DocumentCode :
3317129
Title :
Application of Improved Fuzzy c-Means Clustering in Cell Image Segmentation
Author :
Ren, Peng ; Hu Shangliang ; Zhu Huiping ; Cao, Ying
Author_Institution :
Coll. of Life Sci. & Eng., Southwest Univ. of Sci. & Technol., Mianyang, China
fYear :
2011
fDate :
10-12 May 2011
Firstpage :
1
Lastpage :
4
Abstract :
Cell image show entirely different characteristic due to the biodiversity, complexity, culture conditions and acquisition methods. Segment image is the key step of cell image processing. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document. The fuzzy c-means (FCM) clustering algorithm is one of most widespread methods which has applied in image analyzing, pattern recognition and medical diagnosis. To overcome the limitation of FCM algorithm, several improved FCM algorithm have been compared by applicated in cell image segmentation. The simulation results and the comparison between FCM and improved algorithm indicate that AFCM as shown by experiment indicated the better effect.
Keywords :
cellular biophysics; fuzzy set theory; image segmentation; medical image processing; patient diagnosis; pattern clustering; AFCM; FCM clustering algorithm; cell image processing; cell image segmentation; fuzzy c-means clustering; image analysis; medical diagnosis; pattern recognition; style sheet; Clustering algorithms; Image segmentation; Partitioning algorithms; Pixel; Presses;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedical Engineering, (iCBBE) 2011 5th International Conference on
Conference_Location :
Wuhan
ISSN :
2151-7614
Print_ISBN :
978-1-4244-5088-6
Type :
conf
DOI :
10.1109/icbbe.2011.5779980
Filename :
5779980
Link To Document :
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