pattern Clustering Pixel Classification Synthetic Image Weizmann Database
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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