DocumentCode :
643761
Title :
An integration strategy based on fuzzy clustering and level set method for cell image segmentation
Author :
Gharipour, Amin ; Liew, Alan Wee-Chung
Author_Institution :
Sch. of Inf. & Com munication Technol., Griffith Univ., Gold Coast, QLD, Australia
fYear :
2013
fDate :
5-8 Aug. 2013
Firstpage :
1
Lastpage :
5
Abstract :
In this study a new image segmentation framework which combines the Fuzzy c means clustering and the level set method is presented. Using this framework, the well-known Chan and Vese´s level set technique and classical Bayes classifier are employed to obtain a prior membership value for each pixel based on region information. Next, a novel clustering model based on fuzzy c-mean clustering assisted by prior membership values is used to obtain the final segmentation. Experiments performed on high-throughput fluorescence microscopy colon cancer cell images, which are commonly utilized for the study of many normal and neoplastic procedures, indicate a significant improvement in accuracy when compared to several existing techniques.
Keywords :
Bayes methods; cancer; fuzzy set theory; image segmentation; medical image processing; Chan-Vese level set technique; cell image segmentation; classical Bayes classifier; fluorescence microscopy colon cancer cell images; fuzzy c-mean clustering; fuzzy clustering method; integration strategy; level set method; neoplastic procedure; prior membership value; region information; Cancer; Clustering algorithms; Colon; Educational institutions; Image segmentation; Level set; Signal processing algorithms; Cell image segmentation; Chan-Vese method; classical Bayes classifier; fuzzy c-means; level set;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing, Communication and Computing (ICSPCC), 2013 IEEE International Conference on
Conference_Location :
KunMing
Type :
conf
DOI :
10.1109/ICSPCC.2013.6664081
Filename :
6664081
Link To Document :
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