Robust fuzzy segmentation of magnetic resonance images

Author

Pham, Dzung L.

Author_Institution

Lab. of Personality & Cognition, NIA/NIH, Baltimore, MD, USA

fYear

2001

fDate

2001

Firstpage

127

Lastpage

131

Abstract

A new approach for the robust segmentation of magnetic resonance images is described. The approach is derived from a generalization of the objective function used in D.L. Pham and J.L. Prince´s (1999) adaptive fuzzy c-means algorithm (AFCM). Within the objective function, an additional constraint is placed on the membership functions that forces them to be spatially smooth. Minimization of this objective function results in an unsupervised fuzzy segmentation algorithm that is robust to intensity inhomogeneity artifacts as well as noise and other artifacts. The efficacy of the algorithm is demonstrated on simulated magnetic resonance images

Keywords

adaptive signal processing; fuzzy set theory; image segmentation; magnetic resonance imaging; adaptive fuzzy c-means algorithm; intensity inhomogeneity artifacts; magnetic resonance images; noise; objective function generalization; objective function minimization; robust fuzzy image segmentation; spatially smooth membership functions; unsupervised fuzzy segmentation algorithm; Clustering algorithms; Cognition; Filters; Gerontology; Image segmentation; Laboratories; Magnetic noise; Magnetic resonance; Noise robustness; Smoothing methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer-Based Medical Systems, 2001. CBMS 2001. Proceedings. 14th IEEE Symposium on

Conference_Location

Bethesda, MD

ISSN

1063-7125

Print_ISBN

0-7695-1004-3

Type

conf

DOI

10.1109/CBMS.2001.941709

Filename

941709