DocumentCode :
2835880
Title :
Segmentation and quantification of dental plaque using modified kernelized fuzzy C-means clustering algorithm
Author :
Kang, Jiayin ; Ji, Zhicheng ; Gong, Chenglong
fYear :
2010
fDate :
26-28 May 2010
Firstpage :
788
Lastpage :
791
Abstract :
This paper presented an approach for automatically quantifying the dental plaque based on modified kernelized fuzzy c-means. The proposed approach was applied to a clinical database consisting of 30 objects. The experimental results show that the proposed method provids accurate quantitative measurement of dental plaque compared with that of traditional manual measurement indices of the dental plaque.
Keywords :
database management systems; dentistry; fuzzy set theory; image segmentation; medical image processing; pattern clustering; clinical database; dental plaque quantification-segmentation; manual measurement indices; modified kernelized fuzzy C-means clustering algorithm; Clustering algorithms; Control engineering; Databases; Dentistry; Fuzzy control; Image analysis; Image segmentation; Iterative algorithms; Partitioning algorithms; Pixel; Dental Plaque; Kernel-Induced Distance; Kernelized Fuzzy C-Means; Segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2010 Chinese
Conference_Location :
Xuzhou
Print_ISBN :
978-1-4244-5181-4
Electronic_ISBN :
978-1-4244-5182-1
Type :
conf
DOI :
10.1109/CCDC.2010.5498119
Filename :
5498119
Link To Document :
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