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