.png){kind=icon}
Return
An automatic fuzzy c-means algorithm for image segmentation
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-009-0442-0.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm must be estimated by expertise users to determine the cluster number. So, we propose an automatic fuzzy clustering algorithm (AFCM) for automatically grouping the pixels of an image into different homogeneous regions when the number of clusters is not known beforehand. In order to get better segmentation quality, this paper presents an algorithm based on AFCM algorithm, called automatic modified fuzzy c-means cluster segmentation algorithm (AMFCM). AMFCM algorithm incorporates spatial information into the membership function for clustering. The spatial function is the weighted summation of the membership function in the neighborhood of each pixel under consideration. Experimental results show that AMFCM algorithm not only can spontaneously estimate the appropriate number of clusters but also can get better segmentation quality.
Keywords:
Image segmentation
Fuzzy c-means
Fuzzy clustering
K-means
Spatial information
Journal
IF:
2.5
Papers:
1.0W
Citations:
2.1W
Organization
No organization information available
Cited Papers
ShareSaveShareSaveShareSaveOn cluster validity index for estimation of the optimal number of fuzzy clusters.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSaveSolution structure of human thioredoxin in a mixed disulfide intermediate complex with its target peptide from the transcription factor NFκBStructure
OAAI
StructureIF0
OAAIShareSaveShareSaveShareSave
View more