DocumentCode :
2351851
Title :
Effective Fuzzy C-mean Clustering Technique for Segmentation of T1-T2 Brain MRI
Author :
Kannan, S.R. ; Pandiyarajan, R.
Author_Institution :
Nat. Cheng Kung Univ., Tainan, Taiwan
fYear :
2009
fDate :
27-28 Oct. 2009
Firstpage :
537
Lastpage :
539
Abstract :
This paper presents a modified FCM algorithm for segmentation of MRI. The proposed method has introduced by modifying the objective function of the standard FCM and it has the advantage that it can be applied at an early stage in an automated data analysis. The proposed method can deal with the intensity in-homogeneities and image noise effectively. have compared our results with other reported methods. The results using real MRI data show that our method provides better results compared to standard FCM-based algorithms and other modified FCM-based techniques.
Keywords :
biomedical MRI; brain; data analysis; fuzzy set theory; image segmentation; medical image processing; noise; pattern clustering; T1-T2 brain MRI segmentation; automated data analysis; fuzzy c-mean clustering technique; image noise; Biomedical imaging; Clustering algorithms; Communications technology; Data analysis; Gaussian noise; Image analysis; Image segmentation; Magnetic resonance imaging; Neoplasms; Radio frequency; Bias field; Data analysis; FCM; MRI; Segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advances in Recent Technologies in Communication and Computing, 2009. ARTCom '09. International Conference on
Conference_Location :
Kottayam, Kerala
Print_ISBN :
978-1-4244-5104-3
Electronic_ISBN :
978-0-7695-3845-7
Type :
conf
DOI :
10.1109/ARTCom.2009.63
Filename :
5329183
Link To Document :
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